text:
with free-form deformation motion correction in rapidly enhancing lesions can introduce motion. However, in Dixon-based fat-water separated acquisitions, the nonenhancing fat signal can directly be used to estimate deformable motion, which can be later used to deform the water images. Qualitative comparison of the fat-based registration method to a water-based registration method, and to the unregistered images, was performed by two experienced readers. Quantitative analysis of the registration was evaluated by estimating the mean-squared signal difference on the fat images. Results: Using a scale of 0 (no motion) to 2 ({\textgreater} 4 voxels of motion), the average image quality score of the fat-based registered images was 0.5 ± 0.6, water-based registration was 0.8 ± 0.8, and the unregistered dataset was 1.6 ± 0.6. The mean-squared-signal-difference metric on the fat images was significantly lower for fat-based registered images compared with both water-based registered and unregistered images. Conclusions: Fat-based registration of breast dynamic contrast-enhanced images is a promising technique for performing deformable motion correction of breast without introducing new motion. Magn Reson Med 79:2408–2414, 2018. {\textcopyright} 2017 International Society for Magnetic Resonance in Medicine.},
author = {Srinivasan, Subashini and Hargreaves, Brian A. and Daniel, Bruce L.},
doi = {10.1002/mrm.26851},
issn = {15222594},
journal = {Magnetic Resonance in Medicine},
keywords = {breast deformable motion,fat-based registration},
number = {4},
pages = {2408--2414},
title = {{Fat-based registration of breast dynamic contrast enhanced water images}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {79},
year = {2018}
}
@article{Stegmaier2014,
abstract = {Automated analysis of multi-dimensional microscopy images has become an integral part of modern research in life science. Most available algorithms that provide sufficient segmentation quality, however, are infeasible for a large amount of data due to their high complexity. In this contribution we present a fast parallelized segmentation method that is especially suited for the extraction of stained nuclei from microscopy images, e.g., of developing zebrafish embryos. The idea is to transform the input image based on gradient and normal directions in the proximity of detected seed points such that it can be handled by straightforward global thresholding like Otsu's method. We evaluate the quality of the obtained segmentation results on a set of real and simulated benchmark images in 2D and 3D and show the algorithm's superior performance compared to other state-of-the-art algorithms. We achieve an up to ten-fold decrease in processing times, allowing us to process large data sets while still providing reasonable segmentation results. {\textcopyright} 2014 Stegmaier et al.},
author = {Stegmaier, Johannes and Otte, Jens C. and Kobitski, Andrei and Bartschat, Andreas and Garcia, Ariel and Nienhaus, G. Ulrich and Str{\"{a}}hle, Uwe and Mikut, Ralf},
doi = {10.1371/journal.pone.0090036},
issn = {19326203},
journal = {PLoS ONE},
number = {2},
pages = {11},
title = {{Fast segmentation of stained nuclei in terabyte-scale, time resolved 3D microscopy image stacks}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {9},
year = {2014}
}
@inproceedings{Stevensona,
abstract = {Recent advances in medical research hypothesize that certain body fat, in addition to having a classical role of energy storage, may also have mechanical function. In particular, we analyzed the infrapatellar fat pad of Hoffa using 3D CT images of the knee at multiple angles to determine how the fat pad changes shape as the knee bends and whether the fat pad provides cushioning in the knee joint. The images were initially processed using a median filter then segmented using a region growing technique to isolate the fat pad from the rest of the knee. Next, rigid registration was performed to align the series of images to match the reference image. Finally, multi-resolution FEM registration was completed between the aligned images. The resulting displacements fields were used to determine the local volume change of the fat pad as the knee bends from extension to flexion through different angles. This multi-angle analysis provides a finer description of the intermediate deformations compared to earlier work, where only a pair of images (full extension and flexion) was analyzed.},
author = {Stevenson, Kevin and Schweitzer, Mark and Hamarneh, Ghassan},
booktitle = {Medical Imaging 2006: Physiology, Function, and Structure from Medical Images},
doi = {10.1117/12.654301},
isbn = {0819461865},
issn = {16057422},
pages = {614329},
title = {{Multi-angle deformation analysis of Hoffa's fat pad}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33745353456{\&}doi=10.1117{\%}2F12.654301{\&}partnerID=40{\&}md5=595f4e12f1f8758a3702a5ddb70cd256},
volume = {6143},
year = {2006}
}
@article{Stoyanova2016,
abstract = {Prostate cancer exhibits intra-tumoral heterogeneity that we hypothesize to be the leading confounding factor contributing to the underperformance of the current pre-treatment clinical-pathological and genomic assessment. These limitations impose an urgent need to develop better computational tools to identify men with low risk of prostate cancer versus others that may be at risk for developing metastatic cancer. The patient stratification will directly translate to patient treatments, wherein decisions regarding active surveillance or intensified therapy are made. Multiparametric MRI (mpMRI) provides the platform to investigate tumor heterogeneity by mapping the individual tumor habitats. We hypothesize that quantitative assessment (radiomics) of these habitats results in distinct combinations of descriptors that reveal regions with different physiologies and phenotypes. Radiogenomics, a discipline connecting tumor morphology described by radiomic and its genome described by the genomic data, has the potential to derive "radio phenotypes" that both correlate to and complement existing validated genomic risk stratification biomarkers. In this article we first describe the radiomic pipeline, tailored for analysis of prostate mpMRI, and in the process we introduce our particular implementations of radiomics modules. We also summarize the efforts in the radiomics field related to prostate cancer diagnosis and assessment of aggressiveness. Finally, we describe our results from radiogenomic analysis, based on mpMRI-Ultrasound (MRI-US) biopsies and discuss the potential of future applications of this technique. The mpMRI radiomics data indicate that the platform would significantly improve the biopsy targeting of prostate habitats through better recognition of indolent versus aggressive disease, thereby facilitating a more personalized approach to prostate cancer management. The expectation to non-invasively identify habitats with high probability of housing aggressive cancers would result in directed biopsies that are more informative and actionable. Conversely, providing evidence for lack of disease would reduce the incidence of non-informative biopsies. In radiotherapy of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated.},
author = {Stoyanova, Radka and Takhar, Mandeep and Tschudi, Yohann and Ford, John C. and Sol{\'{o}}rzano, Gabriel and Erho, Nicholas and Balagurunathan, Yoganand and Punnen, Sanoj and Davicioni, Elai and Gillies, Robert J. and Pollack, Alan},
doi = {10.21037/tcr.2016.06.20},
issn = {22196803},
journal = {Translational Cancer Research},
keywords = {Gene expression,MRI-targeted biopsies,Multiparametric MRI,Prostate cancer,Radiogenomics,Radiomics},
number = {4},
pages = {432--447},
pmid = {29188191},
title = {{Prostate cancer radiomics and the promise of radiogenomics}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {5},
year = {2016}
}
@incollection{Stredney2003,
abstract = {We report on efforts to provide high-level intuitive tools that exploit commodity-based computing to facilitate real-time and distributed interactions with volumetric data. These efforts include an open source volume-rendering library, a portable volume visualization application framework, and parallel volume-rendering exploiting commodity-based hardware. We present our design and implementations, as well as examples of some of the various groups currently utilizing these tools, and discuss the tradeoffs of our developments versus existing techniques.},
author = {Stredney, D. and Bryan, J. and Sessanna, D. and Kerwin, T.},
booktitle = {Studies in Health Technology and Informatics},
doi = {10.3233/978-1-60750-938-7-329},
isbn = {1586033204},
issn = {18798365},
pages = {329--335},
title = {{Facilitating real-time volume interaction}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-6344254574{\&}doi=10.3233{\%}2F978-1-60750-938-7-329{\&}partnerID=40{\&}md5=a00d000d033135abebb47756881fd5ff},
volume = {94},
year = {2003}
}
@article{Streicher2012,
abstract = {Object The temperature dependence of the proton resonance frequency (PRF) is often used in MR thermometry. However, thismethod is prone to even very small changes in local magnetic field strength. Here, we report on the effects of susceptibility changes of surrounding air on the magnetic field inside an object and their inferred effect on themeasuredMR temperature. Materials and methods MR phase thermometry was performed on spherical agar phantoms enclosed in cylindrical containers at 7T. The air susceptibility inside the cylindrical container was changed by both heating the air and changing the gas composition. Results Changing the temperature of surrounding air from 23 to 69?C caused an apparent MR temperature error of 2 K. When ambient air was displaced by 100{\%} oxygen, the MR temperature error increased to 40 K. The magnetic field shift and therefore error in inferred MR temperature scales linearly with volume susceptibility change and has a strong and nontrivial dependence on the experimental configuration. Conclusion Air susceptibility changes associated with oxygen concentration changes greatly affect PRFMR thermometry measurements. Air temperature changes can also affect thesemeasurements, but to a smaller degree. For uncalibrated MR thermometry, air susceptibility changes may be a significant source of error. {\textcopyright} ESMRMB 2011.},
author = {Streicher, Markus N. and Sch{\"{a}}fer, Andreas and Reimer, Enrico and Dhital, Bibek and Trampel, Robert and Ivanov, Dimo and Turner, Robert},
doi = {10.1007/s10334-011-0249-8},
issn = {09685243},
journal = {Magnetic Resonance Materials in Physics, Biology and Medicine},
keywords = {Air,PRF,PRFS-based MR thermometry,Susceptibility,Temperature},
number = {1},
pages = {41--47},
title = {{Effects of air susceptibility on proton resonance frequency MR thermometry}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {25},
year = {2012}
}
@article{Sultana2019,
abstract = {Purpose: We propose a segmentation methodology for brainstem cranial nerves using statistical shape model (SSM)-based deformable 3D contours from T2 MR images. Methods: We create shape models for ten pairs of cranial nerves. High-resolution T2 MR images are segmented for nerve centerline using a 1-Simplex discrete deformable 3D contour model. These segmented centerlines comprise training datasets for the shape model. Point correspondence for the training dataset is performed using an entropy-based energy minimization framework applied to particles located on the centerline curve. The shape information is incorporated into the 1-Simplex model by introducing a shape-based internal force, making the deformation stable against low resolution and image artifacts. Results: The proposed method is validated through extensive experiments using both synthetic and patient MRI data. The robustness and stability of the proposed method are experimented using synthetic datasets. SSMs are constructed independently for ten pairs (CNIII–CNXII) of brainstem cranial nerves using ten non-pathological image datasets of the brainstem. The constructed ten SSMs are assessed in terms of compactness, specificity and generality. In order to quantify the error distances between segmented results and ground truths, two metrics are used: mean absolute shape distance (MASD) and Hausdorff distance (HD). MASD error using the proposed shape model is 0.19 ± 0.13 (mean ± std. deviation) mm and HD is 0.21 mm which are sub-voxel accuracy given the input image resolution. Conclusion: This paper described a probabilistic digital atlas of the ten brainstem-attached cranial nerve pairs by incorporating a statistical shape model with the 1-Simplex deformable contour. The integration of shape information as a priori knowledge results in robust and accurate centerline segmentations from even low-resolution MRI data, which is essential in neurosurgical planning and simulations for accurate and robust 3D patient-specific models of critical tissues including cranial nerves.},
author = {Sultana, Sharmin and Agrawal, Praful and Elhabian, Shireen and Whitaker, Ross and Blatt, Jason E. and Gilles, Benjamin and Cetas, Justin and Rashid, Tanweer and Audette, Michel A.},
doi = {10.1007/s11548-019-02014-z},
issn = {18616429},
journal = {International Journal of Computer Assisted Radiology and Surgery},
keywords = {3D contour models,Brainstem,Centerline,Cranial nerves,Deformable models,MRI,Segmentation,Statistical shape models,Surgical guidance},
number = {11},
pages = {1955--1967},
title = {{Medial axis segmentation of cranial nerves using shape statistics-aware discrete deformable models}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {14},
year = {2019}
}
@article{Sun2013,
abstract = {Purpose: The temporal mismatch between PET and standard helical CT (HCT) causes substantial respiratory artifacts in PET reconstructed images when using HCT as the attenuation map. Previously we developed an interpolated average CT (IACT) method for attenuation correction (AC) and demonstrated its merits in simulations. In this study we aim to apply IACT in patients with thoracic lesions using an active breathing controller (ABC). Methods: Under local ethics approval, we recruited 15 patients with a total of 18 lesions in different thoracic regions: left upper lobe (2), right upper lobe (4), right hilum (3), right lower lobe (3), left hilum (2), and esophagus (4). All patients underwent whole body PET scans 1 h after 300-480 MBq 18F-FDG injection, depending on the patients' weight. The PET sinograms were reconstructed with AC using: (i) standard HCT [120 kV, smart mA (30-150 mA), 0.984:1 pitch] and (ii) IACT obtained from end-inspiration and end-expiration breath-hold HCTs (120 kV, 10 mA, 0.984:1 pitch) aided by ABC. IACT was obtained by averaging the intensity of two extreme phases and the interpolated phases between them, where the nonlinear interpolation was obtained by B-spline registration and with an empirical sinusoidal function. The SUVmax, SUVmean, and the differences of centroid-of-lesion (d) between PET and different CT schemes were measured for each lesion. Results: From visual inspection, the respiratory artifacts and blurring generally reduced in the thoracic region for PET IACT. Matching between CT and PET improved for PETIACT, with an average decrease of d for 1.34 ± 1.79 mm as compared to PET HCT. The SUVmax and SUVmean were consistently higher for PETIACT versus PETHCT for all lesions, with (30.95 ± 18.63){\%} and (22.39 ± 15.91){\%} average increase, respectively. Conclusions: IACT-ABC reduces respiratory artifacts, PET/CT misregistration and enhances lesion quantitation. This technique is a robust and low dose AC protocol for clinical oncology application especially in the thoracic region. {\textcopyright} 2013 American Association of Physicists in Medicine.},
author = {Sun, Tao and Wu, Tung Hsin and Wang, Shyh Jen and Yang, Bang Hung and Wu, Nien Yun and Mok, Greta S.P.},
doi = {10.1118/1.4820976},
issn = {00942405},
journal = {Medical Physics},
keywords = {Active breathing controller,Attenuation correction,PET/CT,Respiratory artifacts},
number = {10},
pages = {9},
title = {{Low dose interpolated average CT for thoracic PET/CT attenuation correction using an active breathing controller}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {40},
year = {2013}
}
@article{Tasserie2020,
abstract = {Non-human primate functional MRI (fMRI) is a growing field in neuroscience. However, there is no standardized method for monkey fMRI data analysis, specifically for data preprocessing. The preprocessing of monkey fMRI data is challenged by several technical and experimental specificities of the monkey research such as artifacts related to body movements or to intracranial leads. Here we propose to address these challenges by developing a new versatile pipeline for macaque fMRI preprocessing. We developed a Python module, Pypreclin, to process raw images using state of the art algorithms embedded in a fully automatic pipeline. To evaluate its robustness, we applied Pypreclin to fMRI data acquired at 3T in both awake and anesthetized macaques, with or without iron oxide contrast agent, using single loop or multichannel phased-array coils, combined or not with intracranial implanted electrodes. We performed both resting-state and auditory evoked fMRI and compared the results of Pypreclin to a previously employed preprocessing pipeline. Pypreclin successfully achieved the registration of the fMRI data to the macaque brain template in all the experimental conditions. Moreover, Pypreclin enables more accurate locations of auditory evoked activations in relation to the gray matter at corrected level in the awake fMRI condition. Finally, using the Primate neuroimaging Data-Exchange open access platform, we could further validate Pypreclin for monkey fMRI images that were acquired at ultra-high fields, from other institutions and using different protocols. Pypreclin is a validated preprocessing tool that adapts to diverse experimental and technical situations of monkey fMRI. Pypreclin code is available on open source data sharing platform.},
author = {Tasserie, Jordy and Grigis, Antoine and Uhrig, Lynn and Dupont, Morgan and Amadon, Alexis and Jarraya, B{\'{e}}chir},
doi = {10.1016/j.neuroimage.2019.116353},
issn = {10959572},
journal = {NeuroImage},
keywords = {Automatic,Macaque,Motion artifact,Movement artifact,Non-human primate,Preprocessing,fMRI},
pages = {15},
title = {{Pypreclin: An automatic pipeline for macaque functional MRI preprocessing}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {207},
year = {2020}
}
@article{Taylor2019,
abstract = {Despite general acceptance that the retinotopic organisation of human V4 (hV4) takes the form of a single, uninterrupted ventral hemifield, measured retinotopic maps of this visual area are often incomplete. Here, we test hypotheses that artefact from draining veins close to hV4 cause inverted BOLD responses that may serve to obscure a portion of the lower visual quarterfield-including the lower vertical meridian-in some hemispheres. We further test whether correcting such responses can restore the 'missing' retinotopic coverage in hV4. Subjects (N = 10) viewed bowtie, ring, drifting bar and full field flash stimuli. Functional EPIs were acquired over approximately 1.5h and analysed to reveal retinotopic maps of early visual cortex, including hV4. Normalised mean maps (which show the average EPI signal amplitude) were constructed by voxel-wise averaging of the EPI time course and used to locate venous eclipses, which can be identified by a decrease in the EPI signal caused by deoxygenated blood. Inverted responses are shown to cluster in these regions and correcting these responses improves maps of hV4 in some hemispheres, including restoring a complete hemifield map in one. A leftwards bias was found whereby 6/10 left hemisphere hV4 maps were incomplete, while this was the case in only 1/10 right hemisphere maps. Incomplete hV4 maps did not correspond with venous artefact in every instance, with incomplete maps being present in the absence of a venous eclipse and complete maps coexisting with a proximate venous eclipse. We also show that mean maps of upper surfaces (near the boundary between cortical grey matter and CSF) provide highly detailed maps of veins on the cortical surface. Results suggest that venous eclipses and inverted voxels can explain some incomplete hV4 maps, but cannot explain the remainder nor the leftwards bias in hV4 coverage reported here.},
author = {{Boyd Taylor}, H. G. and Puckett, A. M. and Isherwood, Z. J. and Schira, M. M.},
doi = {10.1371/journal.pone.0204388},
issn = {19326203},
journal = {PLoS ONE},
number = {6},
pages = {32},
title = {{Vascular effects on the BOLD response and the retinotopic mapping of hV4}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {14},
year = {2019}
}
@article{Thomas2016,
abstract = {Positron emission tomography (PET) images are degraded by a phenomenon known as the partial volume effect (PVE). Approaches have been developed to reduce PVEs, typically through the utilisation of structural information provided by other imaging modalities such as MRI or CT. These methods, known as partial volume correction (PVC) techniques, reduce PVEs by compensating for the effects of the scanner resolution, thereby improving the quantitative accuracy. The PETPVC toolbox described in this paper comprises a suite of methods, both classic and more recent approaches, for the purposes of applying PVC to PET data. Eight core PVC techniques are available. These core methods can be combined to create a total of 22 different PVC techniques. Simulated brain PET data are used to demonstrate the utility of toolbox in idealised conditions, the effects of applying PVC with mismatched point-spread function (PSF) estimates and the potential of novel hybrid PVC methods to improve the quantification of lesions. All anatomy-based PVC techniques achieve complete recovery of the PET signal in cortical grey matter (GM) when performed in idealised conditions. Applying deconvolution-based approaches results in incomplete recovery due to premature termination of the iterative process. PVC techniques are sensitive to PSF mismatch, causing a bias of up to 16.7{\%} in GM recovery when over-estimating the PSF by 3 mm. The recovery of both GM and a simulated lesion was improved by combining two PVC techniques together. The PETPVC toolbox has been written in C++, supports Windows, Mac and Linux operating systems, is open-source and publicly available.},
author = {Thomas, Benjamin A. and Cuplov, Vesna and Bousse, Alexandre and Mendes, Adriana and Thielemans, Kris and Hutton, Brian F. and Erlandsson, Kjell},
doi = {10.1088/0031-9155/61/22/7975},
issn = {13616560},
journal = {Physics in Medicine and Biology},
keywords = {PET/MR,partial volume correction,partial volume effects,positron emission tomography},
number = {22},
pages = {7975--7993},
title = {{PETPVC: A toolbox for performing partial volume correction techniques in positron emission tomography}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {61},
year = {2016}
}
@article{Tixier2019,
abstract = {Background: Glioblastoma (GBM) is the most common malignant central nervous system tumor, and MGMT promoter hypermethylation in this tumor has been shown to be associated with better prognosis. We evaluated the capacity of radiomics features to add complementary information to MGMT status, to improve the ability to predict prognosis. Methods: 159 patients with untreated GBM were included in this study and divided into training and independent test sets. 286 radiomics features were extracted from the magnetic resonance images acquired prior to any treatments. A least absolute shrinkage selection operator (LASSO) selection followed by Kaplan-Meier analysis was used to determine the prognostic value of radiomics features to predict overall survival (OS). The combination of MGMT status with radiomics was also investigated and all results were validated on the independent test set. Results: LASSO analysis identified 8 out of the 286 radiomic features to be relevant which were then used for determining association to OS. One feature (edge descriptor) remained significant on the external validation cohort after multiple testing (p=0.04) and the combination with MGMT identified a group of patients with the best prognosis with a survival probability of 0.61 after 43 months (p=0.0005). Conclusion: Our results suggest that combining radiomics with MGMT is more accurate in stratifying patients into groups of different survival risks when compared to with using these predictors in isolation. We identified two subgroups within patients who have methylated MGMT: one with a similar survival to unmethylated MGMT patients and the other with a significantly longer OS.},
author = {Tixier, Florent and Um, Hyemin and Bermudez, Dalton and Iyer, Aditi and Apte, Aditya and Graham, Maya S. and Nevel, Kathryn S. and Deasy, Joseph O. and Young, Robert J. and Veeraraghavan, Harini},
doi = {10.18632/oncotarget.26578},
issn = {19492553},
journal = {Oncotarget},
keywords = {Glioblastoma,MGMT,Magnetic resonance imaging,Radiomics,Survival analysis},
number = {6},
pages = {660--672},
title = {{Preoperative MRI-radiomics features improve prediction of survival in glioblastoma patients over MGMT methylation status alone}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060153455{\&}doi=10.18632{\%}2Foncotarget.26578{\&}partnerID=40{\&}md5=fc3514c9fba21e53a37d3020503c236d},
volume = {10},
year = {2019}
}
@article{Tong2016,
abstract = {Purpose: To elucidate the molecular pathogenesis of age-related macular degeneration (AMD) and interpretation of fundus autofluorescence imaging, the authors identified spectral autofluorescence characteristics of drusen and retinal pigment epithelium (RPE) in donor eyes with AMD. Methods: Macular RPE/Bruch membrane flat mounts were prepared from 5 donor eyes with AMD. In 12 locations (1-3 per eye), hyperspectral autofluorescence images in 10-nmwavelength steps were acquired at 2 excitation wavelengths (lex 436, 480 nm). A nonnegative tensor factorization algorithm was used to recover 5 abundant emission spectra and their corresponding spatial localizations. Results: At lex 436 nm, the authors consistently localized a novel spectrum (SDr) with a peak emission near 510 nm in drusen and sub-RPE deposits. Abundant emission spectra seen previously (S0 in Bruch membrane and S1, S2, and S3 in RPE lipofuscin/melanolipofuscin, respectively) also appeared in AMD eyes, with the same shapes and peak wavelengths as in normal tissue. Lipofuscin/melanolipofuscin spectra localizations in AMD eyes varied widely in their overlap with drusen, ranging from none to complete. Conclusion: An emission spectrum peaking at-510 nm (lex 436 nm) appears to be sensitive and specific for drusen and sub-RPE deposits. One or more abundant spectra from RPE organelles exhibit characteristic relationships with drusen.},
author = {Tong, Yuehong and Ami, Tal Ben and Hong, Sungmin and Heintzmann, Rainer and Gerig, Guido and Ablonczy, Zsolt and Curcio, Christine A. and Ach, Thomas and Smith, R. Theodore},
doi = {10.1097/IAE.0000000000001325},
issn = {15392864},
journal = {Retina},
keywords = {Age-related macular degeneration,Autofluorescence,Bruch membrane,Drusen,Fluorophores,Hyperspectral imaging,Lipofuscin,Nonnegative tensor factorization,Retinal pigment epithelium,Sub-retinal pigment epithelium deposits},
number = {12},
pages = {S127--S136},
title = {{Hyperspectral autofluorescence imaging of drusen and retinal pigment epithelium in donor eyes with age-related macular degeneration}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {36},
year = {2016}
}
@article{Tourbier2015,
abstract = {Although fetal anatomy can be adequately viewed in new multi-slice MR images, many critical limitations remain for quantitative data analysis. To this end, several research groups have recently developed advanced image processing methods, often denoted by super-resolution (SR) techniques, to reconstruct from a set of clinical low-resolution (LR) images, a high-resolution (HR) motion-free volume. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has been quite attracted by Total Variation energies because of their ability in edge preserving but only standard explicit steepest gradient techniques have been applied for optimization. In a preliminary work, it has been shown that novel fast convex optimization techniques could be successfully applied to design an efficient Total Variation optimization algorithm for the super-resolution problem. In this work, two major contributions are presented. Firstly, we will briefly review the Bayesian and Variational dual formulations of current state-of-the-art methods dedicated to fetal MRI reconstruction. Secondly, we present an extensive quantitative evaluation of our SR algorithm previously introduced on both simulated fetal and real clinical data (with both normal and pathological subjects). Specifically, we study the robustness of regularization terms in front of residual registration errors and we also present a novel strategy for automatically select the weight of the regularization as regards the data fidelity term. Our results show that our TV implementation is highly robust in front of motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI recovery as in comparison with state-of-the art methods.},
author = {Tourbier, S{\'{e}}bastien and Bresson, Xavier and Hagmann, Patric and Thiran, Jean Philippe and Meuli, Reto and Cuadra, Meritxell Bach},
doi = {10.1016/j.neuroimage.2015.06.018},
issn = {10959572},
journal = {NeuroImage},
keywords = {Fast convex optimization,Fetal brain MRI,Super-resolution,Total variation},
pages = {584--597},
pmid = {26072252},
title = {{An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940440329{\&}doi=10.1016{\%}2Fj.neuroimage.2015.06.018{\&}partnerID=40{\&}md5=58941cfc11c891f903f91a0bf8aa005c},
volume = {118},
year = {2015}
}
@article{Tourbier2017,
abstract = {Most fetal brain MRI reconstruction algorithms rely only on brain tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion correction and image reconstruction. Consequently the fetal brain needs to be localized and extracted as a first step, which is usually a laborious and time consuming manual or semi-automatic task. We have proposed in this work to use age-matched template images as prior knowledge to automatize brain localization and extraction. This has been achieved through a novel automatic brain localization and extraction method based on robust template-to-slice block matching and deformable slice-to-template registration. Our template-based approach has also enabled the reconstruction of fetal brain images in standard radiological anatomical planes in a common coordinate space. We have integrated this approach into our new reconstruction pipeline that involves intensity normalization, inter-slice motion correction, and super-resolution (SR) reconstruction. To this end we have adopted a novel approach based on projection of every slice of the LR brain masks into the template space using a fusion strategy. This has enabled the refinement of brain masks in the LR images at each motion correction iteration. The overall brain localization and extraction algorithm has shown to produce brain masks that are very close to manually drawn brain masks, showing an average Dice overlap measure of 94.5{\%}. We have also demonstrated that adopting a slice-to-template registration and propagation of the brain mask slice-by-slice leads to a significant improvement in brain extraction performance compared to global rigid brain extraction and consequently in the quality of the final reconstructed images. Ratings performed by two expert observers show that the proposed pipeline can achieve similar reconstruction quality to reference reconstruction based on manual slice-by-slice brain extraction. The proposed brain mask refinement and reconstruction method has shown to provide promising results in automatic fetal brain MRI segmentation and volumetry in 26 fetuses with gestational age range of 23 to 38 weeks.},
author = {Tourbier, S{\'{e}}bastien and Velasco-Annis, Clemente and Taimouri, Vahid and Hagmann, Patric and Meuli, Reto and Warfield, Simon K. and {Bach Cuadra}, Meritxell and Gholipour, Ali},
doi = {10.1016/j.neuroimage.2017.04.004},
issn = {10959572},
journal = {NeuroImage},
keywords = {B-Spline deformation,Block matching,Brain localization,Fetal brain MRI,Slice-by-slice brain extraction,Slice-to-template registration,Super-resolution reconstruction},
pages = {460--472},
title = {{Automated template-based brain localization and extraction for fetal brain MRI reconstruction}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {155},
year = {2017}
}
@article{Tournier2019,
abstract = {MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualisation, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software.},
author = {Tournier, J. Donald and Smith, Robert and Raffelt, David and Tabbara, Rami and Dhollander, Thijs and Pietsch, Maximilian and Christiaens, Daan and Jeurissen, Ben and Yeh, Chun Hung and Connelly, Alan},
doi = {10.1016/j.neuroimage.2019.116137},
issn = {10959572},
journal = {NeuroImage},
keywords = {Image,MRI,Processing,Software,Visualisation},
pages = {17},
title = {{MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {202},
year = {2019}
}
@article{Townsend2017,
abstract = {The ability to perform non-destructive areal surface analysis of the internal surfaces of additively manufactured (AM) components would be advantageous during product development, process control and product acceptance. Currently industrial X-ray computed tomography (XCT) is the only practical method for imaging the internal surfaces of AM components. A viable method of extracting useable areal surface texture data from XCT scans has now been developed and this paper reports on three measurement and data processing factors affecting the value of areal parameters per ISO 25178-2 generated from XCT volume data using this novel technique.},
author = {Townsend, Andrew and Pagani, Luca and Blunt, Liam and Scott, Paul J. and Jiang, Xiangqian},
doi = {10.1016/j.cirp.2017.04.074},
issn = {17260604},
journal = {CIRP Annals - Manufacturing Technology},
keywords = {Additive manufacturing,Metrology,X-ray},
number = {1},
pages = {547--550},
title = {{Factors affecting the accuracy of areal surface texture data extraction from X-ray CT}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {66},
year = {2017}
}
@inbook{Tung2009,
abstract = {Three dimensional (3D) image processing and visualisation methods were applied in craniomaxillofacial surgery for preoperative surgical procedures and surgery planning. Each patient differed in their formation of cranium and facial bones, hence requiring customised reconstruction to identify the defect area and to plan procedural steps. This paper explores the processing and visualisation of patients' data into 3D form, constructed from flat two dimensional (2D) Computed Tomography (CT) images. Depth perception has been useful to identify certain regions of Interest (ROI) elusive in 2D CT slices. We have noted that the 3D models have exemplified the depth perception with the provision of additional cues of perspective, motion, texture and steropsis. This has led to the improvement of treatment design and implementation for patients in this study. {\textcopyright} 2009 Springer-Verlag.},
address = {Berlin},
author = {Tung, Tan Su and Rathinam, Alwin Kumar and Kumar, Yuwaraj and Rahman, Zainal Ariff Abdul},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-642-05036-7_15},
editor = {Zaman, H B and Robinson, P and Petrou, M and Olivier, P and Schroder, H and Shih, T K},
isbn = {3642050352},
issn = {03029743},
keywords = {3D modelling,3D stereo visualisation,Medical image processing,VRC-UM,Virtual reality},
pages = {148--155},
publisher = {Springer-Verlag Berlin},
series = {Lecture Notes in Computer Science},
title = {{Additional cues derived from three dimensional image processing to aid customised reconstruction for medical applications}},
type = {Book Section},
url = {{\%}3CGo to},
volume = {5857 LNCS},
year = {2009}
}
@incollection{Tward2019,
abstract = {The neuroimaging field is moving toward micron scale and molecular features in digital pathology and animal models. These require mapping to common coordinates for annotation, statistical analysis, and collaboration. An important example, the BRAIN Initiative Cell Census Network, is generating 3D brain cell atlases in mouse, and ultimately primate and human. We aim to establish RNAseq profiles from single neurons and nuclei across the mouse brain, mapped to Allen Common Coordinate Framework (CCF). Imaging includes (Forumala Presented). 500 tape-transfer cut 20 (Forumala Presented). m thick Nissl-stained slices per brain. In key areas 100 {\&}{\#}x0024;{\&}{\#}x0024;$\backslash$upmu {\&}{\#}x0024;{\&}{\#}x0024; m thick slices with 0.5–2 mm diameter circular regions punched out for snRNAseq are imaged. These contain abnormalities including contrast changes and missing tissue, two challenges not jointly addressed in diffeomorphic image registration. Existing methods for mapping 3D images to histology require manual steps unacceptable for high throughput, or are sensitive to damaged tissue. Our approach jointly: registers 3D CCF to 2D slices, models contrast changes, estimates abnormality locations. Our registration uses 4 unknown deformations: 3D diffeomorphism, 3D affine, 2D diffeomorphism per-slice, 2D rigid per-slice. Contrast changes are modeled using unknown cubic polynomials per-slice. Abnormalities are estimated using Gaussian mixture modeling. The Expectation Maximization algorithm is used iteratively, with E step: compute posterior probabilities of abnormality, M step: registration and intensity transformation minimizing posterior-weighted sum-of-square-error. We produce per-slice anatomical labels using Allen Institute's ontology, and publicly distribute results online, with several typical and abnormal slices shown here. This work has further applications in digital pathology, and 3D brain mapping with stroke, multiple sclerosis, or other abnormalities.},
author = {Tward, Daniel and Li, Xu and Huo, Bingxing and Lee, Brian and Mitra, Partha and Miller, Michael},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-030-33226-6_18},
isbn = {9783030332259},
issn = {16113349},
keywords = {Histology,Image registration,Neuroimaging},
pages = {162--173},
title = {{3D Mapping of Serial Histology Sections with Anomalies Using a Novel Robust Deformable Registration Algorithm}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075564799{\&}doi=10.1007{\%}2F978-3-030-33226-6{\_}18{\&}partnerID=40{\&}md5=09c3ae4bdd0efa79e25ca5a9e196c9eb},
volume = {11846 LNCS},
year = {2019}
}
@article{Um2019,
abstract = {Recent advances in radiomics have enhanced the value of medical imaging in various aspects of clinical practice, but a crucial component that remains to be investigated further is the robustness of quantitative features to imaging variations and across multiple institutions. In the case of MRI, signal intensity values vary according to the acquisition parameters used, yet no consensus exists on which preprocessing techniques are favorable in reducing scanner-dependent variability of image-based features. Hence, the purpose of this study was to assess the impact of common image preprocessing methods on the scanner dependence of MRI radiomic features in multi-institutional glioblastoma multiforme (GBM) datasets. Two independent GBM cohorts were analyzed: 50 cases from the TCGA-GBM dataset and 111 cases acquired in our institution, and each case consisted of 3 MRI sequences viz. FLAIR, T1-weighted, and T1-weighted post-contrast. Five image preprocessing techniques were examined: 8-bit global rescaling, 8-bit local rescaling, bias field correction, histogram standardization, and isotropic resampling. A total of 420 features divided into eight categories representing texture, shape, edge, and intensity histogram were extracted. Two distinct imaging parameters were considered: scanner manufacturer and scanner magnetic field strength. Wilcoxon tests identified features robust to the considered acquisition parameters under the selected image preprocessing techniques. A machine learning-based strategy was implemented to measure the covariate shift between the analyzed datasets using features computed using the aforementioned preprocessing methods. Finally, radiomic scores (rad-scores) were constructed by identifying features relevant to patients' overall survival after eliminating those impacted by scanner variability. These were then evaluated for their prognostic significance through Kaplan-Meier and Cox hazards regression analyses. Our results demonstrate that overall, histogram standardization contributes the most in reducing radiomic feature variability as it is the technique to reduce the covariate shift for three feature categories and successfully discriminate patients into groups of different survival risks.},
author = {Um, Hyemin and Tixier, Florent and Bermudez, Dalton and Deasy, Joseph O. and Young, Robert J. and Veeraraghavan, Harini},
doi = {10.1088/1361-6560/ab2f44},
issn = {13616560},
journal = {Physics in Medicine and Biology},
keywords = {covariate shift,glioblastoma multiforme (GBM),image preprocessing,magnetic resonance imaging (MRI),multi-institution,radiomics,scanner variations},
number = {16},
pages = {12},
title = {{Impact of image preprocessing on the scanner dependence of multi-parametric MRI radiomic features and covariate shift in multi-institutional glioblastoma datasets}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {64},
year = {2019}
}
@article{Urish2013,
abstract = {Objective: Although conventional radiography is used to assess osteoarthritis in a clinical setting, it has limitations, including an inability to stage early cartilage degeneration. There is a growing interest in using quantitative magnetic resonance imaging to identify degenerative changes in articular cartilage, including the large multicentered study, the Osteoarthritis Initiative (OAI). There is a demand for suitable image registration and segmentation software to complete this analysis. The objective of this study was to develop and validate the open source software, ImageK, that registers 3 T MRI T2 mapping and double echo steady state (DESS) knee MRI sequences acquired in the OAI protocol. Methods: A C++ library, the insight toolkit, was used to develop open source software to register DESS and T2 mapping image MRI sequences using Mattes's Multimodality Mutual information metric. Results: Registration was assessed using three separate methods. A checkerboard layout demonstrated acceptable visual alignment. Fiducial markers placed in cadaveric knees measured a registration error of 0.85 voxels. Measuring the local variation in Mattes's Mutual Information metric in the local area of the registered solution showed precision within 1 pixel. In this group, the registered solution required a transform of 56 voxels in translation and 1 degree of rotation. Conclusion: The software we have developed, ImageK, provides free, open source image analysis software that registers DESS and T2 mapping sequences of knee articular cartilage within 1 voxel accuracy. This image registration software facilitates quantitative MRI analyses of knee articular cartilage. {\textcopyright} The Author(s) 2013.},
author = {Urish, Kenneth L. and Williams, Ashley A. and Durkin, John R. and Chu, Constance R.},
doi = {10.1177/1947603512451745},
issn = {19476035},
journal = {Cartilage},
keywords = {DESS,MRI,T2,cartilage,registration},
number = {1},
pages = {20--27},
title = {{Registration of Magnetic Resonance Image Series for Knee Articular Cartilage Analysis: Data from the Osteoarthritis Initiative}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {4},
year = {2013}
}
@article{Uthoff2019,
abstract = {Background: This study explores whether objective, quantitative radiomic biomarkers derived from magnetic resonance (MR), positron emission tomography (PET), and computed tomography (CT) may be useful in reliably distinguishing malignant peripheral nerve sheath tumors (MPNST) from benign plexiform neurofibromas (PN). Methods: A registration and segmentation pipeline was established using a cohort of NF1 patients with histopathological diagnosis of PN or MPNST, and medical imaging of the PN including MR and PET-CT. The corrected MR datasets were registered to the corresponding PET-CT via landmark-based registration. PET standard-uptake value (SUV) thresholds were used to guide segmentation of volumes of interest: MPNST-associated PET-hot regions (SUV ≥ 3.5) and PN-associated PET-elevated regions (2.0 {\textless} SUV {\textless} 3.5). Quantitative imaging features were extracted from the MR, PET, and CT data and compared for statistical differences. Intensity histogram features included (mean, media, maximum, variance, full width at half maximum, entropy, kurtosis, and skewness), while image texture was quantified using Law's texture energy measures, grey-level co-occurrence matrices, and neighborhood grey-tone difference matrices. Results: For each of the 20 NF1 subjects, a total of 320 features were extracted from the image data. Feature reduction and statistical testing identified 9 independent radiomic biomarkers from the MR data (4 intensity and 5 texture) and 4 PET (2 intensity and 2 texture) were different between the PET-hot versus PET-elevated volumes of interest. Conclusions: Our data suggests imaging features can be used to distinguish malignancy in NF1-realted tumors, which could improve MPNST risk assessment and positively impact clinical management of NF1 patients.},
author = {Uthoff, J. and {De Stefano}, F. A. and Panzer, K. and Darbro, B. W. and Sato, T. S. and Khanna, R. and Quelle, D. E. and Meyerholz, D. K. and Weimer, J. and Sieren, J. C.},
doi = {10.1016/j.neurad.2018.05.006},
issn = {17730406},
journal = {Journal of Neuroradiology},
keywords = {Magnetic resonance imaging,Malignant peripheral nerve sheath tumor,Plexiform neurofibroma,Positron emission tomography,Quantitative feature extraction},
number = {3},
pages = {179--185},
title = {{Radiomic biomarkers informative of cancerous transformation in neurofibromatosis-1 plexiform tumors}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {46},
year = {2019}
}
@article{Vanquin2019,
abstract = {Purpose: Magnetic resonance imaging (MRI) plays an increasing role in radiotherapy dose planning. Indeed, MRI offers superior soft tissue contrast compared to computerized tomography (CT) and therefore could provide a better delineation of target volumes and organs at risk than CT for radiotherapy. Furthermore, an MRI-only radiotherapy workflow would suppress registration errors inherent to the registration of MRI with CT. However, the estimation of the electronic density of tissues using MRI images is still a challenging issue. The purpose of this work was to design and evaluate a pseudo-CT generation method for prostate cancer treatments. Materials and methods: A pseudo-CT was generated for ten prostate cancer patients using an elastic deformation based method. For each patient, dose delivered to the patient was calculated using both the planning CT and the pseudo-CT. Dose differences between CT and pseudo-CT were investigated. Results: Mean dose relative difference in the planning target volume is 0.9{\%} on average and ranges from 0.1{\%} to 1.7{\%}. In organs at risks, this value is 1.8{\%}, 0.8{\%}, 0.8{\%} and 1{\%} on average in the rectum, the right and left femoral heads, and the bladder respectively. Conclusion: The dose calculated using the pseudo-CT is very close to the dose calculated using the CT for both organs at risk and PTV. These results confirm that pseudo-CT images generated using the proposed method could be used to calculate radiotherapy treatment doses on MRI images.},
author = {Vanquin, L. and Boydev, C. and Korhonen, J. and Rault, E. and Crop, F. and Lacornerie, T. and Wagner, A. and Laffarguette, J. and Pasquier, D. and Reynaert, N.},
doi = {10.1016/j.canrad.2018.09.005},
issn = {17696658},
journal = {Cancer/Radiotherapie},
keywords = {Elastic registration,MRI,Pseudo-CT (Pseudo-Computerized Tomography),Radiotherapy},
number = {4},
pages = {281--289},
title = {{Radiotherapy treatment planning of prostate cancer using magnetic resonance imaging}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {23},
year = {2019}
}
@inbook{Veress2014,
abstract = {In 1961, scientists from biology, geology, and material sciences convened a small meeting at a mountaintop retreat in Germany. Their purpose was to discuss an obscure problem common to all of their research: how to quantify the morphological features of arbitrary-shaped 3-D objects based on their 2-D appearance on cut surfaces. To describe this topic, they selected the term stereology, from the Greek stereos for “the study of objects in three dimensions,” and a year later formed the International Society for Stereology (ISS). After more than a decade of debating the relative merits of different stereology approaches in their respective fields of study, the ISS categorically rejected older methods based on assumption-and model-based Euclidean geometry in favor of innovative new methods designed to avoid methodological bias. Today unbiased or design-based stereology is the sine qua non for the reliable quantification of structure in many fields of biological and biomedical research. This chapter examines key concepts of design-based stereology using systematic random sampling and objective geometry probes in comparison with biased methods. To exemplify an application to neurosciences (neurostereology), we review the results and discussion from our recently published study to quantify differences in postmortem brains from autistic and normal children. Continuing advances in computer-assisted microscopy, image segmentation, and whole slide scanning support the growing trend toward higher throughput and greater efficiency in computerized stereology, with the promise of fully automatic and accurate stereoanalysis of tissue sections and in vivo images in the near future.},
author = {Mouton, Peter R.},
booktitle = {Handbook of Imaging in Biological Mechanics},
doi = {10.1201/b17566},
isbn = {9781466588141},
pages = {217--228},
title = {{Quantitative anatomy using design-based stereology}},
type = {Book Section},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054614475{\&}doi=10.1201{\%}2Fb17566{\&}partnerID=40{\&}md5=c910ea6ed3c6c6ab22591fe45986254f},
year = {2014}
}
@article{Vijayavenkataraman2018,
abstract = {3D bioprinting is a pioneering technology that enables fabrication of biomimetic, multiscale, multi-cellular tissues with highly complex tissue microenvironment, intricate cytoarchitecture, structure-function hierarchy, and tissue-specific compositional and mechanical heterogeneity. Given the huge demand for organ transplantation, coupled with limited organ donors, bioprinting is a potential technology that could solve this crisis of organ shortage by fabrication of fully-functional whole organs. Though organ bioprinting is a far-fetched goal, there has been a considerable and commendable progress in the field of bioprinting that could be used as transplantable tissues in regenerative medicine. This paper presents a first-time review of 3D bioprinting in regenerative medicine, where the current status and contemporary issues of 3D bioprinting pertaining to the eleven organ systems of the human body including skeletal, muscular, nervous, lymphatic, endocrine, reproductive, integumentary, respiratory, digestive, urinary, and circulatory systems were critically reviewed. The implications of 3D bioprinting in drug discovery, development, and delivery systems are also briefly discussed, in terms of in vitro drug testing models, and personalized medicine. While there is a substantial progress in the field of bioprinting in the recent past, there is still a long way to go to fully realize the translational potential of this technology. Computational studies for study of tissue growth or tissue fusion post-printing, improving the scalability of this technology to fabricate human-scale tissues, development of hybrid systems with integration of different bioprinting modalities, formulation of new bioinks with tuneable mechanical and rheological properties, mechanobiological studies on cell-bioink interaction, 4D bioprinting with smart (stimuli-responsive) hydrogels, and addressing the ethical, social, and regulatory issues concerning bioprinting are potential futuristic focus areas that would aid in successful clinical translation of this technology.},
author = {Vijayavenkataraman, Sanjairaj and Yan, Wei Cheng and Lu, Wen Feng and Wang, Chi Hwa and Fuh, Jerry Ying Hsi},
doi = {10.1016/j.addr.2018.07.004},
issn = {18728294},
journal = {Advanced Drug Delivery Reviews},
keywords = {3D bioprinting,3D printing,Bioprinting,Organ printing,Regenerative medicine,Tissue engineering},
pages = {296--332},
title = {{3D bioprinting of tissues and organs for regenerative medicine}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {132},
year = {2018}
}
@article{Vikal2010,
abstract = {Motivation: Image-guided percutaneous (through the skin) needle-based surgery has become part of routine clinical practice in performing procedures such as biopsies, injections and therapeutic implants. A novice physician typically performs needle interventions under the supervision of a senior physician; a slow and inherently subjective training process that lacks objective, quantitative assessment of the surgical skill and performance. Shortening the learning curve and increasing procedural consistency are important factors in assuring high-quality medical care. Methods: This paper describes a laboratory validation system, called Perk Station, for standardized training and performance measurement under different assistance techniques for needle-based surgical guidance systems. The initial goal of the Perk Station is to assess and compare different techniques: 2D image overlay, biplane laser guide, laser protractor and conventional freehand. The main focus of this manuscript is the planning and guidance software system developed on the 3D Slicer platform, a free, open source software package designed for visualization and analysis of medical image data. Results: The prototype Perk Station has been successfully developed, the associated needle insertion phantoms were built, and the graphical user interface was fully implemented. The system was inaugurated in undergraduate teaching and a wide array of outreach activities. Initial results, experiences, ongoing activities and future plans are reported. {\textcopyright} 2009 Elsevier Ltd. All rights reserved.},
author = {Vikal, Siddharth and U-Thainual, Paweena and Carrino, John A. and Iordachita, Iulian and Fischer, Gregory S. and Fichtinger, Gabor},
doi = {10.1016/j.compmedimag.2009.05.001},
issn = {08956111},
journal = {Computerized Medical Imaging and Graphics},
keywords = {Augmented reality,Image guidance,Needle placement,Surgical training},
number = {1},
pages = {19--32},
title = {{Perk Station-Percutaneous surgery training and performance measurement platform}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {34},
year = {2010}
}
@article{VonFalck2008,
abstract = {OBJECTIVES: To analyze the effects of the sliding-thin-slab averaging algorithm on low-contrast performance in MDCT imaging and to find reasonable parameters for clinical routine work. MATERIALS AND METHODS: A low-contrast phantom simulating hypodense lesions (20 HU object contrast) was scanned with a 16-slice spiral CT scanner using different mAs-settings of 25, 50, 100, and 195 mAs. Other scan parameters were as follows: tube voltage = 120 kVp, slice collimation = 0.625 mm, pitch = 1.375 (high speed), reconstruction interval = 0.5 mm. Images were reconstructed with soft, standard, and bone algorithms, resulting in a total of 12 datasets. A sliding-thin-slab averaging algorithm was applied to these primary datasets, systematically varying the slab thickness between 0.5 and 5.0 mm. The low-contrast performance of the resulting datasets was semi-automatically analyzed using a statistical reader-independent approach: A size-dependent analysis of the image noise within the phantom was used to empirically generate a contrast discrimination function (CDF). The ratio between the actual contrast and the minimum contrast necessary for the detection (as given by the CDF) was calculated for all lesions in each dataset and used to evaluate the low-contrast detectability of the different lesions at increasing slab thickness. The results were compared with the original datasets to calculate the improvement in low-contrast detectability. RESULTS: Using the sliding-thin-slab algorithm, low-contrast performance was increased by a factor between 1.1 and 1.7 when compared with the primary dataset. The improvement of the visibility index at optimal slab thickness when compared with the original slice thickness (0.625 mm) was statistically significant (P {\textless} 0.05, Student t test) for the following datasets: 8 mm: all datasets; 6 mm: 25 mAs/soft, 195 mAs/bone, 25 mAs/bone; 5 mm: 25 mAs/soft, 25 mAs/bone. The ideal slab thickness over all datasets was 43{\%} (±3{\%}) of the diameter of the lesion to be detected. CONCLUSIONS: The use of an interactive sliding-thin-slab averaging algorithm can be readily applied to optimize low-contrast detectability in thin-collimated CT datasets. As a general rule for daily routine, a slice thickness of approximately 2.5 to 3.0 mm can be regarded as a reasonable preset, resulting in an optimized detectability of lesions with a diameter of 5 mm and above. {\textcopyright} 2008 Lippincott Williams {\&} Wilkins, Inc.},
author = {{Von Falck}, Christian and Hartung, Alexander and Berndzen, Frank and King, Benjamin and Galanski, Michael and Shin, Hoen Oh},
doi = {10.1097/RLI.0b013e3181614f2d},
issn = {00209996},
journal = {Investigative Radiology},
keywords = {Contrast,Imaging phantom,Spiral CT},
number = {4},
pages = {229--235},
title = {{Optimization of low-contrast detectability in thin-collimated modern multidetector CT using an interactive sliding-thin-slab averaging algorithm}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-40949127029{\&}doi=10.1097{\%}2FRLI.0b013e3181614f2d{\&}partnerID=40{\&}md5=787eeed3860e71b1c776f64c81144821},
volume = {43},
year = {2008}
}
@article{Falck2010,
abstract = {Rationale and Objectives: To develop and evaluate a novel algorithm for semiautomated segmentation and volumetry of pleural effusions in multidetector computed tomography (MDCT) datasets. Materials and Methods: A seven-step algorithm for semiautomated segmentation of pleural effusions in MDCT datasets was developed, mainly using algorithms from the ITK image processing library. Semiautomated segmentation of pleural effusions was performed in 40 MDCT datasets of the chest (males = 22, females = 18, mean age: 56.7 ± 19.3 years). The accuracy of the semiautomated segmentation as compared with a manual segmentation approach was quantified based on the differences of the segmented volumes, the degree of over-/undersegmentation, and the Hausdorff distance. The time needed for the semiautomated and the manual segmentation process were recorded and compared. Results: The mean volume of the pleural effusions was 557.30 mL (± 477.27 mL) for the semiautomated and 553.19 (± 473.49 mL) for the manual segmentation. The difference was not statistically significant (Student t-test, P = .133). Regression analysis confirmed a strong relationship between the semiautomated algorithm and the gold standard (r2 = 0.998). Mean overlap of the segmented areas was 79{\%} (± 9.3{\%}) over all datasets with moderate oversegmentation (22{\%} ± 9.3{\%}) and undersegmentation (21{\%} ± 9.7{\%}). The mean Hausdorff distance was 17.2 mm (± 8.35 mm). The mean duration of the semiautomated segmentation process with user interaction was 8.4 minutes (± 2.6 minutes) as compared to 32.9 minutes (± 17.4 minutes) for manual segmentation. Conclusion: The semiautomated algorithm for segmentation and volumetry of pleural effusions in MDCT datasets shows a high diagnostic accuracy when compared with manual segmentation. {\textcopyright} 2010 AUR.},
author = {von Falck, Christian and Meier, Simone and J{\"{o}}rdens, Steffen and King, Benjamin and Galanski, Michael and oh Shin, Hoen},
doi = {10.1016/j.acra.2010.02.011},
issn = {10766332},
journal = {Academic Radiology},
keywords = {MDCT,Pleural effusion,diaphragm,segmentation,volumetry},
number = {7},
pages = {841--848},
title = {{Semiautomated Segmentation of Pleural Effusions in MDCT Datasets}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952884852{\&}doi=10.1016{\%}2Fj.acra.2010.02.011{\&}partnerID=40{\&}md5=af8867fcfe3518e57c7b6076890f3032},
volume = {17},
year = {2010}
}
@inproceedings{Weber,
abstract = {Image segmentation plays a major role in medical imaging. Especially in radiology, the detection and development of tumors and other diseases can be supported by image segmentation applications. Tools that provide image segmentation and calculation of segmentation scores are not available at any time for every device due to the size and scope of functionalities they offer. These tools need huge periodic updates and do not properly work on old or weak systems. However, medical use-cases often require fast and accurate results. A complex and slow software can lead to additional stress and thus unnecessary errors. The aim of this contribution is the development of a cross-platform tool for medical segmentation use-cases. The goal is a device-independent and always available possibility for medical imaging including manual segmentation and metric calculation. The result is Studierfenster (studierfenster.at), a web-tool for manual segmentation and segmentation metric calculation. In this contribution, the focus lies on the segmentation metric calculation part of the tool. It provides the functionalities of calculating directed and undirected Hausdorff Distance (HD) and Dice Similarity Coefficient (DSC) scores for two uploaded volumes, filtering for specific values, searching for specific values in the calculated metrics and exporting filtered metric lists in different file formats.},
author = {Weber, Maximilian and Wild, Daniel and Wallner, Jurgen and Egger, Jan},
booktitle = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS},
doi = {10.1109/EMBC.2019.8856481},
isbn = {9781538613115},
issn = {1557170X},
pages = {3463--3467},
title = {{A Client/Server based Online Environment for the Calculation of Medical Segmentation Scores}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077880308{\&}doi=10.1109{\%}2FEMBC.2019.8856481{\&}partnerID=40{\&}md5=289fafd6a7830c42d4e57271141c9bce},
year = {2019}
}
@article{Weis2015,
abstract = {{\textcopyright} 2015 Society of Photo-Optical Instrumentation Engineers (SPIE).Cancer progression has been linked to mechanics. Therefore, there has been recent interest in developing noninvasive imaging tools for cancer assessment that are sensitive to changes in tissue mechanical properties. We have developed one such method, modality independent elastography (MIE), that estimates the relative elastic properties of tissue by fitting anatomical image volumes acquired before and after the application of compression to biomechanical models. The aim of this study was to assess the accuracy and reproducibility of the method using phantoms and a murine breast cancer model. Magnetic resonance imaging data were acquired, and the MIE method was used to estimate relative volumetric stiffness. Accuracy was assessed using phantom data by comparing to gold-standard mechanical testing of elasticity ratios. Validation error was {\textless}12{\%}. Reproducibility analysis was performed on animal data, and within-subject coefficients of variation ranged from 2 to 13{\%} at the bulk level and 32{\%} at the voxel level. To our knowledge, this is the first study to assess the reproducibility of an elasticity imaging metric in a preclinical cancer model. Our results suggest that the MIE method can reproducibly generate accurate estimates of the relative mechanical stiffness and provide guidance on the degree of change needed in order to declare biological changes rather than experimental error in future therapeutic studies.},
author = {Weis, Jared A. and Flint, Katelyn M. and Sanchez, Violeta and Yankeelov, Thomas E. and Miga, Michael I.},
doi = {10.1117/1.jmi.2.3.036001},
issn = {2329-4302},
journal = {Journal of Medical Imaging},
number = {3},
pages = {036001},
title = {{Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {2},
year = {2015}
}
@article{Wellein2011,
abstract = {In various clinical or research scenarios, such as neurosurgical intervention planning, diagnostics, or clinical studies concerning neurological diseases, cortex segmentation can be of great value. As, e.g., the visualization of the cortical surface along with target and risk structures enables conservative access planning and gives context information about the patient-specific anatomy. We present an interactive cortex segmentation pipeline (CSP) for T1-weighted MR images, utilizing watershed and level set methods. It is designed to allow the user to adjust the intermediate results at any stage of the segmentation process. Particular attention is paid to the appropriate visualization of the segmentation in the context of the original data for verification and to different interaction methods (manual editing, parameter tuning, morphological operations). Evaluation of the interactive CSP is performed with the Segmentation Validation Engine (SVE) by Shattuck et al. (NeuroImage 45(2):431-439, 2009). The segmentation quality of our method is comparable to the best results of three different established methods: the brain extraction tool (BET), brain surface extractor (BSE), and hybrid watershed algorithm (HWA). Being designed for interaction, the CSP integrates the users' expertise by allowing him to perform correction at any stage of the pipeline, enabling him to easily achieve a segmentation fulfilling his specific needs. {\textcopyright} 2010 Springer-Verlag.},
author = {Wellein, Daniela and Born, Silvia and Pfeifle, Matthias and Duffner, Frank and Bartz, Dirk},
doi = {10.1007/s00450-010-0130-4},
issn = {18652034},
journal = {Computer Science - Research and Development},
keywords = {Cortex segmentation,Level set method,Neurosurgical intervention planning,Segmentation evaluation,User interaction,Watershed algorithm},
number = {1-2},
pages = {87--96},
title = {{A pipeline for interactive cortex segmentation}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79251618712{\&}doi=10.1007{\%}2Fs00450-010-0130-4{\&}partnerID=40{\&}md5=63eb5e1f357ccd1387341c04ab3f7522},
volume = {26},
year = {2011}
}
@inproceedings{Wetzel,
abstract = {The detailed reconstruction of neural anatomy for connectomics studies requires a combination of resolution and large three-dimensional data capture provided by serial section electron microscopy (ssEM). The convergence of high throughput ssEM imaging and improved tissue preparation methods now allows ssEM capture of complete specimen volumes up to cubic millimeter scale. The resulting multi-terabyte image sets span thousands of serial sections and must be precisely registered into coherent volumetric forms in which neural circuits can be traced and segmented. This paper introduces a Signal Whitening Fourier Transform Image Registration approach (SWiFT-IR) under development at the Pittsburgh Supercomputing Center and its use to align mouse and zebrafish brain datasets acquired using the wafer mapper ssEM imaging technology recently developed at Harvard University. Unlike other methods now used for ssEM registration, SWiFT-IR modifies its spatial frequency response during image matching to maximize a signal-to-noise measure used as its primary indicator of alignment quality. This alignment signal is more robust to rapid variations in biological content and unavoidable data distortions than either phase-only or standard Pearson correlation, thus allowing more precise alignment and statistical confidence. These improvements in turn enable an iterative registration procedure based on projections through multiple sections rather than more typical adjacent-pair matching methods. This projection approach, when coupled with known anatomical constraints and iteratively applied in a multi-resolution pyramid fashion, drives the alignment into a smooth form that properly represents complex and widely varying anatomical content such as the full crosssection zebrafish data.},
archivePrefix = {arXiv},
arxivId = {1612.04787},
author = {Wetzel, Arthur W. and Bakal, Jennifer and Dittrich, Markus and Hildebrand, David G.C. and Morgan, Josh L. and Lichtman, Jeff W.},
booktitle = {Proceedings - Applied Imagery Pattern Recognition Workshop},
doi = {10.1109/AIPR.2016.8010595},
eprint = {1612.04787},
isbn = {9781509032846},
issn = {21642516},
keywords = {connectomics,electron microscopy,image registration,neural circuit reconstruction,signal whitening},
title = {{Registering large volume serial-section electron microscopy image sets for neural circuit reconstruction using FFT signal whitening}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028755924{\&}doi=10.1109{\%}2FAIPR.2016.8010595{\&}partnerID=40{\&}md5=688016e1bcc150f30e8230da73803097},
year = {2017}
}
@article{Whitmarsh2011,
abstract = {The accurate diagnosis of osteoporosis has gained increasing importance due to the aging of our society. Areal bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is an established criterion in the diagnosis of osteoporosis. This measure, however, is limited by its two-dimensionality. This work presents a method to reconstruct both the 3D bone shape and 3D BMD distribution of the proximal femur from a single DXA image used in clinical routine. A statistical model of the combined shape and BMD distribution is presented, together with a method for its construction from a set of quantitative computed tomography (QCT) scans. A reconstruction is acquired in an intensity based 3D-2D registration process whereby an instance of the model is found that maximizes the similarity between its projection and the DXA image. Reconstruction experiments were performed on the DXA images of 30 subjects, with a model constructed from a database of QCT scans of 85 subjects. The accuracy was evaluated by comparing the reconstructions with the same subject QCT scans. The method presented here can potentially improve the diagnosis of osteoporosis and fracture risk assessment from the low radiation dose and low cost DXA devices currently used in clinical routine. {\textcopyright} 2006 IEEE.},
author = {Whitmarsh, Tristan and Humbert, Ludovic and {De Craene}, Mathieu and {Del Rio Barquero}, Luis M. and Frangi, Alejandro F.},
doi = {10.1109/TMI.2011.2163074},
issn = {02780062},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Active appearance model,active shape model,deformable models,image reconstruction,image registration},
number = {12},
pages = {2101--2114},
title = {{Reconstructing the 3D shape and bone mineral density distribution of the proximal femur from dual-energy x-ray absorptiometry}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {30},
year = {2011}
}
@article{Wibmer2015,
abstract = {Objectives: To investigate Haralick texture analysis of prostate MRI for cancer detection and differentiating Gleason scores (GS). Methods: One hundred and forty-seven patients underwent T2- weighted (T2WI) and diffusion-weighted prostate MRI. Cancers ≥0.5 ml and non-cancerous peripheral (PZ) and transition (TZ) zone tissue were identified on T2WI and apparent diffusion coefficient (ADC) maps, using whole-mount pathology as reference. Texture features (Energy, Entropy, Correlation, Homogeneity, Inertia) were extracted and analysed using generalized estimating equations. Results: PZ cancers (n = 143) showed higher Entropy and Inertia and lower Energy, Correlation and Homogeneity compared to non-cancerous tissue on T2WI and ADC maps (p-values: {\textless}.0001–0.008). In TZ cancers (n = 43) we observed significant differences for all five texture features on the ADC map (all p-values: {\textless}.0001) and for Correlation (p = 0.041) and Inertia (p = 0.001) on T2WI. On ADC maps, GS was associated with higher Entropy (GS 6 vs. 7: p = 0.0225; 6 vs. {\textgreater}7: p = 0.0069) and lower Energy (GS 6 vs. 7: p = 0.0116, 6 vs. {\textgreater}7: p = 0.0039). ADC map Energy (p = 0.0102) and Entropy (p = 0.0019) were significantly different in GS ≤3 + 4 versus ≥4 + 3 cancers; ADC map Entropy remained significant after controlling for the median ADC (p = 0.0291). Conclusion: Several Haralick-based texture features appear useful for prostate cancer detection and GS assessment. Key Points: • Several Haralick texture features may differentiate non-cancerous and cancerous prostate tissue. • Tumour Energy and Entropy on ADC maps correlate with Gleason score. • T2w-image-derived texture features are not associated with the Gleason score.},
author = {Wibmer, Andreas and Hricak, Hedvig and Gondo, Tatsuo and Matsumoto, Kazuhiro and Veeraraghavan, Harini and Fehr, Duc and Zheng, Junting and Goldman, Debra and Moskowitz, Chaya and Fine, Samson W. and Reuter, Victor E. and Eastham, James and Sala, Evis and Vargas, Hebert Alberto},
doi = {10.1007/s00330-015-3701-8},
issn = {14321084},
journal = {European Radiology},
keywords = {Adenocarcinoma,Computer-assisted,Gleason grading,Image processing,Magnetic resonance imaging,Prostatic neoplasm},
number = {10},
pages = {2840--2850},
title = {{Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {25},
year = {2015}
}
@article{Wienert2013,
abstract = {Background: Automated image analysis methods are becoming more and more important to extract and quantify image features in microscopy-based biomedical studies and several commercial or open-source tools are available. However, most of the approaches rely on pixel-wise operations, a concept that has limitations when high-level object features and relationships between objects are studied and if user-interactivity on the object-level is desired.Results: In this paper we present an open-source software that facilitates the analysis of content features and object relationships by using objects as basic processing unit instead of individual pixels. Our approach enables also users without programming knowledge to compose " analysis pipelines" that exploit the object-level approach. We demonstrate the design and use of example pipelines for the immunohistochemistry-based cell proliferation quantification in breast cancer and two-photon fluorescence microscopy data about bone-osteoclast interaction, which underline the advantages of the object-based concept.Conclusions: We introduce an open source software system that offers object-based image analysis. The object-based concept allows for a straight-forward development of object-related interactive or fully automated image analysis solutions. The presented software may therefore serve as a basis for various applications in the field of digital image analysis. {\textcopyright} 2013 Wienert et al.; licensee BioMed Central Ltd.},
author = {Wienert, Stephan and Heim, Daniel and Kotani, Manato and Lindequist, Bj{\"{o}}rn and Stenzinger, Albrecht and Ishii, Masaru and Hufnagl, Peter and Beil, Michael and Dietel, Manfred and Denkert, Carsten and Klauschen, Frederick},
doi = {10.1186/1746-1596-8-34},
issn = {17461596},
journal = {Diagnostic Pathology},
keywords = {Image analysis,Object-based image analysis,Open source,Software},
number = {1},
pages = {8},
title = {{CognitionMaster: An object-based image analysis framework}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {8},
year = {2013}
}
@article{Wolfelschneider2017,
abstract = {Purpose: The aim of this study was to evaluate a surrogate-driven motion model based on fourdimensional computed tomography that is able to predict CT volumes corresponding to arbitrary respiratory phases. Furthermore, the comparison of three different driving surrogates is examined and the feasibility of using the model for 4D dose re-calculation will be discussed. Methods: The study is based on repeated 4DCTs of twenty patients treated for bronchial carcinoma and metastasis. The motion model was estimated from the planning 4DCT through deformable image registration. To predict a certain phase of a follow-up 4DCT, the model considers inter-fractional variations (baseline correction) and intra-fractional respiratory parameters (amplitude and phase) derived from surrogates. The estimated volumes resulting from the model were compared to ground-truth clinical 4DCTs using absolute HU differences in the lung region and landmarks localized using the Scale Invariant Feature Transform. Finally, the c-index was used to evaluate the dosimetric effects of the intensity differences measured between the estimated and the ground-truth CTvolumes. Results: The results show absolute HU differences between estimated and ground-truth images with median value (± standard deviation) of (61.3 ± 16.7) HU. Median 3D distances, measured on about 400 matching landmarks in each volume, were (2.9 ± 3.0) mm. 3D errors up to 28.2 mm were found for CT images with artifacts or reduced quality. Pass rates for all surrogate approaches were above 98.9{\%} with a c-criterion of 2{\%}/2 mm. Conclusion: The results depend mainly on the image quality of the initial 4DCT and the deformable image registration. All investigated surrogates can be used to estimate follow-up 4DCT phases, however, uncertainties decrease for volumetric approaches. Application of the model for 4D dose calculations is feasible.},
author = {Wolfelschneider, Jens and Seregni, Matteo and Fassi, Aurora and Ziegler, Marc and Baroni, Guido and Fietkau, Rainer and Riboldi, Marco and Bert, Christoph},
doi = {10.1002/mp.12243},
issn = {00942405},
journal = {Medical Physics},
keywords = {4D dose reconstruction,4DCT,Respiratory motion model,Scale invariant feature transform,Surrogates},
number = {6},
pages = {2066--2076},
title = {{Examination of a deformable motion model for respiratory movements and 4D dose calculations using different driving surrogates}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {44},
year = {2017}
}
@article{Wollny2013,
abstract = {Background: Gray scale images make the bulk of data in bio-medical image analysis, and hence, the main focus of many image processing tasks lies in the processing of these monochrome images. With ever improving acquisition devices, spatial and temporal image resolution increases, and data sets become very large. Various image processing frameworks exists that make the development of new algorithms easy by using high level programming languages or visual programming. These frameworks are also accessable to researchers that have no background or little in software development because they take care of otherwise complex tasks. Specifically, the management of working memory is taken care of automatically, usually at the price of requiring more it. As a result, processing large data sets with these tools becomes increasingly difficult on work station class computers. One alternative to using these high level processing tools is the development of new algorithms in a languages like C++, that gives the developer full control over how memory is handled, but the resulting workflow for the prototyping of new algorithms is rather time intensive, and also not appropriate for a researcher with little or no knowledge in software development. Another alternative is in using command line tools that run image processing tasks, use the hard disk to store intermediate results, and provide automation by using shell scripts. Although not as convenient as, e.g. visual programming, this approach is still accessable to researchers without a background in computer science. However, only few tools exist that provide this kind of processing interface, they are usually quite task specific, and don't provide an clear approach when one wants to shape a new command line tool from a prototype shell script.Results: The proposed framework, MIA, provides a combination of command line tools, plug-ins, and libraries that make it possible to run image processing tasks interactively in a command shell and to prototype by using the according shell scripting language. Since the hard disk becomes the temporal storage memory management is usually a non-issue in the prototyping phase. By using string-based descriptions for filters, optimizers, and the likes, the transition from shell scripts to full fledged programs implemented in C++ is also made easy. In addition, its design based on atomic plug-ins and single tasks command line tools makes it easy to extend MIA, usually without the requirement to touch or recompile existing code.Conclusion: In this article, we describe the general design of MIA, a general purpouse framework for gray scale image processing. We demonstrated the applicability of the software with example applications from three different research scenarios, namely motion compensation in myocardial perfusion imaging, the processing of high resolution image data that arises in virtual anthropology, and retrospective analysis of treatment outcome in orthognathic surgery. With MIA prototyping algorithms by using shell scripts that combine small, single-task command line tools is a viable alternative to the use of high level languages, an approach that is especially useful when large data sets need to be processed. {\textcopyright} 2013 Wollny et al.; licensee BioMed Central Ltd.},
author = {Wollny, Gert and Kellman, Peter and Ledesma-Carbayo, Mar{\'{i}}a Jesus and Skinner, Matthew M. and Hublin, Jean Jaques and Hierl, Thomas},
doi = {10.1186/1751-0473-8-20},
issn = {17510473},
journal = {Source Code for Biology and Medicine},
title = {{MIA - A free and open source software for gray scale medical image analysis}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885357120{\&}doi=10.1186{\%}2F1751-0473-8-20{\&}partnerID=40{\&}md5=67c49f93d1bf780334ae506f97a71ff2},
volume = {8},
year = {2013}
}
@article{Wollny2010,
abstract = {Free-breathing image acquisition is desirable in first-pass gadolinium-enhanced magnetic resonance imaging (MRI), but the breathing movements hinder the direct automatic analysis of the myocardial perfusion and qualitative readout by visual tracking. Nonrigid registration can be used to compensate for these movements but needs to deal with local contrast and intensity changes with time. We propose an automatic registration scheme that exploits the quasiperiodicity of free breathing to decouple movement from intensity change. First, we identify and register a subset of the images corresponding to the same phase of the breathing cycle. This registration step deals with small differences caused by movement but maintains the full range of intensity change. The remaining images are then registered to synthetic references that are created as a linear combination of images belonging to the already registered subset. Because of the quasiperiodic respiratory movement, the subset images are distributed evenly over time and, therefore, the synthetic references exhibit intensities similar to their corresponding unregistered images. Thus, this second registration step needs to account only for the movement. Validation experiments were performed on data obtained from six patients, three slices per patient, and the automatically obtained perfusion profiles were compared with profiles obtained by manually segmenting the myocardium. The results show that our automatic approach is well suited to compensate for the free-breathing movement and that it achieves a significant improvement in the average Pearson correlation coefficient between manually and automatically obtained perfusion profiles before (0.87±0.18) and after (0.96±0.09) registration. {\textcopyright} 2010 IEEE.},
author = {Wollny, Gert and Ledesma-Carbayo, Maria J. and Kellman, Peter and Santos, Andres},
doi = {10.1109/TMI.2010.2049270},
issn = {02780062},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Heart,image registration,myocardial perfusion},
number = {8},
pages = {1516--1527},
title = {{Exploiting quasiperiodicity in motion correction of free-breathing myocardial perfusion MRI}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {29},
year = {2010}
}
@article{Wu2012,
abstract = {True 3D CT dataset for treatment planning of an oversized patient is difficult to acquire due to the bore size and field of view (FOV) reconstruction. This project aims to provide a simple approach to reconstruct true CT data for oversize patients using CT scanner with limited FOV by acquiring double partial CT (left and right side) images. An efficient line profile-based method has been developed to minimize the difference of the CT numbers in the overlapping region between the right and left images and to generate a complete true 3D CT dataset in the natural state. New image processing modules have been developed and integrated to the Insight Segmentation {\&} Registration Toolkit (ITK 3.6) package. For example, different modules for image cropping, line profile generation, line profile matching, and optimized partial image fusion have been developed. The algorithm has been implemented for images containing the bony structure of the spine and tested on 3D CT planning datasets from both phantom and real patients with satisfactory results in both cases. The proposed optimized line profile-based partial registration method provides a simple and accurate method for acquiring a complete true 3D CT dataset for an oversized patient using CT scanning with small bore size, that can be used for accurate treatment planning.},
author = {Wu, Huanmei and Zhao, Qingya and Cao, Minsong and Das, Indra},
doi = {10.1120/jacmp.v13i2.3629},
issn = {15269914},
journal = {Journal of Applied Clinical Medical Physics},
keywords = {Double scanning,Oversized patients,Partial registration,Planning CT},
number = {2},
pages = {20--31},
title = {{A line profile-based double partial fusion method for acquiring planning CT of oversized patients in radiation treatment}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {13},
year = {2012}
}
@article{Wunderling2017,
abstract = {Secondary growth occurs in dicotyledons and gymnosperms, and results in an increased girth of plant organs. It is driven primarily by the vascular cambium, which produces thousands of cells throughout the life of several plant species. For instance, even in the small herbaceous model plant Arabidopsis, manual quantification of this massive process is impractical. Here, we provide a comprehensive overview of current methods used to measure radial growth. We discuss the issues and problematics related to its quantification. We highlight recent advances and tools developed for automated cellular phenotyping and its future applications.},
author = {Wunderling, Anna and Targem, Mehdi Ben and {De Reuille}, Pierre Barbier and Ragni, Laura and Turner, Simon},
doi = {10.1093/jxb/erw450},
issn = {14602431},
journal = {Journal of Experimental Botany},
keywords = {Arabidopsis,Automated cellular phenotyping,Machine learning,Quantitative histology,Secondary growth},
number = {1},
pages = {89--95},
title = {{Novel tools for quantifying secondary growth}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {68},
year = {2016}
}
@article{Xu2013,
abstract = {Purpose: To assess retrospectively the clinical accuracy of an magnetic resonance imaging-guided robotic prostate biopsy system that has been used in the US National Cancer Institute for over 6 years. Methods: Series of 2D transverse volumetric MR image slices of the prostate both pre (high-resolution T2-weighted)- and post (low-resolution)- needle insertions were used to evaluate biopsy accuracy. A three-stage registration algorithm consisting of an initial two-step rigid registration followed by a B-spline deformable alignment was developed to capture prostate motion during biopsy. The target displacement (distance between planned and actual biopsy target), needle placement error (distance from planned biopsy target to needle trajectory), and biopsy error (distance from actual biopsy target to needle trajectory) were calculated as accuracy assessment. Results: A total of 90 biopsies from 24 patients were studied. The registrations were validated by checking prostate contour alignment using image overlay, and the results were accurate to within 2 mm. The mean target displacement, needle placement error, and clinical biopsy error were 5.2, 2.5, and 4.3 mm, respectively. Conclusion: The biopsy error reported suggests that quantitative imaging techniques for prostate registration and motion compensation may improve prostate biopsy targeting accuracy. {\textcopyright} 2013 CARS.},
author = {Xu, Helen and Lasso, Andras and Guion, Peter and Krieger, Axel and Kaushal, Aradhana and Singh, Anurag K. and Pinto, Peter A. and Coleman, Jonathan and Grubb, Robert L. and Lattouf, Jean Baptiste and Menard, Cynthia and Whitcomb, Louis L. and Fichtinger, Gabor},
doi = {10.1007/s11548-013-0831-9},
issn = {18616429},
journal = {International Journal of Computer Assisted Radiology and Surgery},
keywords = {Accuracy validation,Image registration,MRI-guidance,Prostate biopsy},
number = {6},
pages = {937--944},
title = {{Accuracy analysis in MRI-guided robotic prostate biopsy}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {8},
year = {2013}
}
@article{Xu2015,
abstract = {Purpose: Prostate needle biopsy is a commonly performed procedure since it is the most definitive form of cancer diagnosis. Magnetic resonance imaging (MRI) allows target-specific biopsies to be performed. However, needle placements are often inaccurate due to intra-operative prostate motion and the lack of motion compensation techniques. This paper detects and determines the extent of tissue displacement during an MRI-guided biopsy so that the needle insertion plan can be adjusted accordingly. Methods: A multi-slice-to-volume registration algorithm was developed to align the pre-operative planning image volume with three intra-operative orthogonal image slices of the prostate acquired immediately before needle insertion. The algorithm consists of an initial rigid transformation followed by a deformable step. Results: A total of 14 image sets from 10 patients were studied. Based on prostate contour alignment, the registrations were accurate to within 2 mm. Conclusion: This algorithm can be used to increase the needle targeting accuracy by alerting the clinician if the biopsy target has moved significantly prior to needle insertion. The proposed method demonstrated feasibility of intra-operative target localization and motion compensation for MRI-guided prostate biopsy.},
author = {Xu, Helen and Lasso, Andras and Fedorov, Andriy and Tuncali, Kemal and Tempany, Clare and Fichtinger, Gabor},
doi = {10.1007/s11548-014-1108-7},
issn = {18616429},
journal = {International Journal of Computer Assisted Radiology and Surgery},
keywords = {Image registration,MRI-guidance,Prostate biopsy,Target localization},
number = {5},
pages = {563--572},
title = {{Multi-slice-to-volume registration for MRI-guided transperineal prostate biopsy}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {10},
year = {2015}
}
@article{Xu2015a,
abstract = {Structural abnormalities of the microvasculature can impair perfusion and function. Conventional histology provides good spatial resolution with which to evaluate the microvascular structure but affords no 3-dimensional information; this limitation could lead to misinterpretations of the complex microvessel network in health and disease. The objective of this study was to develop and evaluate an accurate, fully automated 3D histology reconstruction method to visualize the arterioles and venules within the mouse hind-limb. Sections of the tibialis anterior muscle from C57BL/J6 mice (both normal and subjected to femoral artery excision) were reconstructed using pairwise rigid and affine registrations of 5 $\mu$m-thick, paraffin-embedded serial sections digitized at 0.25 $\mu$m/pixel. Low-resolution intensity-based rigid registration was used to initialize the nucleus landmark-based registration, and conventional high-resolution intensity-based registration method. The affine nucleus landmark-based registration was developed in this work and was compared to the conventional affine high-resolution intensity-based registration method. Target registration errors were measured between adjacent tissue sections (pairwise error), as well as with respect to a 3D reference reconstruction (accumulated error, to capture propagation of error through the stack of sections). Accumulated error measures were lower (p{\textless}0.01) for the nucleus landmark technique and superior vasculature continuity was observed. These findings indicate that registration based on automatic extraction and correspondence of small, homologous landmarks may support accurate 3D histology reconstruction. This technique avoids the otherwise problematic "banana-into-cylinder" effect observed using conventional methods that optimize the pairwise alignment of salient structures, forcing them to be section-orthogonal. This approach will provide a valuable tool for high-accuracy 3D histology tissue reconstructions for analysis of diseased microvasculature.},
author = {Xu, Yiwen and Pickering, J. Geoffrey and Nong, Zengxuan and Gibson, Eli and Arpino, John Michael and Yin, Hao and Ward, Aaron D.},
doi = {10.1371/journal.pone.0126817},
issn = {19326203},
journal = {PLoS ONE},
number = {5},
pages = {24},
title = {{A method for 3D histopathology reconstruction supporting mouse microvasculature analysis}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {10},
year = {2015}
}
@article{Yan2018,
abstract = {Bioprinted skin tissue has the potential for aiding drug screening, formulation development, clinical transplantation, chemical and cosmetic testing, as well as basic research. Limitations of conventional skin tissue engineering approaches have driven the development of biomimetic skin equivalent via 3D bioprinting. A key hope for bioprinting skin is the improved tissue authenticity over conventional skin equivalent construction, enabling the precise localization of multiple cell types and appendages within a construct. The printing of skin faces challenges broadly associated with general 3D bioprinting, including the selection of cell types and biomaterials, and additionally requires in vitro culture formats that allow for growth at an air-liquid interface. This paper provides a thorough review of current 3D bioprinting technologies used to engineer human skin constructs and presents the overall pipelines of designing a biomimetic artificial skin via 3D bioprinting from the design phase (i.e. pre-processing phase) through the tissue maturation phase (i.e. post-processing) and into final product evaluation for drug screening, development, and drug delivery applications.},
author = {Yan, Wei Cheng and Davoodi, Pooya and Vijayavenkataraman, Sanjairaj and Tian, Yuan and Ng, Wei Cheng and Fuh, Jerry Y.H. and Robinson, Kim Samirah and Wang, Chi Hwa},
doi = {10.1016/j.addr.2018.07.016},
issn = {18728294},
journal = {Advanced Drug Delivery Reviews},
keywords = {3D bioprinting,Artificial skin,Skin tissue engineering,Tissue engineering},
pages = {270--295},
title = {{3D bioprinting of skin tissue: From pre-processing to final product evaluation}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {132},
year = {2018}
}
@article{Yang2015,
abstract = {Purpose: Accurate visualization of lung motion is important in many clinical applications, such as radiotherapy of lung cancer. Advancement in imaging modalities [e.g., computed tomography (CT) and MRI] has allowed dynamic imaging of lung and lung tumor motion. However, each imaging modality has its advantages and disadvantages. The study presented in this paper aims at generating synthetic 4D-CT dataset for lung cancer patients by combining both continuous three-dimensional (3D) motion captured by 4D-MRI and the high spatial resolution captured by CT using the author's proposed approach.Methods: A novel hybrid approach based on deformable image registration (DIR) and finite element method simulation was developed to fuse a static 3D-CT volume (acquired under breath-hold) and the 3D motion information extracted from 4D-MRI dataset, creating a synthetic 4D-CT dataset. Results: The study focuses on imaging of lung and lung tumor. Comparing the synthetic 4D-CT dataset with the acquired 4D-CT dataset of six lung cancer patients based on 420 landmarks, accurate results (average error {\textless}2 mm) were achieved using the authors' proposed approach. Their hybrid approach achieved a 40{\%} error reduction (based on landmarks assessment) over using only DIR techniques. Conclusions: The synthetic 4D-CT dataset generated has high spatial resolution, has excellent lung details, and is able to show movement of lung and lung tumor over multiple breathing cycles.},
author = {Yang, Y. X. and Teo, S. K. and {Van Reeth}, E. and Tan, C. H. and Tham, I. W.K. and Poh, C. L.},
doi = {10.1118/1.4923167},
issn = {00942405},
journal = {Medical Physics},
keywords = {DIR,FEM,image fusion,lung cancer,radiotherapy},
number = {8},
pages = {4484--4496},
title = {{A hybrid approach for fusing 4D-MRI temporal information with 3D-CT for the study of lung and lung tumor motion}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {42},
year = {2015}
}
@article{Yin2006,
abstract = {Oligometastases refer to metastases that are limited in number and location and are amenable to regional treatment. The majority of these metastases appear in the brain, lung, liver, and bone. Although the focus of interest in the past within radiation oncology has been on the treatment of intracranial metastases, there has been growing interest in extracranial sites such as the liver and lung. This is largely because of the rapid development of targeting techniques for oligometastases such as intensity-modulated and image-guided radiation therapy, which has made it possible to deliver single or a few fractions of high-dose radiation treatments, highly conformal to the target. The clinical decision to use radiation to treat oligometastases is based on both radiobiological and physics considerations. The radiobiological considerations involve improvement of treatment schema for time, dose, and volume. Areas of interests are hypofractionation, tumor and normal tissue tolerance, and hypoxia. The physics considerations for oligometastases treatment are focused mainly on ensuring treatment accuracy and precision. This article discusses the physics and imaging aspects involved in each step of the radiation treatment process for oligometastases, including target definition, treatment simulation, treatment planning, pretreatment target localization, radiation delivery, treatment verification, and treatment evaluation. {\textcopyright} 2006 Elsevier Inc. All rights reserved.},
author = {Yin, Fang Fang and Das, Shiva and Kirkpatrick, John and Oldham, Mark and Wang, Zhiheng and Zhou, Su Min},
doi = {10.1016/j.semradonc.2005.12.004},
issn = {10534296},
journal = {Seminars in Radiation Oncology},
number = {2},
pages = {85--101},
title = {{Physics and imaging for targeting of oligometastases}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33645088409{\&}doi=10.1016{\%}2Fj.semradonc.2005.12.004{\&}partnerID=40{\&}md5=1274c24db6658fb341f2b0b1f475f7ce},
volume = {16},
year = {2006}
}
@article{Zaffino2016,
abstract = {Purpose: Multiatlas based segmentation is largely used in many clinical and research applications. Due to its good performances, it has recently been included in some commercial platforms for radiotherapy planning and surgery guidance. Anyway, to date, a software with no restrictions about the anatomical district and image modality is still missing. In this paper we introduce PLASTIMATCH MABS, an open source software that can be used with any image modality for automatic segmentation. Methods: PLASTIMATCH MABS workflow consists of two main parts: (1) an offline phase, where optimal registration and voting parameters are tuned and (2) an online phase, where a new patient is labeled from scratch by using the same parameters as identified in the former phase. Several registration strategies, as well as different voting criteria can be selected. A flexible atlas selection scheme is also available. To prove the effectiveness of the proposed software across anatomical districts and image modalities, it was tested on two very different scenarios: head and neck (H{\&}N) CT segmentation for radiotherapy application, and magnetic resonance image brain labeling for neuroscience investigation. Results: For the neurological study, minimum dice was equal to 0.76 (investigated structures: left and right caudate, putamen, thalamus, and hippocampus). For head and neck case, minimum dice was 0.42 for the most challenging structures (optic nerves and submandibular glands) and 0.62 for the other ones (mandible, brainstem, and parotid glands). Time required to obtain the labels was compatible with a real clinical workflow (35 and 120 min). Conclusions: The proposed software fills a gap in the multiatlas based segmentation field, since all currently available tools (both for commercial and for research purposes) are restricted to a well specified application. Furthermore, it can be adopted as a platform for exploring MABS parameters and as a reference implementation for comparing against other segmentation algorithms.},
author = {Zaffino, Paolo and Raudaschl, Patrik and Fritscher, Karl and Sharp, Gregory C. and Spadea, Maria Francesca},
doi = {10.1118/1.4961121},
issn = {00942405},
journal = {Medical Physics},
keywords = {CT,MRI,automatic segmentation,multiatlas based segmentation,open source},
number = {9},
pages = {5155--5160},
title = {{Technical Note: Plastimatch mabs, an open source tool for automatic image segmentation}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {43},
year = {2016}
}
@article{Zhang2019,
abstract = {Purpose: Respiratory gated four-dimensional (4D) single photon emission computed tomography (SPECT) with phase-matched CT reduces respiratory blurring and attenuation correction (AC) artifacts in cardiac SPECT. This study aims to develop and investigate the effectiveness of an interpolated CT (ICT) method for improved cardiac SPECT AC using simulations. Methods: We used the 4D XCAT phantom to simulate a population of ten patients varied in gender, anatomy, 99mTc-sestamibi distribution, respiratory patterns, and disease states. Simulated 120 SPECT projection data were rebinned into six equal count gates. Activity and attenuation maps in each gate were averaged as gated SPECT and CT (GCT). Three helical CTs were simulated at end-inspiration (HCT-IN), end-expiration (HCT-EX), and mid-respiration (HCT-MID). The ICTs were obtained from HCT-EX and HCT-IN using the motion vector field generated between them from affine plus b-spline registration. Projections were reconstructed by OS-EM method, using GCT, ICT, and three HCTs for AC. Reconstructed images of each gate were registered to end-expiration and averaged to generate the polar plots. Relative difference for each segment and relative defect size were computed using images of GCT AC as reference. Results: The average of maximum relative difference through ten phantoms was 7.93 ± 4.71{\%}, 2.50 ± 0.98{\%}, 3.58 ± 0.74{\%}, and 2.14 ± 0.56{\%} for noisy HCT-IN, HCT-MID, HCT-EX, and ICT AC data, respectively. The ICT showed closest defect size to GCT while the differences from HCTs can be over 40{\%}. Conclusion: We conclude that the performance of ICT is similar to GCT. It improves the image quality and quantitative accuracy for respiratory-gated cardiac SPECT as compared to conventional HCT, while it can potentially further reduce the radiation dose of GCT.},
author = {Zhang, Duo and Ghaly, Michael and Mok, Greta S.P.},
doi = {10.1002/mp.13513},
issn = {00942405},
journal = {Medical Physics},
keywords = {attenuation correction,cardiac SPECT/CT,respiratory gating},
number = {6},
pages = {2621--2628},
title = {{Interpolated CT for attenuation correction on respiratory gating cardiac SPECT/CT — A simulation study}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {46},
year = {2019}
}
@article{Zhang2018,
abstract = {Background: Respiratory gating reduces motion blurring in cardiac SPECT. Here we aim to evaluate the performance of three respiratory gating strategies using a population of digital phantoms with known truth and clinical data. Methods: We analytically simulated 60 projections for 10 XCAT phantoms with 99mTc-sestamibi distributions using three gating schemes: equal amplitude gating (AG), equal count gating (CG), and equal time gating (TG). Clinical list-mode data for 10 patients who underwent 99mTc-sestamibi scans were also processed using the 3 gating schemes. Reconstructed images in each gate were registered to a reference gate, averaged and reoriented to generate the polar plots. For simulations, image noise, relative difference (RD) of averaged count for each of the 17 segment, and relative defect size difference (RSD) were analyzed. For clinical data, image intensity profile and FWHM were measured across the left ventricle wall. Results: For simulations, AG and CG methods showed significantly lower RD and RSD compared to TG, while noise variation was more non-uniform through different gates for AG. In the clinical study, AG and CG had smaller FWHM than TG. Conclusions: AG and CG methods show better performance for motion reduction and are recommended for clinical respiratory gating SPECT implementation.},
author = {Zhang, Duo and Pretorius, P. Hendrik and Ghaly, Michael and Zhang, Qi and King, Michael A. and Mok, Greta S.P.},
doi = {10.1007/s12350-018-1392-7},
issn = {15326551},
journal = {Journal of Nuclear Cardiology},
keywords = {Cardiac perfusion,Respiratory gating,SPECT/CT,Simulation},
title = {{Evaluation of different respiratory gating schemes for cardiac SPECT}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051860976{\&}doi=10.1007{\%}2Fs12350-018-1392-7{\&}partnerID=40{\&}md5=58fd21a00fccc01830e89c4949b3bcee},
year = {2018}
}
@article{Zheng2012,
abstract = {The longitudinal coverage of a LINAC-mounted CBCT scan is limited to the corresponding dimensional limits of its flat panel detector, which is often shorter than the length of the treatment field. These limits become apparent when fields are designed to encompass wide regions, as when providing nodal coverage. Therefore, we developed a novel protocol to acquire double orbit CBCT images using a commercial system, and combine the images to extend the longitudinal coverage for image-guided adaptive radiotherapy (IGART). The protocol acquires two CBCT scans with a couch shift similar to the "step-and-shoot" cine CT acquisition, allowing a small longitudinal overlap of the two reconstructed volumes. An in-house DICOM reading/writing software was developed to combine the two image sets into one. Three different approaches were explored to handle the possible misalignment between the two image subsets: simple stacking, averaging the overlapped volumes, and a 3D-3D image registration with the three translational degrees of freedom. Using thermoluminescent dosimeters and custom-designed holders for a CTDI phantom set, dose measurements were carried out to assess the resultant imaging dose of the technique and its geometric distribution. Deformable registration was tested on patient images generated with the double-orbit protocol, using both the planning FBCT and the artificially deformed CBCT as source images. The protocol was validated on phantoms and has been employed clinically for IRB-approved IGART studies for head and neck and prostate cancer patients.},
author = {Zheng, Dandan and Lu, Jun and Jefferson, Ariel and Zhang, Cheng and Wu, Jian and Sleeman, William and Weiss, Elisabeth and Dogan, Nesrin and Song, Shiyu and Williamson, Jeffrey},
doi = {10.1120/jacmp.v13i4.3796},
issn = {15269914},
journal = {Journal of Applied Clinical Medical Physics},
keywords = {Cbct,Field of view,Igart,Image registration},
number = {4},
pages = {141--151},
title = {{A protocol to extend the longitudinal coverage of on-board cone-beam CT}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {13},
year = {2012}
}
@article{Zhuge2006,
abstract = {We present a system for segmenting the human aortic aneurysm in CT angiograms (CTA), which, in turn, allows measurements of volume and morphological aspects useful for treatment planning. The system estimates a rough "initial surface," and then refines it using a level set segmentation scheme augmented with two external analyzers: The global region analyzer, which incorporates a priori knowledge of the intensity, volume, and shape of the aorta and other structures, and the local feature analyzer, which uses voxel location, intensity, and texture features to train and drive a support vector machine classifier. Each analyzer outputs a value that corresponds to the likelihood that a given voxel is part of the aneurysm, which is used during level set iteration to control the evolution of the surface. We tested our system using a database of 20 CTA scans of patients with aortic aneurysms. The mean and worst case values of volume overlap, volume error, mean distance error, and maximum distance error relative to human tracing were 95.3{\%}±1.4{\%} (s.d.); worst case=92.9{\%}, 3.5{\%}±2.5{\%} (s.d.); worst case=7.0{\%}, 0.6±0.2 mm (s.d.); worst case=1.0 mm, and 5.2±2.3mm (s.d.); worstcase=9.6 mm, respectively. When implemented on a 2.8 GHz Pentium IV personal computer, the mean time required for segmentation was 7.4±3.6min (s.d.). We also performed experiments that suggest that our method is insensitive to parameter changes within 10{\%} of their experimentally determined values. This preliminary study proves feasibility for an accurate, precise, and robust system for segmentation of the abdominal aneurysm from CTA data, and may be of benefit to patients with aortic aneurysms. {\textcopyright} 2006 American Association of Physicists in Medicine.},
author = {Zhuge, Feng and Rubin, Geoffrey D. and Sun, Shaohua and Napel, Sandy},
doi = {10.1118/1.2193247},
issn = {00942405},
journal = {Medical Physics},
keywords = {Abdominal aortic aneurysm,CT angiography,Deformable model},
number = {5},
pages = {1440--1453},
title = {{An abdominal aortic aneurysm segmentation method: Level set with region and statistical information}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33646440020{\&}doi=10.1118{\%}2F1.2193247{\&}partnerID=40{\&}md5=b99c7457d5ae6ae677679f84b3f61548},
volume = {33},
year = {2006}
}
@article{Ziegeler2012,
abstract = {A common problem with modern numerical oceanographic models is spatial displacement, including misplacement and misshapenness of ocean circulation features. Traditional error metrics, such as least squares methods, are ineffective in many such cases; for example, only small errors in the location of a frontal pattern are translated to large differences in least squares of intensities. Such problems are common in meteorological forecast verification as well, so the application of spatial error metrics have been a recently popular topic there. Spatial error metrics separate model error into a displacement component and an intensity component, providing a more reliable assessment of model biases and a more descriptive portrayal of numerical model prediction skill. The application of spatial error metrics to oceanographic models has been sparse, and further advances for both meteorology and oceanography exist in the medical imaging field. These advances are presented, along with modifications necessary for oceanographic model output. Standard methods and options for those methods in the literature are explored, and where the best arrangements of options are unclear, comparison studies are conducted. These trials require the reproduction of synthetic displacements in conjunction with synthetic intensity perturbations across 480 Navy Coastal Ocean Model (NCOM) temperature fields from various regions of the globe throughout 2009. Study results revealed the success of certain approaches novel to both meteorology and oceanography, including B-spline transforms and mutual information. That, combined with other common methods, such as quasi-Newton optimization and land masking, could best recover the synthetic displacements under various synthetic intensity changes. {\textcopyright} 2012 American Meteorological Society.},
author = {Ziegeler, Sean B. and Dykes, James D. and Shriver, Jay F.},
doi = {10.1175/JTECH-D-11-00109.1},
issn = {07390572},
journal = {Journal of Atmospheric and Oceanic Technology},
keywords = {Error analysis,Forecast verification,Ocean models},
number = {2},
pages = {260--266},
title = {{Spatial error metrics for oceanographic model verification}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {29},
year = {2012}
}
@article{Ziegler2019,
abstract = {The Vero system can treat intra-fractionally moving tumors with gimbaled dynamic tumor tracking (DTT) by rotating the treatment beam so that it follows the motion of the tumor. However, the changes in the beam geometry and the constant breathing motion of the patient influence the dose applied to the patient. This study aims to perform a full 4D dose reconstruction for thirteen patients treated with DTT at the Vero system at the Universit{\"{a}}tsklinikum Erlangen and investigates the temporal resolution required to perform an accurate 4D dose reconstruction. For all patients, a 4DCT was used to train a 4D motion model, which is able to calculate pseudo-CT images for arbitrary breathing phases. A new CT image was calculated for every 100 ms of treatment and a dose calculation was performed according to the current beam geometry (i.e. the rotation of the treatment beam at this moment in time) by rotating according to the momentary beam rotation, which is extracted from log-files. The resulting dose distributions were accumulated on the planning CT and characteristic parameters were extracted and compared. $\gamma$-evaluations of dose accumulations with different spatialoral resolutions were performed to determine the minimal required resolution. In total 173 700 dose calculations were performed. The accumulated 4D dose distributions show a reduced mean GTV dose of 0.77{\%} compared to the static treatment plan. For some patients larger deviations were observed, especially in the presence of a poor 4DCT quality. The $\gamma$-evaluation showed that a temporal resolution of 500 ms is sufficient for an accurate dose reconstruction. If the tumor motion is regarded as well, a spatialoral sampling of 1400 ms and 2 mm yields accurate results, which reduces the workload by 84{\%}.},
author = {Ziegler, Marc and Brandt, Tobias and Lettmaier, Sebastian and Fietkau, Rainer and Bert, Christoph},
doi = {10.1088/1361-6560/ab4e51},
issn = {13616560},
journal = {Physics in Medicine and Biology},
keywords = {4D dose calculation,Tumor tracking,motion modelling,real-time monitoring,stereotactic body radiation therapy},
number = {22},
pages = {12},
title = {{Method for a motion model based automated 4D dose calculation}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {64},
year = {2019}
}
@article{Ziegler2019a,
abstract = {Purpose: With the introduction of dynamic tumor tracking in radiotherapy, it is possible to irradiate moving targets with minimal safety margins. However, most dynamic tumor tracking techniques rely on changing the beam geometry by, for example, adapting the multileaf collimator (MLC) positions or rotating the LINAC head. These changes are relative to a reference position which is determined by a specific breathing phase. Since these changes in the beam path also influence the delivered dose, choosing a different reference position based on a different breathing phase impacts the applied dose to the patient. This work investigates the influence of choosing different reference breathing phases on the dose distribution. Methods: The Vero system tracks the moving target by performing a pan and tilt rotation of the LINAC head. For 13 patients, the target position was extracted from every phase of a four-dimensional computed tomography (4DCT) and the pan and tilt values were determined with respect to three different reference phases. These reference phases were inspiration, expiration, and the midventilation. For all reference phases, a 4D dose calculation was performed on the 4DCT regarding the respective pan and tilt values. Furthermore, the applied dose to the target and surrounding organs at risk was calculated. To accumulate the dose distribution, weights from the actual patient breathing motion were determined. The weights were calculated from the breathing motions from different days to investigate the impact of daily variations in the breathing motion onto the accumulated dose distribution. All obtained values were then compared to the static treatment plan. Results: The mean and maximum doses applied to the target or surrounding organs at risk show no general behavior depending on the different reference phases. Nevertheless, for some patients, large differences (approx. 30{\%}) in the applied dose to certain organs at risk could be observed, whereas the applied dose to the target shows no dependency on the different reference phases. However, the mean target dose is in all cases approx. 1.5{\%} below the reference value from the static treatment plan. Conclusion: Although no general dependency of the applied dose on the selected reference phase could be found, the choice of the reference phase can have great impact on the organ at risk dose for some patients. Thus, the choice of the reference phase used for patient positioning should be considered during treatment planning since it can be seen as a new degree of freedom of a treatment based on tracking.},
author = {Ziegler, Marc and Lettmaier, Sebastian and Fietkau, Rainer and Bert, Christoph},
doi = {10.1002/mp.13654},
issn = {00942405},
journal = {Medical Physics},
keywords = {4D dose calculation,dynamic tumor tracking,liver cancer,lung cancer},
number = {8},
pages = {3371--3377},
title = {{Choosing a reference phase for a dynamic tumor tracking treatment: A new degree of freedom?}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {46},
year = {2019}
}
@article{Zukic2018,
abstract = {Multicenter clinical trials that use positron emission tomography (PET) imaging frequently rely on stable bias in imaging biomarkers to assess drug effectiveness. Many well-documented factors cause variability in PET intensity values. Two of the largest scanner-dependent errors are scanner calibration and reconstructed image resolution variations. For clinical trials, an increase in measurement error significantly increases the number of patient scans needed. We aim to provide a robust quality assurance system using portable PET/computed tomography "pocket" phantoms and automated image analysis algorithms with the goal of reducing PET measurement variability. A set of the "pocket" phantoms was scanned with patients, affixed to the underside of a patient bed. Our software analyzed the obtained images and estimated the image parameters. The analysis consisted of 2 steps, automated phantom detection and estimation of PET image resolution and global bias. Performance of the algorithm was tested under variations in image bias, resolution, noise, and errors in the expected sphere size. A web-based application was implemented to deploy the image analysis pipeline in a cloud-based infrastructure to support multicenter data acquisition, under Software-as-a-Service (SaaS) model. The automated detection algorithm localized the phantom reliably. Simulation results showed stable behavior when image properties and input parameters were varied. The PET "pocket" phantom has the potential to reduce and/or check for standardized uptake value measurement errors.},
author = {Zuki{\'{c}}, D{\v{z}}enan and Byrd, Darrin W. and Kinahan, Paul E. and Enquobahrie, Andinet},
doi = {10.18383/j.tom.2018.00020},
issn = {2379139X},
journal = {Tomography (Ann Arbor, Mich.)},
keywords = {PET imaging,bias,calibration,correction,phantom},
number = {3},
pages = {148--158},
title = {{Calibration Software for Quantitative PET/CT Imaging Using Pocket Phantoms}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {4},
year = {2018}
}
@article{Willfuhr2015,
abstract = {The lung parenchyma provides a maximal surface area of blood-containing capillaries that are in close contact with a large surface areaof the air-containing alveoli. Volume and surface area of capillaries are the classic stereological parameters to characterize the alveolar capillary network (ACN) and have provided essential structure-function information of the lung. When loss (rarefaction) or gain (angiogenesis) of capillaries occurs, these parameters may not be sufficient to provide mechanistic insight. Therefore, it would be desirable to estimate the number of capillaries, as it contains more distinct and mechanistically oriented information. Here, we present a new stereological method to estimate the number of capillary loops in the ACN. One advantage of this method is that it is independent of the shape, size, or distribution of the capillaries. We used consecutive, 1 {\_}mthick sections from epoxy resin-embedded material as a physical disector. The Euler-Poincar{\'{e}} characteristic of capillary networks can be estimated by counting the easily recognizable topolog ical constellations of “islands,” “bridges,” and “holes.” The total number of capillary loops in the ACN can then be calculated from the Euler- Poincar{\'{e}} characteristic. With the use of the established estimator of alveolar number, it is possible to obtain the mean number of capillary loops per alveolus. In conclusion, estimation of alveolar capillaries by design-based stereology is an efficient and unbiased method to characterize the ACN and may be particularly useful for studies on emphysema, pulmonary hypertension, or lung development.},
author = {Willf{\"{u}}hr, Alper and Brandenberger, Christina and Piatkowski, Tanja and Grothausmann, Roman and Nyengaard, Jens Randel and Ochs, Matthias and M{\"{u}}hlfeld, Christian},
doi = {10.1152/ajplung.00410.2014},
issn = {15221504},
journal = {American Journal of Physiology - Lung Cellular and Molecular Physiology},
keywords = {Capillary number,Euler number,Stereology},
number = {11},
pages = {L1286--L1293},
title = {{Estimation of the number of alveolar capillaries by the euler number (Euler-poincar{\'{e}} characteristic)}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {309},
year = {2015}
}
@inbook{Alaytsev2018,
abstract = {Computed tomography is a great source of biomedical data because it allows a detailed exploration of complex anatomical structures. Some structures are not visible on CT scans, and some are hard to distinguish due to partial volume effect. CT datasets require preprocessing before using them as anatomical models in a simulation system. The work describes segmentation and data transformation methods for an anatomical model creation from the CT data. The result models may be used for visual and haptic rendering and drilling simulation in a virtual surgery system. {\textcopyright} 2018 SPIE.},
address = {Bellingham},
author = {Danilova, Tatyana V. and Manturov, Alexey O. and Mareev, Gleb O. and Mareev, Oleg V. and Alaytsev, Innokentiy K.},
booktitle = {Saratov Fall Meeting 2017: Laser Physics and Photonics Xviii; and Computational Biophysics and Analysis of Biomedical Data Iv},
doi = {10.1117/12.2309318},
editor = {Derbov, V L and Postnov, D E},
isbn = {9781510620032},
issn = {16057422},
pages = {18},
publisher = {Spie-Int Soc Optical Engineering},
series = {Proceedings of SPIE},
title = {{Creation of anatomical models from CT data}},
type = {Book Section},
url = {{\%}3CGo to},
volume = {10717},
year = {2018}
}
@inbook{Alic2010,
abstract = {Spatial correspondence between histology and multi sequence MRI can provide information about the capabilities of non-invasive imaging to characterize cancerous tissue. However, shrinkage and deformation occurring during the excision of the tumor and the histological processing complicate the co registration of MR images with histological sections. This work proposes a methodology to establish a detailed 3D relation between histology sections and in vivo MRI tumor data. The key features of the methodology are a very dense histological sampling (up to 100 histology slices per tumor), mutual information based non-rigid B-spline registration, the utilization of the whole 3D data sets, and the exploitation of an intermediate ex vivo MRI. In this proof of concept paper, the methodology was applied to one tumor. We found that, after registration, the visual alignment of tumor borders and internal structures was fairly accurate. Utilizing the intermediate ex vivo MRI, it was possible to account for changes caused by the excision of the tumor: we observed a tumor expansion of 20{\%}. Also the effects of fixation, dehydration and histological sectioning could be determined: 26{\%} shrinkage of the tumor was found. The annotation of viable tissue, performed in histology and transformed to the in vivo MRI, matched clearly with high intensity regions in MRI. With this methodology, histological annotation can be directly related to the corresponding in vivo MRI. This is a vital step for the evaluation of the feasibility of multi-spectral MRI to depict histological ground-truth. {\textcopyright} 2010 Copyright SPIE - The International Society for Optical Engineering.},
address = {Bellingham},
author = {Alic, Lejla and Haeck, Joost C. and Klein, Stefan and Bol, Karin and van Tiel, Sandra T. and Wielopolski, Piotr A. and Bijster, Magda and Niessen, Wiro J. and Bernsen, Monique and Veenland, Jifke F. and de Jong, Marion},
booktitle = {Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging},
doi = {10.1117/12.844123},
editor = {Molthen, R C and Weaver, J B},
isbn = {9780819480279},
issn = {16057422},
pages = {762603},
publisher = {Spie-Int Soc Optical Engineering},
series = {Proceedings of SPIE},
title = {{Multi-modal image registration: matching MRI with histology}},
type = {Book Section},
url = {{\%}3CGo to},
volume = {7626},
year = {2010}
}
@article{Bieri2013,
abstract = {Purpose The speed limit for three-dimensional Fourier-encoded steady state free precession (SSFP) imaging is explored on a clinical whole body system and pushed toward a pulse repetition time (TR) close to or even below the 1 ms regime; in the following referred to as ultra-fast SSFP imaging. Methods To this end, contemporary optimization strategies, such as efficient gradient switching patterns, partial echoes, ramp sampling techniques, and a target-related design of excitation pulses were applied to explore the lower boundaries in TR for SSFP-based Cartesian imaging. Results Generally, minimal TR was limited in vivo by peripheral nerve stimulation, allowing a TR ∼1 ms for isotropic resolutions down

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