text:
14,000 mm3, 21,000 mm3, 28,000 mm3 and 35,000 mm3), which constituted the gold standards. The sound phantom and the phantom filled with different volumes of water were scanned in a Picasso Trio CBCT unit set at 80 kVp, 3.7 mA, 0.2 mm3 voxel and 12 × 8.5 cm field of view. Semi-automatic segmentation was performed with ITK-SNAP 3.0 software by two trained oral radiologists. Linear regression analyzed the relation between ITK-SNAP calculated volumes and the gold standard. Intraclass correlation coefficient was applied to analyze the reproducibility of the method. Significance level was set at 5{\%}. Results: Linear regression analysis showed a significant relationship between ITK-SNAP volumes and the gold standard (F = 22,537.3, p {\textless} 0.0001), with an R2 of 0.9993. The average error found was 4.7 (± 4.3) {\%}. To minimize this error, a mathematical model was developed and provided a reduction of it. ICC revealed excellent intra-examiner agreements for both examiners 1 (ICC = 0.9991, p {\textless} 0.0001) and 2 (ICC = 0.9989, p {\textless} 0.0001). Likewise, inter-examiner agreement was excellent (ICC = 0.9991, p {\textless} 0.0001). Conclusion: The software showed to be accurate for evaluating non-regular shape structures. The mathematical model developed reduced an already small error on the software's measurements.},
author = {Gomes, Amanda Farias and Brasil, Danieli Moura and Silva, Amaro Il{\'{i}}dio Vespasiano and Freitas, Deborah Queiroz and Haiter-Neto, Francisco and Groppo, Francisco Carlos},
doi = {10.1007/s11282-019-00397-y},
issn = {16139674},
journal = {Oral Radiology},
keywords = {Cone beam computed tomography,Cross-sectional anatomy,Software,Three-dimensional imaging},
title = {{Accuracy of ITK-SNAP software for 3D analysis of a non-regular topography structure}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068856165{\&}doi=10.1007{\%}2Fs11282-019-00397-y{\&}partnerID=40{\&}md5=951d995522aefad42b00463ceb33e155},
year = {2019}
}
@article{RN812,
abstract = {The deposition of light-absorbing particles (LAPs) such as mineral dust and black carbon on snow is responsible for a highly effective climate forcing, through darkening of the snow surface and associated feedbacks. The interplay between post-depositional snow transformation (metamorphism) and the dynamics of LAPs in snow remains largely unknown. We obtained time series of X-ray tomography images of dust-contaminated samples undergoing dry snow metamorphism at around-2C. They provide the first observational evidence that temperature gradient metamorphism induces dust particle motion in snow, while no movement is observed under isothermal conditions. Under temperature gradient metamorphism, dust particles can enter the ice matrix due to sublimation-condensation processes and spread down mainly by falling into the pore space. Overall, such motions might reduce the radiative impact of dust in snow, in particular in arctic regions where temperature gradient metamorphism prevails.},
author = {Hagenmuller, Pascal and Flin, Frederic and Dumont, Marie and Tuzet, Fran{\c{c}}ois and Peinke, Isabel and Lapalus, Philippe and Dufour, Anne and Roulle, Jacques and P{\'{e}}zard, Laurent and Voisin, Didier and Ando, Edward and {Rolland Du Roscoat}, Sabine and Charrier, Pascal},
doi = {10.5194/tc-13-2345-2019},
issn = {19940424},
journal = {Cryosphere},
number = {9},
pages = {2345--2359},
title = {{Motion of dust particles in dry snow under temperature gradient metamorphism}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {13},
year = {2019}
}
@article{RN798,
abstract = {Each year, approximately 300,000 heart valve repair or replacement procedures are performed worldwide, including approximately 70,000 aortic valve replacement surgeries in the United States alone. Computational platforms for simulating cardiovascular devices such as prosthetic heart valves promise to improve device design and assist in treatment planning, including patient-specific device selection. This paper describes progress in constructing anatomically and physiologically realistic immersed boundary (IB) models of the dynamics of the aortic root and ascending aorta. This work builds on earlier IB models of fluid–structure interaction (FSI) in the aortic root, which previously achieved realistic hemodynamics over multiple cardiac cycles, but which also were limited to simplified aortic geometries and idealized descriptions of the biomechanics of the aortic valve cusps. By contrast, the model described herein uses an anatomical geometry reconstructed from patient-specific computed tomography angiography (CTA) data, and employs a description of the elasticity of the aortic valve leaflets based on a fiber-reinforced constitutive model fit to experimental tensile test data. The resulting model generates physiological pressures in both systole and diastole, and yields realistic cardiac output and stroke volume at physiological Reynolds numbers. Contact between the valve leaflets during diastole is handled automatically by the IB method, yielding a fully competent valve model that supports a physiological diastolic pressure load without regurgitation. Numerical tests show that the model is able to resolve the leaflet biomechanics in diastole and early systole at practical grid spacings. The model is also used to examine differences in the mechanics and fluid dynamics yielded by fresh valve leaflets and glutaraldehyde-fixed leaflets similar to those used in bioprosthetic heart valves. Although there are large differences in the leaflet deformations during diastole, the differences in the open configurations of the valve models are relatively small, and nearly identical hemodynamics are obtained in all cases considered.},
archivePrefix = {arXiv},
arxivId = {1705.04279},
author = {Hasan, Ali and Kolahdouz, Ebrahim M. and Enquobahrie, Andinet and Caranasos, Thomas G. and Vavalle, John P. and Griffith, Boyce E.},
doi = {10.1016/j.medengphy.2017.05.007},
eprint = {1705.04279},
issn = {18734030},
journal = {Medical Engineering and Physics},
keywords = {Aortic valve,Finite element method,Fluid–structure interaction,Immersed boundary method,Nonlinear elasticity},
pages = {72--84},
title = {{Image-based immersed boundary model of the aortic root}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {47},
year = {2017}
}
@inproceedings{RN958,
abstract = {Programs organization and advancement that endeavor to kill a portion of the pitfalls of traditional programming strategies by data organized in a hierarchy of classes with several powerful new concepts. This paper displays another strategy for examining and reengineering Constructor in class hierarchies. In object oriented Software (OOS), the accentuation is on data instead of procedure. Class is a way that ties the data and procedure together. Constructor is a special member procedure (SMP) class and destructor restores the memory delivers back to the program. The advancement comprises of exercises, for example, analyze, design, code, test, implement and maintenance. This paper gives the information about measuring the estimations of Object Oriented programming (OOP) advancement and proposed a Metric Measurement Model to guarantee that measurements of value properties are imperative in Object Oriented programming improvement.},
author = {Hassan, Nadia Moqbel},
booktitle = {2017 2nd Al-Sadiq International Conference on Multidisciplinary in IT and Communication Science and Applications, AIC-MITCSA 2017},
doi = {10.1109/AIC-MITCSA.2017.8722987},
isbn = {9781538642412},
keywords = {Constructors in derived class,Inheritance,Metrics measurement model,Object Oriented programming,Object oriented design metrics,Software Metrics},
pages = {7--12},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Analysis and Implementation of Constructor in Class Hierarchy}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067129598{\&}doi=10.1109{\%}2FAIC-MITCSA.2017.8722987{\&}partnerID=40{\&}md5=2c3f65035b60b33f66bb21bb6842bc6a},
year = {2017}
}
@article{RN919,
abstract = {Effective image-based artifact correction is an essential step in the analysis of diffusion MR images. Many current approaches are based on retrospective registration, which becomes challenging in the realm of high b-values and low signal-to-noise ratio, rendering the corresponding correction schemes more and more ineffective. We propose a novel registration scheme based on memetic search optimization that allows for simultaneous exploitation of different signal intensity relationships between the images, leading to more robust registration results. We demonstrate the increased robustness and efficacy of our method on simulated as well as in vivo datasets. In contrast to the state-of-art methods, the median target registration error (TRE) stayed below the voxel size even for high b-values (3000 s {\textperiodcentered} mm-2 and higher) and low SNR conditions. We also demonstrate the increased precision in diffusion-derived quantities by evaluating Neurite Orientation Dispersion and Density Imaging (NODDI) derived measures on a in vivo dataset with severe motion artifacts. These promising results will potentially inspire further studies on metaheuristic optimization in diffusion MRI artifact correction and image registration in general.},
author = {Hering, Jan and Wolf, Ivo and Maier-Hein, Klaus H.},
doi = {10.1109/TMI.2016.2557580},
issn = {1558254X},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Artifact correction,diffusion-weighted images,motion correction,particle swarm optimization,registration},
number = {10},
pages = {2280--2291},
title = {{Multi-Objective Memetic Search for Robust Motion and Distortion Correction in Diffusion MRI}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991444277{\&}doi=10.1109{\%}2FTMI.2016.2557580{\&}partnerID=40{\&}md5=c42388a9bec72c65c2504113fc52a566},
volume = {35},
year = {2016}
}
@article{RN882,
abstract = {Polysaccharide gels assembled from the anionic biopolymers pectin and carrageenan have been studied using transmission electron microscopy (TEM). Gels were formed in several different ways: for pectin, hydrogen bonding was used to form junction zones between strands, whereas for carrageenan systems, several different ion types were used to form ionotropic networks. Using this approach, several distinct network architectures were realized. In addition to preparing gelled samples for electron microscopy, a set of samples was taken without performing the additional treatment necessitated by the TEM measurements, and these were studied directly by small-angle X-ray scattering (SAXS). Taking careful consideration of the relative merits of different image sizes and available processing techniques, the real-space images acquired by TEM were used via radial integration of the Fourier transform to produce simulated scattering patterns. These intensity-versus-wavevector plots were compared with the results of SAXS experiments carried out on the unadulterated gels using synchrotron radiation. Although information regarding chain thicknesses and flexibilities was found to be modified by labeling and changes in the dielectric constant and mechanical properties of the surroundings in the TEM, the studies carried out here show that careful protocols can produce data sets where information acquired above ∼20 nm is broadly consistent with that obtained by SAXS studies carried out on unadulterated samples. The fact that at larger length scale the structure of these water-rich networks seems largely preserved in the TEM samples suggests that three-dimensional (3D) TEM tomography experiments carried out with careful sample preparation will be valuable tools for measuring network architecture and connectivity; information that is lost in SAXS owing to the intrinsic averaging nature of the technique.},
author = {Hernandez-Cerdan, Pablo and Mansel, Bradley W. and Leis, Andrew and Lundin, Leif and Williams, Martin A.K.},
doi = {10.1021/acs.biomac.7b01773},
issn = {15264602},
journal = {Biomacromolecules},
number = {3},
pages = {989--995},
title = {{Structural Analysis of Polysaccharide Networks by Transmission Electron Microscopy: Comparison with Small-Angle X-ray Scattering}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043595060{\&}doi=10.1021{\%}2Facs.biomac.7b01773{\&}partnerID=40{\&}md5=395df47da135ea25e2ffd52248d04f49},
volume = {19},
year = {2018}
}
@inproceedings{RN858,
author = {Hernandez-Cerdan, Pablo and Paniagua, Beatriz and Prothero, Jack and Marron, James S. and Livingston, Eric and Bateman, Ted and McCormick, Matthew M.},
booktitle = {Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging},
doi = {10.1117/12.2513007},
editor = {Gimi, B and Krol, A},
isbn = {9781510625532},
issn = {0277-786X},
pages = {40},
publisher = {SPIE},
title = {{Methods for quantitative characterization of bone injury from computed-tomography images}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068414398{\&}doi=10.1117{\%}2F12.2513007{\&}partnerID=40{\&}md5=919e41c5d51d55066df5c354c9c554f4},
volume = {10953},
year = {2019}
}
@inproceedings{RN914,
abstract = {From the preoperative partial nephrectomy planning perspective, it is essential to expose separately different kidney structures and to analyze their mutual topological relations. Only then, the identification of possible conflicts prior to surgical intervention can be facilitated. To enable this, we propose a segmentation frameworks for renal vascular tree, kidney and pelvicalyceal system from corresponding CT phases. In order to compensate for both patient position changes and volumetric changes related to respiratory activity, registration of different CT phases is required. It is performed by combining global rigid transform with multilevel and multiresolution B-spline registration. The research material consisted of fifteen patients that underwent CT scanning preceding kidney cancer surgery. Presented results using checkerboards and differential images prove the effectiveness of the proposed method. In addition, visualizations of the segmented structures (renal arteries, kidney, pelvicalyceal system) from registered CT phases are provided to exemplary demonstrate individual model for preoperative planning. This kind of solution meets the expectations of urological oncology in terms of facilitating planning the optimal surgical approach in partial nephrectomy. To the best of our knowledge, such a comprehensive strategy involving both the proposed segmentation frameworks and registration has not been introduced yet.},
author = {Heryan, Katarzyna and Skalski, Andrzej and Gajda, Janusz and Drewniak, Tomasz and Jakubowski, Jacek},
booktitle = {IST 2016 - 2016 IEEE International Conference on Imaging Systems and Techniques, Proceedings},
doi = {10.1109/IST.2016.7738233},
isbn = {9781509018178},
keywords = {B-spline registration,image registration,medical image processing,partial nephrectomy,segmentation,surgery planning},
pages = {255--260},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Registration of different phases of contrast-enhanced CT for facilitation of partial nephrectomy}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85004072339{\&}doi=10.1109{\%}2FIST.2016.7738233{\&}partnerID=40{\&}md5=aa3e12d899e600e256992bc3fbaface3},
year = {2016}
}
@article{RN829,
abstract = {Background and purpose: To evaluate the impact of deformation magnitude and image modality on deformable-image-registration (DIR) accuracy using Halcyon megavoltage cone beam CT images (MVCBCT). Materials and methods: Planning CT images of an anthropomorphic Head phantom were aligned rigidly with MVCBCT and re-sampled to achieve the same resolution, denoted as pCT. MVCBCT was warped with twenty simulated pre-known virtual deformation fields (Ti, i = 1–20) with increasing deformation magnitudes, yielding warped CBCT (wCBCT). The pCT and MVCBCT were registered to wCBCT respectively (Multi-modality and Uni-modality DIR), generating deformation vector fields Vi and Vi′ (i = 1–20). Vi and Vi′ were compared with Ti respectively to assess the DIR accuracy geometrically. In addition, Vi, Ti, and Vi′ were applied to pCT, generating deformed CT (dCTi), ground-truth CT (Gi) and deformed CT′ (dCTi′) respectively. The Hounsfield Unit (HU) on these virtual CT images were also compared. Results: The mean errors of vector displacement increased with the deformation magnitude. For deformation magnitudes between 2.82 mm and 7.71 mm, the errors of uni-modality DIR were 1.16 mm {\~{}} 1.73 mm smaller than that of multi-modality (p = 0.0001, Wilcoxon signed rank test). DIR could reduce the maximum signed and absolute HU deviations from 70.8 HU to 11.4 HU and 208 HU to 46.2 HU respectively. Conclusions: As deformation magnitude increases, DIR accuracy continues to deteriorate and uni-modality DIR consistently outperformed multi-modality DIR. DIR-based adaptive radiotherapy utilizing the noisy MVCBCT images is only conditionally applicable with caution.},
author = {Huang, Yuliang and Li, Chenguang and Wang, Haiyang and Hu, Qiaoqiao and Wang, Ruoxi and Chang, Cheng and Ma, Wenjun and Li, Weibo and Wu, Hao and Zhang, Yibao},
doi = {10.1016/j.ejmp.2020.02.016},
issn = {1724191X},
journal = {Physica Medica},
keywords = {Adaptive radiotherapy,Deformable image registration,MVCBCT},
pages = {82--87},
title = {{A quantitative evaluation of deformable image registration based on MV cone beam CT images: Impact of deformation magnitudes and image modalities}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079849262{\&}doi=10.1016{\%}2Fj.ejmp.2020.02.016{\&}partnerID=40{\&}md5=ffe26ce9da5145f82cc70d5a9518fab4},
volume = {71},
year = {2020}
}
@inbook{RN821,
abstract = {Accurate methods for computer aided diagnosis of breast cancer increase accuracy of detection and provide support to physicians in detecting challenging cases. In dynamic contrast enhancing magnetic resonance imaging (DCE-MRI), motion artifacts can appear as a result of patient displacements. Non-linear deformation algorithms for breast image registration provide with a solution to the correspondence problem in contrast with affine models. In this study we evaluate 3 popular non-linear registration algorithms: MIRTK, Demons, SyN Ants, and compare to the affine baseline. We propose automatic measures for reproducible evaluation on the DCE-MRI breast-diagnosis TCIA-database, based on edge detection and clustering algorithms, and provide a rank of the methods according to these measures.},
address = {Cham},
author = {Illan, I. A. and Ramirez, J. and Gorriz, J. M. and Pinker, K. and Meyer-Baese, A.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-030-00946-5_14},
editor = {Stoyanov, D and Taylor, Z and Kainz, B and Maicas, G and Beichel, R R},
isbn = {9783030009458},
issn = {16113349},
keywords = {DCE-MRI,Diffeomorphism,Medical image processing,Non-affine registration,Optical flow,Registration,Reproducibility},
pages = {124--131},
publisher = {Springer International Publishing Ag},
series = {Lecture Notes in Computer Science},
title = {{Reproducible evaluation of registration algorithms for movement correction in dynamic contrast enhancing magnetic resonance imaging for breast cancer diagnosis}},
type = {Book Section},
url = {{\%}3CGo to},
volume = {11040 LNCS},
year = {2018}
}
@article{RN811,
abstract = {An increasing number of studies are analysing the shapes of objects using geometric morphometrics with tomographic data, which are often segmented and transformed to three-dimensional (3D) surface models before measurement. This study aimed to evaluate the effects of different image segmentation methods on geometric morphometric data collection using computed tomography data collected from non-human primate skulls. Three segmentation methods based on a visually selected threshold, a half-maximum height protocol and a gradient and watershed algorithm were compared. For each method, the efficiency of surface reconstruction, the accuracy of landmark placement and the level of variation in shape and size compared with various levels of biological variation were evaluated. The visual-based method inflated the surface in high-density anatomical regions, whereas the half-maximum height protocol resulted in a large number of artificial holes and erosion. However, the gradient-based method mitigated these issues and generated the most efficient surface model. The segmentation method used had a much smaller effect on shape and size variation than interspecific and inter-individual differences. However, this effect was statistically significant and not negligible when compared with intra-individual (fluctuating asymmetric) variation. Although the gradient-based method is not widely used in geometric morphometric analyses, it may be one of promising options for reconstructing 3D surfaces. When evaluating small variations, such as fluctuating asymmetry, care should be taken around combining 3D data that were obtained using different segmentation methods.},
author = {Ito, Tsuyoshi},
doi = {10.1111/2041-210X.13274},
issn = {2041210X},
journal = {Methods in Ecology and Evolution},
keywords = {computed tomography,geometric morphometrics,measurement error,repeatability,segmentation},
number = {11},
pages = {1972--1984},
title = {{Effects of different segmentation methods on geometric morphometric data collection from primate skulls}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {10},
year = {2019}
}
@inproceedings{RN859,
abstract = {Magnetic Resonance Spectroscopy Imaging (MRSI) is a medical imaging method that measures per voxel a spectrum of signal intensities. It allows for the analysis of chemical compositions within the scanned tissue, which is particularly useful for tumor classification and measuring its infiltration of healthy tissue. Common analysis approaches consider one metabolite concentration at a time to produce intensity maps in the image space, which does not consider all relevant information at hand. We propose a system that uses coordinated views between image-space visualizations and visual representations of the spectral (or feature) space. Coordinated interaction allows for analyzing both aspects and relating the analysis results back to the other for further investigations. We demonstrate how our system can be used to analyze brain tumors.},
author = {Jawad, Muhammad and Molchanov, Vladimir and Linsen, Lars},
booktitle = {VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications},
doi = {10.5220/0007571801180128},
editor = {Kerren, A and Hurter, C and Braz, J},
isbn = {9789897583544},
keywords = {Coordinated views,Medical visualization,Multidimensional data visualization,Spectral imaging analysis},
pages = {118--128},
publisher = {SciTePress},
title = {{Coordinated image- and feature-space visualization for interactive magnetic resonance spectroscopy imaging data analysis}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064760921{\&}partnerID=40{\&}md5=85bea0be829568fc67dd653faf50fc47},
volume = {3},
year = {2019}
}
@article{RN897,
abstract = {This paper presents an overview of several efforts towards reproducible research in the field of medical imaging and visualization. In the first section, the components of Open Science are presented: open access, open data and open source. In the second section, the challenges of open-science are described and potential solutions are mentioned. Finally, a discussion on the potential future of open science and reproducible research is introduced.},
author = {Jomier, Julien},
doi = {10.3233/ISU-170846},
issn = {01675265},
journal = {Information Services and Use},
keywords = {Open access,Open data,Open science,Open source,Reproducibility},
number = {3},
pages = {361--367},
title = {{Open science - towards reproducible Research}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032568749{\&}doi=10.3233{\%}2FISU-170846{\&}partnerID=40{\&}md5=e551028edd91b74209c5cb3d27e210c2},
volume = {37},
year = {2017}
}
@article{RN945,
abstract = {This paper presents the development of the cloud software services for computational analysis of blood flows on a private university cloud. The main focus is on the software service level built on the top of the computational platform provided. Moreover, user friendly management tools have been developed by using the Apache jclouds API to enhance the management of OpenStack cloud infrastructure and to increase the accessibility of engineering software. The blood flow through an aortic valve is considered as a pilot application of the private cloud infrastructure. The investigated flows can be described using numerical models based on viscous incompressible Navier-Stokes equations. The modelling software environment based on ANSYS Fluent is developed as a software service (SaaS) for the numerical analysis of low flow, low pressure gradient aortic stenosis. The performance of the developed cloud infrastructure has been assessed testing CPU, memory IO, disk IO, network and the developed software service for computations of blood flow through an aortic valve. The results obtained have been compared with the performance obtained using the native hardware.},
author = {Kaceniauskas, A. and Pacevic, R. and Sta{\v{s}}kuniene, M. and Starikovicius, V. and Davidavicius, G.},
doi = {10.4203/ccp.107.23},
issn = {17593433},
journal = {Civil-Comp Proceedings},
keywords = {ANSYS Fluent,Aortic valve,Blood flows,Cloud computing,OpenStack,Performance analysis,Software as a service},
title = {{Development of cloud software services for computational analysis of blood flows}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971597818{\&}partnerID=40{\&}md5=313cdcaa420e54b4e9dad1c9f2228cc8},
volume = {107},
year = {2015}
}
@article{RN827,
abstract = {This paper presents a simulator tool that can simulate large databases of visually realistic longitudinal MRIs with known volume changes. The simulator is based on a previously proposed biophysical model of brain deformation due to atrophy in AD. In this work, we propose a novel way of reproducing realistic intensity variation in longitudinal brain MRIs, which is inspired by an approach used for the generation of synthetic cardiac sequence images. This approach combines a deformation field obtained from the biophysical model with a deformation field obtained by a non-rigid registration of two images. The combined deformation field is then used to simulate a new image with specified atrophy from the first image, but with the intensity characteristics of the second image. This allows to generate the realistic variations present in real longitudinal time-series of images, such as the independence of noise between two acquisitions and the potential presence of variable acquisition artifacts. Various options available in the simulator software are briefly explained in this paper. In addition, the software is released as an open-source repository. The availability of the software allows researchers to produce tailored databases of images with ground truth volume changes; we believe this will help developing more robust brain morphometry tools. Additionally, we believe that the scientific community can also use the software to further experiment with the proposed model, and add more complex models of brain deformation and atrophy generation.},
author = {Khanal, Bishesh and Ayache, Nicholas and Pennec, Xavier},
doi = {10.3389/fnins.2017.00132},
issn = {1662453X},
journal = {Frontiers in Neuroscience},
keywords = {Biomechanical simulation,Biophysical modeling,Neurodegeneration,Simulated database,Synthetic images,Synthetic longitudinal MRIs},
number = {MAR},
title = {{Simulating longitudinal brain MRIs with known volume changes and realistic variations in image intensity}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {11},
year = {2017}
}
@article{RN877,
abstract = {Background: With the development of versatile magnetic resonance acquisition techniques there arises a need for more advanced imaging simulation tools to enable adequate image appearance prediction, measurement sequence design and testing thereof. Recently, there is a growing interest in phase contrast angiography (PCA) sequence due to the capabilities of blood flow quantification that it offers. Moreover, as it is a non-contrast enhanced protocol, it has become an attractive option in areas, where usage of invasive contrast agents is not indifferent for the imaged tissue. Monitoring of the kidney function is an example of such an application. Results: We present a computer framework for simulation of the PCA protocol, both conventional and accelerated with echo-planar imaging (EPI) readout, and its application to the numerical models of kidney vasculatures. Eight patient-specific renal arterial trees were reconstructed following vessel segmentation in real computed tomography angiograms. In addition, a synthetic model was designed using a vascular tree growth simulation algorithm. The results embrace a series of synthetic PCA images of the renal arterial trees giving insight into the image formation and quantification of kidney hemodynamics. Conclusions: The designed simulation framework enables quantification of the PCA measurement error in relation to ground-truth flow velocity data. The mean velocity measurement error for the reconstructed renal arterial trees range from 1.5 to 12.8{\%} of the aliasing velocity value, depending on image resolution and flip angle. No statistically significant difference was observed between measurements obtained using EPI with a number of echos (NETL) = 4 and conventional PCA. In case of higher NETL factors peak velocity values can be underestimated up to 34{\%}.},
author = {Klepaczko, Artur and Szczypi{\'{n}}ski, Piotr and Strzelecki, Micha{\l} and Stefa{\'{n}}czyk, Ludomir},
doi = {10.1186/s12938-018-0471-y},
issn = {1475925X},
journal = {BioMedical Engineering Online},
keywords = {Blood flow simulation,Kidney vasculature modeling,MRI simulation,Phase contrast angiography,Vessel segmentation},
number = {1},
title = {{Simulation of phase contrast angiography for renal arterial models}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045392503{\&}doi=10.1186{\%}2Fs12938-018-0471-y{\&}partnerID=40{\&}md5=eb7e35093af28b9fca81c4e33c818b84},
volume = {17},
year = {2018}
}
@article{RN818,
abstract = {Magnetic resonance imaging (MRI) has evolved into the gold standard for quantifying excess adiposity, but reliable, efficient use in longitudinal studies requires analysis of large numbers of images. The objective of this study is to develop and evaluate a segmentation method designed to identify cardiac, subcutaneous, and visceral adipose tissue (VAT) in Dixon MRI scans. The proposed method is evaluated using 10 scans from volunteer females 18- to 35-years old, with body mass indexes between 30 and 39.99  kg  /  m2. Cross-sectional area (CSA) for cardiac adipose tissue (CAT), subcutaneous adipose tissue (SAT), and VAT, is compared to manually-traced results from three observers. Comparisons of CSA are made in 191 images for CAT, 394 images for SAT, and 50 images for VAT. The segmentation correlated well with respect to average observer CSA with Pearson correlation coefficient (R2) values of 0.80 for CAT, 0.99 for SAT, and 0.99 for VAT. The proposed method provides accurate segmentation of CAT, SAT, and VAT and provides an option to support longitudinal studies of obesity intervention.},
author = {Klingensmith, Jon D. and Elliott, Addison L. and Givan, Amy H. and Faszold, Zechariah D. and Mahan, Cory L. and Doedtman, Adam M.},
doi = {10.1117/1.jmi.6.1.014004},
issn = {2329-4310},
journal = {Journal of Medical Imaging},
number = {01},
pages = {1},
title = {{Development and evaluation of a method for segmentation of cardiac, subcutaneous, and visceral adipose tissue from Dixon magnetic resonance images}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {6},
year = {2019}
}
@article{RN946,
abstract = {A method for generating three-dimensional tomograms from multiple three-dimensional axial projections in STimulated Emission Depletion (STED) superresolution microscopy is introduced. Our STED{\textless} method, based on the use of a micromirror placed on top of a standard microscopic sample, is used to record a three-dimensional projection at an oblique angle in relation to the main optical axis. Combining the STED{\textless} projection with the regular STED image into a single view by tomographic reconstruction, is shown to result in a tomogram with three-to-four-fold improved apparent axial resolution. Registration of the different projections is based on the use of a mutual-information histogram similarity metric. Fusion of the projections into a single view is based on Richardson-Lucy iterative deconvolution algorithm, modified to work with multiple projections. Our tomographic reconstruction method is demonstrated to work with real biological STED superresolution images, including a data set with a limited signal-to-noise ratio (SNR); the reconstruction software (SuperTomo) and its source code will be released under BSD open-source license.},
author = {Koho, S. and Deguchi, T. and H{\"{a}}nninen, P. E.},
doi = {10.1111/jmi.12287},
issn = {13652818},
journal = {Journal of Microscopy},
keywords = {Axial tomography,Image fusion,Image processing,Image registration,Open source software,Reconstruction algorithms,STED,Superresolution microscopy},
number = {2},
pages = {208--218},
title = {{A software tool for tomographic axial superresolution in STED microscopy}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945471664{\&}doi=10.1111{\%}2Fjmi.12287{\&}partnerID=40{\&}md5=a793356576a358ca87cce6a3b305aea9},
volume = {260},
year = {2015}
}
@article{RN873,
abstract = {In this work, a data assimilation method is proposed following an optimise-then-discretise approach, and is applied in the context of computational haemodynamics. The methodology aims to make use of phase-contrast magnetic resonance imaging to perform optimal flow control in computational fluid dynamic simulations. Flow matching between observations and model predictions is performed in luminal regions, excluding near-wall areas, improving the near-wall flow reconstruction to enhance the estimation of related quantities such as wall shear stresses. The proposed approach remarkably improves the flow field at the aortic root and reveals a great potential for predicting clinically relevant haemodynamic phenomenology. This work presents model validation against an analytical solution using the standard 3-D Hagen-Poiseuille flow, and validation with real data involving the flow control problem in a glass replica of a human aorta imaged with a 3T magnetic resonance scanner. In vitro experiments consist of both a numerically generated reference flow solution, which is considered as the ground truth, as well as real flow MRI data obtained from phase-contrast flow acquisitions. The validation against the in vitro flow MRI experiments is performed for different flow regimes and model parameters including different mesh refinements.},
author = {Koltukluoǧlu, Taha S. and Blanco, Pablo J.},
doi = {10.1017/jfm.2018.329},
issn = {14697645},
journal = {Journal of Fluid Mechanics},
keywords = {blood flow,control theory,variational methods},
pages = {329--364},
title = {{Boundary control in computational haemodynamics}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047266553{\&}doi=10.1017{\%}2Fjfm.2018.329{\&}partnerID=40{\&}md5=50aee576fbf3bbc2426b262c16341404},
volume = {847},
year = {2018}
}
@article{RN805,
abstract = {Kondo et al. have generated a comprehensive library of convertible GAL4 knockin Drosophila strains for neurotransmitter receptor genes. The GAL4 lines can be converted into other reporters through the use of RMCE, providing a versatile toolkit. Expression profiling of receptor genes reveals neurochemical segmentation of the brain.},
author = {Kondo, Shu and Takahashi, Takahiro and Yamagata, Nobuhiro and Imanishi, Yasuhito and Katow, Hidetaka and Hiramatsu, Shun and Lynn, Katrina and Abe, Ayako and Kumaraswamy, Ajayrama and Tanimoto, Hiromu},
doi = {10.1016/j.celrep.2019.12.018},
issn = {22111247},
journal = {Cell Reports},
number = {1},
pages = {284--297.e5},
title = {{Neurochemical Organization of the Drosophila Brain Visualized by Endogenously Tagged Neurotransmitter Receptors}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {30},
year = {2020}
}
@article{RN866,
abstract = {Watershed is a widespread technique for image segmentation. Many researchers apply the method implemented in open source libraries without a deep understanding of its characteristics and limitations. In the review, we describe benchmarking outcomes of six open-source marker-controlled watershed implementations for the segmentation of 2D and 3D images. Even though the considered solutions are based on the same algorithm by flooding having O(n)computational complexity, these implementations have significantly different performance. In addition, building of watershed lines grows processing time. High memory consumption is one more bottleneck for dealing with huge volumetric images. Sometimes, the usage of more optimal software is capable of mitigating the issues with the long processing time and insufficient memory space. We assume parallel processing is capable of overcoming the current limitations. However, the development of concurrent approaches for the watershed segmentation remains a challenging problem.},
author = {Kornilov, Anton S. and Safonov, Ilia V.},
doi = {10.3390/jimaging4100123},
issn = {2313433X},
journal = {Journal of Imaging},
keywords = {Computational complexity,Flooding,Memory consumption,Open source software,Processing speed,Rain falling,Watershed segmentation},
number = {10},
title = {{An overview of watershed algorithm implementations in open source libraries}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059431965{\&}doi=10.3390{\%}2Fjimaging4100123{\&}partnerID=40{\&}md5=3ef917586e8b50f8e99994a08d52d2c9},
volume = {4},
year = {2018}
}
@incollection{RN956,
abstract = {Biological membrane images contain a variety of objects and patterns, which convey information about the underlying biological structures and mechanisms. The field of image analysis includes methods of computation which convert features and objects identified in images into quantitative information about biological structures represented in these images. Microscopy images are complex, noisy, and full of artifacts and consequently require multiple image processing steps for the extraction of meaningful quantitative information. This review is focused on methods of analysis of images of cells and biological membranes such as detection, segmentation, classification and machine learning, registration, tracking, and visualization. These methods could make possible, for example, to automatically identify defects in the cell membrane which affect physiological processes. Detailed analysis of membrane images could facilitate understanding of the underlying physiological structures or help in the interpretation of biological experiments.},
author = {Kulbacki, Marek and Segen, Jakub and Bak, Artur},
booktitle = {Advances in Anatomy Embryology and Cell Biology},
doi = {10.1007/978-3-319-56895-9_8},
isbn = {03015556 (ISSN)},
issn = {03015556},
pages = {119--140},
publisher = {Springer Verlag},
title = {{Analysis, recognition, and classification of biological membrane images}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030665154{\&}doi=10.1007{\%}2F978-3-319-56895-9{\_}8{\&}partnerID=40{\&}md5=ee50a7c74dbc3a51f07be9c4038bb7b0},
volume = {227},
year = {2017}
}
@inproceedings{RN937,
abstract = {Lung cancer is the leading cause of cancer-related death, and early-stage diagnosis is critical to survival. Biopsy is typically required for a definitive diagnosis, but current low-risk clinical options for lung biopsy cannot access all biopsy sites. We introduce a motion planner for a multilumen transoral lung access system, a new system that has the potential to perform safe biopsies anywhere in the lung, which could enable more effective early-stage diagnosis of lung cancer. The system consists of three stages in which a bronchoscope is deployed transorally to the lung, a concentric tube robot pierces through the bronchial tubes into the lung parenchyma, and a steerable needle deploys through a properly oriented concentric tube and steers through the lung parenchyma to the target site while avoiding anatomical obstacles such as significant blood vessels. A sampling-based motion planner computes actions for each stage of the system and considers the coupling of the stages in an efficient manner. We demonstrate the motion planner's fast performance and ability to compute plans with high clearance from obstacles in simulated anatomical scenarios.},
author = {Kuntz, Alan and Torres, Luis G. and Feins, Richard H. and Webster, Robert J. and Alterovitz, Ron},
booktitle = {IEEE International Conference on Intelligent Robots and Systems},
doi = {10.1109/IROS.2015.7353829},
isbn = {9781479999941},
issn = {21530866},
keywords = {Biomedical imaging,Biopsy,Electron tubes,Lungs,Needles,Planning,Robots},
pages = {3255--3261},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Motion planning for a three-stage multilumen transoral lung access system}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958176573{\&}doi=10.1109{\%}2FIROS.2015.7353829{\&}partnerID=40{\&}md5=7513ff75c060dfaaf113a4487f0d9a72},
volume = {2015-Decem},
year = {2015}
}
@incollection{RN907,
abstract = {CLARITY is a method for converting biological tissues into translucent and porous hydrogel-tissue hybrids. This facilitates interrogation with light sheet microscopy and penetration of molecular probes while avoiding physical slicing. In this work, we develop a pipeline for registering CLARIfied mouse brains to an annotated brain atlas. Due to the novelty of this microscopy technique it is impractical to use absolute intensity values to align these images to existing standard atlases. Thus we adopt a large deformation diffeomorphic approach for registering images via mutual information matching. Furthermore we show how a cascaded multi-resolution approach can improve registration quality while reducing algorithm run time. As acquired image volumes were over a terabyte in size, they were far too large for work on personal computers. Therefore the NeuroData computational infrastructure was deployed for multi-resolution storage and visualization of these images and aligned annotations on the web.},
archivePrefix = {arXiv},
arxivId = {1612.00356},
author = {Kutten, Kwame S. and Charon, Nicolas and Miller, Michael I. and Ratnanather, J. Tilak and Matelsky, Jordan and Baden, Alexander D. and Lillaney, Kunal and Deisseroth, Karl and Ye, Li and Vogelstein, Joshua T.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-319-66182-7_32},
editor = {Descoteaux, M and Duchesne, S and Franz, A and Jannin, P and Collins, D L and Maier-Hein, L},
eprint = {1612.00356},
isbn = {9783319661810},
issn = {16113349},
pages = {275--282},
publisher = {Springer Verlag},
title = {{A large deformation diffeomorphic approach to registration of CLARITY images via mutual information}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029360827{\&}doi=10.1007{\%}2F978-3-319-66182-7{\_}32{\&}partnerID=40{\&}md5=3534498089ac268864d5dd2937d2983d},
volume = {10433 LNCS},
year = {2017}
}
@inproceedings{RN922,
abstract = {The CLARITY method renders brains optically transparent to enable high-resolution imaging in the structurally intact brain. Anatomically annotating CLARITY brains is necessary for discovering which regions contain signals of interest. Manually annotating whole-brain, terabyte CLARITY images is difficult, time-consuming, subjective, and error-prone. Automatically registering CLARITY images to a pre-annotated brain atlas offers a solution, but is difficult for several reasons. Removal of the brain from the skull and subsequent storage and processing cause variable non-rigid deformations, thus compounding inter-subject anatomical variability. Additionally, the signal in CLARITY images arises from various biochemical contrast agents which only sparsely label brain structures. This sparse labeling challenges the most commonly used registration algorithms that need to match image histogram statistics to the more densely labeled histological brain atlases. The standard method is a multiscale Mutual Information B-spline algorithm that dynamically generates an average template as an intermediate registration target. We determined that this method performs poorly when registering CLARITY brains to the Allen Institute's Mouse Reference Atlas (ARA), because the image histogram statistics are poorly matched. Therefore, we developed a method (Mask-LDDMM) for registering CLARITY images, that automatically find the brain boundary and learns the optimal deformation between the brain and atlas masks. Using Mask-LDDMM without an average template provided better results than the standard approach when registering CLARITY brains to the ARA. The LDDMM pipelines developed here provide a fast automated way to anatomically annotate CLARITY images. Our code is available as open source software at http://NeuroData.io.},
archivePrefix = {arXiv},
arxivId = {1605.02060},
author = {Kutten, Kwame S. and Vogelstein, Joshua T. and Charon, Nicolas and Ye, Li and Deisseroth, Karl and Miller, Michael I.},
booktitle = {Optics, Photonics and Digital Technologies for Imaging Applications IV},
doi = {10.1117/12.2227444},
editor = {Truchetet, F and Schelkens, P and Ebrahimi, T and Cristobal, G and Saarikko, P},
eprint = {1605.02060},
isbn = {9781510601413},
issn = {1996756X},
pages = {989616},
publisher = {SPIE},
title = {{Deformably registering and annotating whole CLARITY brains to an atlas via masked LDDMM}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991503754{\&}doi=10.1117{\%}2F12.2227444{\&}partnerID=40{\&}md5=e5bfa771d1160566e24476b33887e0d9},
volume = {9896},
year = {2016}
}
@inproceedings{RN950,
abstract = {In this paper we present a novel image registration software that has been specifically designed to suit the needs of the study of tumor response quantification. Joining a cross-platform architecture, the exclusive use of open-source libraries and some unique features, our tool is being successfully used in the frame of the ARTFIBio project, focused on the study of predictive individualized models of head and neck tumor response to radiotherapy. {\textcopyright} 2014 IEEE.},
author = {Landesa-Vazquez, Iago and Alba-Castro, Jose Luis and Mera-Iglesias, Moises and Aramburu-Nunez, David and Lopez-Medina, Antonio and Munoz-Garzon, Victor},
booktitle = {2014 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2014},
doi = {10.1109/BHI.2014.6864326},
isbn = {9781479921317},
pages = {149--152},
publisher = {IEEE Computer Society},
title = {{ARTFIBio: A cross-platform image registration tool for tumor response quantification in head and neck cancer}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906848785{\&}doi=10.1109{\%}2FBHI.2014.6864326{\&}partnerID=40{\&}md5=edbfdf06c7b7a66d48a98f667bc3d4ad},
year = {2014}
}
@article{RN898,
abstract = {This paper provides an overview of the highlights of the 2017 NFAIS Annual Conference, The Big Pivot: Re- Engineering Scholarly Communication, that was held in Alexandria, VA from February 26-28, 2017. The goal of the conference was to examine the scholarly record and its current evolution in a digital world - both in how it functions and how it serves the information and scholarly research communities. The program stressed how in today's environment, new and innovative advances in information technology are drafting a blueprint that will optimize the ways in which users create, access, and use data and information. New government mandates and policies continue to be implemented on a global basis to facilitate open access to research outputs while in parallel alternative methods for peer review and measuring impact are being utilized.Within the context of these changes, the conference attempted to look at where this blueprint may lead the information community over the next few years.},
author = {Lawlor, Bonnie},
doi = {10.3233/ISU-170854},
issn = {01675265},
journal = {Information Services and Use},
keywords = {Data management,Library cyberinfrastructure,Open access,Open data,Scholarly communication},
number = {3},
pages = {283--306},
title = {{An overview of the NFAIS 2017 annual conference: The big pivot: Re-engineering scholarly communication}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032578212{\&}doi=10.3233{\%}2FISU-170854{\&}partnerID=40{\&}md5=b8ce9fb87b2e39ac050ef613e4bf6b5a},
volume = {37},
year = {2017}
}
@inproceedings{RN972,
abstract = {A precise analysis of medical image is an important stage in the contouring phase throughout radiotherapy preparation. Medical images are mostly used as radiographic techniques in diagnosis, clinical studies and treatment planning Medical image processing tool are also similarly as important. With a medical image processing tool, it is possible to speed up and enhance the operation of the analysis of the medical image. This paper describes medical image processing software tool which attempts to secure the same kind of programmability advantage for exploring applications of the pipelined processors. These tools simulate complete systems consisting of several of the proposed processing components, in a configuration described by a graphical schematic diagram. In this paper, fifteen different medical image processing tools will be compared in several aspects. The main objective of the comparison is to gather and analysis on the tool in order to recommend users of different operating systems on what type of medical image tools to be used when analysing different types of imaging. A result table was attached and discussed in the paper.},
author = {Lee, Lay Khoon and Liew, Siau Chuin},
booktitle = {2015 4th International Conference on Software Engineering and Computer Systems, ICSECS 2015: Virtuous Software Solutions for Big Data},
doi = {10.1109/ICSECS.2015.7333105},
editor = {Zain, J M and Majid, M A and Ameedeen, M A and Arshah, R A and Azmi, Z R M and Mustaffa, Z M},
isbn = {9781467367226},
keywords = {computer vision,image processing,tools component},
pages = {171--176},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{A survey of medical image processing tools}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962073514{\&}doi=10.1109{\%}2FICSECS.2015.7333105{\&}partnerID=40{\&}md5=67e62e688838868692dd48230c893599},
year = {2015}
}
@inbook{RN902,
abstract = {We are entering a new era of biomedical research that is driven by the demand for more effective therapeutics to prevent and treat human disease. Organoids, cultured ex vivo, are the future of this new era of biomedical research and are poised to replace preclinical 2D cell models, and in some cases animal models of human disease. Therefore, the drug discovery and development pipeline is retooling high-throughput technologies to accommodate organoids as the model of choice. In particular, the marriage of high-content screening (HCS) with organoid models for drug discovery will be a critical component in this new era of drug development. This book chapter is focused on the state-of-the-art HCS technology and how this technology is being retooled for drug discovery and development with human organoids.},
author = {Li, L. and LaBarbera, D. V.},
booktitle = {Comprehensive Medicinal Chemistry III},
doi = {10.1016/B978-0-12-409547-2.12329-7},
isbn = {9780128032008},
keywords = {3D imaging technology,3D tissue culture,Drug discovery,Extracellular matrix,High-content screening,High-throughput imaging,High-throughput screening,Image analysis software,Microenvironment,Multicellular tumor spheroids,Organoids,Organotypic,Pluripotent stem cells,Precision medicine,Spheroids},
pages = {388--415},
publisher = {Elsevier Inc.},
title = {{3D high-content screening of organoids for drug discovery}},
type = {Book Section},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046151116{\&}doi=10.1016{\%}2FB978-0-12-409547-2.12329-7{\&}partnerID=40{\&}md5=e152840c738581970058285c2f8412a3},
volume = {2-8},
year = {2017}
}
@article{RN893,
abstract = {Background Most existing objective surgical motion analysis schemes are limited to structured surgical tasks or recognition of motion patterns for certain categories of surgeries. Analyzing instrument motion data with respect to anatomical structures can break the limit, and an anatomical region segmentation algorithm is required for the analysis. Methods An atlas was generated by manually segmenting the skull base into nine regions, including left/right anterior/posterior ethmoid sinuses, frontal sinus, left and right maxillary sinuses, nasal airway, and sphenoid sinus. These regions were selected based on anatomical and surgical significance in skull base and sinus surgery. Six features, including left and right eye center, nasofrontal beak, anterior tip of nasal spine, posterior edge of hard palate at midline, and clival body at foramen magnum, were used for alignment. The B-spline deformable registration was adapted to fine tune the registration, and bony boundaries were automatically extracted for final precision improvement. The resultant deformation field was applied to the atlas, and the motion data were clustered according to the deformed atlas. Results Eight maxillofacial computed tomography scans were used in experiments. One was manually segmented as the atlas. The others were segmented by the proposed method. Motion data were clustered into nine groups for every dataset and outliers were filtered. Conclusions The proposed algorithm improved the efficiency of motion data clustering and requires limited human interaction in the process. The anatomical region segmentations effectively filtered out the portion of motion data that are out of surgery sites and grouped them according to anatomical similarities.},
author = {Li, Yangming and Bly, Randall A. and Harbison, R. Alex and Humphreys, Ian M. and Whipple, Mark E. and Hannaford, Blake and Moe, Kris S.},
doi = {10.1055/s-0037-1604406},
issn = {21936331},
journal = {Journal of Neurological Surgery, Part B: Skull Base},
keywords = {anatomical region,atlas-based segmentation,motion analysis,objective skill assessment,operating room data,sinus surgery,skull base},
number = {6},
pages = {490--496},
title = {{Anatomical Region Segmentation for Objective Surgical Skill Assessment with Operating Room Motion Data}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026850018{\&}doi=10.1055{\%}2Fs-0037-1604406{\&}partnerID=40{\&}md5=e63320b0921b5580303ada6248b2e769},
volume = {78},
year = {2017}
}
@incollection{RN879,
abstract = {Data fusion and specific visualization of CT and SPECT are important for diagnosis and research purposes. Selected problems are considered in the paper and are related to the developed CT–SPECT Analyzer software. Hierarchical mapping with SPECT priority for maximum value of rays is applied in this software. Three variants of color mappings are presented. Some practical aspects related to low quality of CT are considered also. The most promising is the rainbow gradient with gamma curve adjustment.},
author = {Listewnik, Maria H. and Piwowarska-Bilska, Hanna and Safranow, Krzysztof and Iwanowski, Jacek and Laszczy{\'{n}}ska, Maria and Chosia, Maria and Ostrowski, Marek and Birkenfeld, Bo{\.{z}}ena and Mazurek, Przemys{\l}aw},
booktitle = {Advances in Intelligent Systems and Computing},
doi = {10.1007/978-3-319-68720-9_2},
editor = {Choras, M and Choras, R S},
isbn = {9783319687193},
issn = {21945357},
keywords = {CT,Data fusion,SPECT,Volumetric visualization},
pages = {11--18},
publisher = {Springer Verlag},
title = {{CT–SPECT analyzer - A tool for CT and SPECT data fusion and volumetric visualization}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031410415{\&}doi=10.1007{\%}2F978-3-319-68720-9{\_}2{\&}partnerID=40{\&}md5=333a82c9184f554ff151234b1617f13b},
volume = {681},
year = {2018}
}
@article{RN841,
abstract = {An abdominal aortic aneurysm (AAA) is a focal dilation of the abdominal aorta, that if not treated, tends to grow and may rupture. The most common treatment for AAAs is the endovascular aneurysm repair (EVAR), which requires that patients undergo Computed Tomography Angiography (CTA)-based post-operative lifelong surveillance due to the possible appearance of complications. These complications may again lead to AAA dilation and rupture. However, there is a lack of advanced quantitative image-analysis tools to support the clinicians in the follow-up. Currently, the approach is to evaluate AAA diameter changes along time to infer the progress of the patient and the post-operative risk of AAA rupture. An increased AAA diameter is usually associated with a higher rupture risk, but there are some small AAAs that rupture, whereas other larger aneurysms remain stable. This means that the diameter-based rupture risk assessment is not suitable for all the cases, and there is increasing evidence that the biomechanical behavior of the AAA may provide additional valuable information regarding the progression of the disease and the risk of rupture. Hence, we propose a promising methodology for post-operative CTA time-series registration and subsequent aneurysm biomechanical strain analysis. From these strains, quantitative image-based descriptors are extracted using a principal component analysis of the tensile and compressive strain fields. Evaluated on 22 patients, our approach yields a mean area under the curve of 88.6{\%} when correlating the strain-based quantitative descriptors with the long-term patient prognosis. This suggests that the strain information directly extracted from the CTA images is able to capture the biomechanical behavior of the aneurysm without relying on finite element modeling and simulation. Furthermore, the extracted descriptors set the basis for possible future imaging biomarkers that may be used in clinical practice. Apart from the diameter, these biomarkers may be used to assess patient prognosis and to enable informed decision making after an EVAR intervention, especially in difficult uncertain cases.},
author = {L{\'{o}}pez-Linares, Karen and Garc{\'{i}}a, Inmaculada and Garc{\'{i}}a, Ainhoa and Cortes, Camilo and Piella, Gemma and Mac{\'{i}}a, Iv{\'{a}}n and Noailly, J{\'{e}}r{\^{o}}me and {Gonz{\'{a}}lez Ballester}, Miguel A.},
doi = {10.3389/fbioe.2019.00267},
issn = {22964185},
journal = {Frontiers in Bioengineering and Biotechnology},
keywords = {abdominal aortic aneurysm,biomarker,biomechanics,computed tomography angiography,deformation,follow-up,prognosis,strain analysis},
title = {{Image-Based 3D Characterization of Abdominal Aortic Aneurysm Deformation After Endovascular Aneurysm Repair}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075353420{\&}doi=10.3389{\%}2Ffbioe.2019.00267{\&}partnerID=40{\&}md5=a232028ba988f3c1b4c51cbbb0913dc4},
volume = {7},
year = {2019}
}
@article{RN961,
abstract = {Facial nerve segmentation is of considerable importance for preoperative planning of cochlear implantation. However, it is strongly influenced by the relatively low resolution of the cone-beam computed tomography (CBCT) images used in clinical practice. In this paper, we propose a super-resolution classification method, which refines a given initial segmentation of the facial nerve to a subvoxel classification level from CBCT/CT images. The super-resolution classification method learns the mapping from low-resolution CBCT/CT images to high-resolution facial nerve label images, obtained from manual segmentation on micro-CT images. We present preliminary results on dataset, 15 ex vivo samples scanned including pairs of CBCT/CT scans and high-resolution micro-CT scans, with a leave-one-out evaluation, and manual segmentations on micro-CT images as ground truth. Our experiments achieved a segmentation accuracy with a Dice coefficient of 0.818 ± 0.052, surface-to-surface distance of 0.121 ± 0.030 mm, and Hausdorff distance of 0.715 ± 0.169 mm. We compared the proposed technique to two other semiautomated segmentation software tools, ITK-SNAP and GeoS, and show the ability of the proposed approach to yield subvoxel levels of accuracy in delineating the facial nerve.},
author = {Lu, Ping and Barazzetti, Livia and Chandran, Vimal and Gavaghan, Kate and Weber, Stefan and Gerber, Nicolas and Reyes, Mauricio},
doi = {10.1109/TBME.2017.2697916},
issn = {15582531},
journal = {IEEE Transactions on Biomedical Engineering},
keywords = {CBCT,CT,Cochlear implantation,Facial nerve,Micro-CT,Segmentation,Superresolution,Supervised learning},
number = {1},
pages = {178--188},
title = {{Highly accurate facial nerve segmentation refinement from CBCT/CT imaging using a super-resolution classification approach}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047089305{\&}doi=10.1109{\%}2FTBME.2017.2697916{\&}partnerID=40{\&}md5=1e4c788c488d0055ad983d484d1cb30c},
volume = {65},
year = {2018}
}
@article{RN860,
abstract = {Automatic and accurate segmentation of the prostate is still a challenging task due to intensity inhomogeneity and complicated deformation of MR images. To tackle these problems with multi-atlas segmentation, in this paper, we propose a new metric for image registration and new descriptor for label fusion. First, to reduce the amount of edges in entropic graph, a modified {\$}\backslashalpha {\$} -mutual information ( {\$}\backslashalpha {\$} -MI) based on fast minimal spanning tree (MST) is implemented for deformable registration. Second, localized {\$}\backslashalpha {\$} -MI allowing for the spatial information is proposed with the stochastic gradient optimization, and the feature space is encoded by a sparse auto-encoder. Finally, a multi-scale descriptor utilizing local self-similarity is integrated into the patch-based label fusion to obtain final segmentation. Experiments were performed on two subsets of totally 46 T2-weighted prostate MR images from 46 patients. Compared to {\$}\backslashalpha {\$} -MI based on {\$}{\{}k{\}}{\$} -nearest neighbor graph, the registration time of {\$}\backslashalpha {\$} -MI based on fast MST can be reduced by almost half. The median Dice overlap of registration using localized {\$}\backslashalpha {\$} -MI on one subset is shown to improve significantly from 0.725 to 0.764 ( {\$}p=1.14\backslashtimes 10{\^{}}{\{}-5{\}}{\$} ), compared to using {\$}\backslashalpha {\$} -MI without the spatial information. The median Dice overlap of prostate segmentation using the proposed method on 20 testing images of another subset is 0.871, and the median Hausdorff distance is 8.013 mm, which demonstrate a comparable accuracy to state-of-the-art methods.},
author = {Lu, Xuesong and Zha, Yunfei and Qiao, Yuchuan and Wang, Defeng},
doi = {10.1109/ACCESS.2019.2943485},
issn = {21693536},
journal = {IEEE Access},
keywords = {Deformable registration,local self-similarity,minimal spanning tree,patch-based label fusion,prostate segmentation,$\alpha$-mutual information},
pages = {138645--138656},
title = {{Feature-Based Deformable Registration Using Minimal Spanning Tree for Prostate MR Segmentation}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077958932{\&}doi=10.1109{\%}2FACCESS.2019.2943485{\&}partnerID=40{\&}md5=daba943919bc91c3fa945bebda516afa},
volume = {7},
year = {2019}
}
@article{RN785,
abstract = {In order to effectively improve the accuracy of multi-atlas segmentation algorithm of hippocampus, U-Net convolutional neural network is applied to label fusion of multi-atlas. The algorithm in the atlases selection performs mutual information and gradient similarity calculation, avoids the interference of the surrounding tissue structure on the atlas selection, selects the floating image group which is more suitable for the target map. In the pre-processing stage, extracting the region of interest centered on hippocampus can effectively reduce the size of data. In the registration process, re-sampling is used instead of the coarse registration, which reduces the time, and then uses the diffeomorphic demons algorithm, which has good smoothness, continuity and topological retentiveness. In the label fusion stage, an improved U-Net network based on deep learning theory for multi-atlas MRI hippocampal segmentation algorithm is proposed. The experimental results show that the segmentation accuracy of the improved algorithm is about 5{\%} higher than that of the traditional algorithm, the algorithm time is reduced by about 50{\%}. The improved U-Net network based multi-atlas hippocampal segmentation algorithm has the characteristics of high precision and high efficiency for segmentation of the hippocampus in target image.},
author = {Lu, Yue and Ma, Yu and Wang, Hui and Wang, Yuan},
doi = {10.3788/YJYXS20193411.1091},
issn = {10072780},
journal = {Chinese Journal of Liquid Crystals and Displays},
keywords = {Hippocampus,Label fusion,Multi-atlas,U-Net},
number = {11},
pages = {1090--1103},
title = {{Multi-atlaslabel fusion based on U-Net}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {34},
year = {2019}
}
@article{RN828,
abstract = {Techniques based on imaging serial sections of brain tissue provide insight into brain structure and function. However, to compare or combine them with results from three dimensional imaging methods, reconstruction into a volumetric form is required. Currently, there are no tools for performing such a task in a streamlined way. Here we propose the Possum volumetric reconstruction framework which provides a selection of 2D to 3D image reconstruction routines allowing one to build workflows tailored to one's specific requirements. The main components include routines for reconstruction with or without using external reference and solutions for typical issues encountered during the reconstruction process, such as propagation of the registration errors due to distorted sections. We validate the implementation using synthetic datasets and actual experimental imaging data derived from publicly available resources. We also evaluate efficiency of a subset of the algorithms implemented. The Possum framework is distributed under MIT license and it provides researchers with a possibility of building reconstruction workflows from existing components, without the need for low-level implementation. As a consequence, it also facilitates sharing and data exchange between researchers and laboratories.},
author = {Majka, Piotr and W{\'{o}}jcik, Daniel K.},
doi = {10.1007/s12021-015-9286-1},
issn = {15392791},
journal = {Neuroinformatics},
keywords = {3D reconstruction,Brain atlas,Histology,Image analysis,Image registration,Light microscopy},
number = {3},
pages = {265--278},
title = {{Possum—A Framework for Three-Dimensional Reconstruction of Brain Images from Serial Sections}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {14},
year = {2016}
}
@inproceedings{RN951,
abstract = {Peripheral Quantitative Computed Tomography (pQCT) is a non-invasive imaging technology that is well-suited for quantification of bone structural and material properties. Because of its increasing use and applicability, the development of automated quantification methods for pQCT images is an appealing field of research. In this paper we introduce a software system for hard and soft tissue quantification in the lower leg using pQCT imaging data. The main stages of our approach are the segmentation and identification of bone, muscle and fat, and the computation of densitometric and geometric variables of each regional tissue type. Our system was validated against reference area and densitometric measurements over a set of test images and produced encouraging results. {\textcopyright} 2014 Copyright SPIE.},
author = {Makrogiannis, Sokratis and Ferrucci, Luigi},
booktitle = {Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring IV},
doi = {10.1117/12.2050790},
isbn = {9781628410495},
issn = {1996756X},
pages = {911216},
publisher = {SPIE},
title = {{Software system for computing material and structural properties of bone and muscle in the lower extremity from pQCT}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907042843{\&}doi=10.1117{\%}2F12.2050790{\&}partnerID=40{\&}md5=f686ccba07e29e0052a7bb5edd216e5b},
volume = {9112},
year = {2014}
}
@article{RN862,
abstract = {With this work we release $\backslash$ttC $\backslash$ttL $\backslash$ttA $\backslash$ttI $\backslash$ttR $\backslash$ttE, a distributed-memory implementation of an effective solver for constrained large deformation diffeomorphic image registration problems in three dimensions. We consider an optimal control formulation. We invert for a stationary velocity field that parameterizes the deformation map. Our solver is based on a globalized, preconditioned, inexact reduced space Gauss-Newton-Krylov scheme. We exploit state-of-the-art techniques in scientific computing to develop an effective solver that scales to thousands of distributed memory nodes on high-end clusters. We present the formulation, discuss algorithmic features, describe the software package, and introduce an improved preconditioner for the reduced space Hessian to speed up the convergence of our solver. We test registration performance on synthetic and real data. We demonstrate registration accuracy on several neuroimaging datasets. We compare the performance of our scheme against different flavors of the $\backslash$ttD $\backslash$tte $\backslash$ttm $\backslash$tto $\backslash$ttn $\backslash$tts algorithm for diffeomorphic image registration. We study convergence of our preconditioner and our overall algorithm. We report scalability results on state-of-the-art supercomputing platforms. We demonstrate that we can solve registration problems for clinically relevant data sizes in two to four minutes on a standard compute node with 20 cores, attaining excellent data fidelity. With the present work we achieve a speedup of (on average) 5$\backslash$times with a peak performance of up to 17$\backslash$times compared to our former work.},
archivePrefix = {arXiv},
arxivId = {1808.04487},
author = {Mang, Andreas and Gholami, Amir and Davatzikos, Christos and Biros, George},
doi = {10.1137/18M1207818},
eprint = {1808.04487},
issn = {10957197},
journal = {SIAM Journal on Scientific Computing},
keywords = {Diffeomorphic image registration,Distributed-memory algorithm,KKT preconditioner,LDDMM,Newton-Krylov method,Optimal control,PDE-constrained optimization},
number = {5},
pages = {C548--C584},
title = {{Claire: A distributed-memory solver for constrained large deformation diffeomorphic image registration}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074644073{\&}doi=10.1137{\%}2F18M1207818{\&}partnerID=40{\&}md5=e3c350f33ddb25e3d2f751778e14fc3c},
volume = {41},
year = {2019}
}
@article{RN900,
abstract = {Diffusion weighted magnetic resonance imaging, or DWI, is one of the most promising tools for the analysis of neural microstructure and the structural connectome of the human brain. The application of DWI to map early development of the human connectome in-utero, however, is challenged by intermittent fetal and maternal motion that disrupts the spatial correspondence of data acquired in the relatively long DWI acquisitions. Fetuses move continuously during DWI scans. Reliable and accurate analysis of the fetal brain structural connectome requires careful compensation of motion effects and robust reconstruction to avoid introducing bias based on the degree of fetal motion. In this paper we introduce a novel robust algorithm to reconstruct in-vivo diffusion-tensor MRI (DTI) of the moving fetal brain and show its effect on structural connectivity analysis. The proposed algorithm involves multiple steps of image registration incorporating a dynamic registration-based motion tracking algorithm to restore the spatial correspondence of DWI data at the slice level and reconstruct DTI of the fetal brain in the standard (atlas) coordinate space. A weighted linear least squares approach is adapted to remove the effect of intra-slice motion and reconstruct DTI from motion-corrected data. The proposed algorithm was tested on data obtained from 21 healthy fetuses scanned in-utero at 22–38 weeks gestation. Significantly higher fractional anisotropy values in fiber-rich regions, and the analysis of whole-brain tractography and group structural connectivity, showed the efficacy of the proposed method compared to the analyses based on original data and previously proposed methods. The results of this study show that slice-level motion correction and robust reconstruction is necessary for reliable in-vivo structural connectivity analysis of the fetal brain. Connectivity analysis based on graph theoretic measures show high degree of modularity and clustering, and short average characteristic path lengths indicative of small-worldness property of the fetal brain network. These findings comply with previous findings in newborns and a recent study on fetuses. The proposed algorithm can provide valuable information from DWI of the fetal brain not available in the assessment of the original 2D slices and may be used to more reliably study the developing fetal brain connectome.},
author = {Marami, Bahram and {Mohseni Salehi}, Seyed Sadegh and Afacan, Onur and Scherrer, Benoit and Rollins, Caitlin K. and Yang, Edward and Estroff, Judy A. and Warfield, Simon K. and Gholipour, Ali},
doi = {10.1016/j.neuroimage.2017.04.033},
issn = {10959572},
journal = {NeuroImage},
keywords = {Connectome,Diffusion-weighted MRI,Fetal brain,Motion Correction,Registration},
pages = {475--488},
pmid = {28433624},
title = {{Temporal slice registration and robust diffusion-tensor reconstruction for improved fetal brain structural connectivity analysis}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018689711{\&}doi=10.1016{\%}2Fj.neuroimage.2017.04.033{\&}partnerID=40{\&}md5=2a9b47eff0a2637f2e0b729996723477},
volume = {156},
year = {2017}
}
@article{RN918,
abstract = {This work proposes a novel approach for motion-robust diffusion-weighted (DW) brain MRI reconstruction through tracking temporal head motion using slice-to-volume registration. The slice-level motion is estimated through a filtering approach that allows tracking the head motion during the scan and correcting for out-of-plane inconsistency in the acquired images. Diffusion-sensitized image slices are registered to a base volume sequentially over time in the acquisition order where an outlier-robust Kalman filter, coupled with slice-to-volume registration, estimates head motion parameters. Diffusion gradient directions are corrected for the aligned DWI slices based on the computed rotation parameters and the diffusion tensors are directly estimated from the corrected data at each voxel using weighted linear least squares. The method was evaluated in DWI scans of adult volunteers who deliberately moved during scans as well as clinical DWI of 28 neonates and children with different types of motion. Experimental results showed marked improvements in DWI reconstruction using the proposed method compared to the state-of-the-art DWI analysis based on volume-to-volume registration. This approach can be readily used to retrieve information from motion-corrupted DW imaging data.},
author = {Marami, Bahram and Scherrer, Benoit and Afacan, Onur and Erem, Burak and Warfield, Simon K. and Gholipour, Ali},
doi = {10.1109/TMI.2016.2555244},
issn = {1558254X},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Diffusion-weighted MRI,motion tracking,motion-robust MRI,outlier-robust kalman filter,slice registration},
number = {10},
pages = {2258--2269},
pmid = {27834639},
title = {{Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991454091{\&}doi=10.1109{\%}2FTMI.2016.2555244{\&}partnerID=40{\&}md5=377eb413c86d8cfebc71fb33434e9bf4},
volume = {35},
year = {2016}
}
@inproceedings{RN977,
abstract = {In this paper we present SimpleElastix, an extension of SimpleITK designed to bring the Elastix medical image registration library to a wider audience. Elastix is a modular collection of robust C++ image registration algorithms that is widely used in the literature. However, its command-line interface introduces overhead during prototyping, experimental setup, and tuning of registration algorithms. By integrating Elastix with SimpleITK, Elastix can be used as a native library in Python, Java, R, Octave, Ruby, Lua, Tcl and C{\#} on Linux, Mac and Windows. This allows Elastix to intregrate naturally with many development environments so the user can focus more on the registration problem and less on the underlying C++ implementation. As means of demonstration, we show how to register MR images of brains and natural pictures of faces using minimal amount of code. SimpleElastix is open source, licensed under the permissive Apache License Version 2.0 and available at https://github.com/kaspermarstal/SimpleElastix.},
author = {Marstal, Kasper and Berendsen, Floris and Staring, Marius and Klein, Stefan},
booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
doi = {10.1109/CVPRW.2016.78},
isbn = {9781467388504},
issn = {21607516},
pages = {574--582},
publisher = {IEEE Computer Society},
title = {{SimpleElastix: A User-Friendly, Multi-lingual Library for Medical Image Registration}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010216516{\&}doi=10.1109{\%}2FCVPRW.2016.78{\&}partnerID=40{\&}md5=0d777eb051999b3ff620a82e4ec05c67},
year = {2016}
}
@article{RN960,
abstract = {Medical image atlases contain much information about human anatomy, but learning the shapes of anatomical regions and making sense of the overall structure defined in the atlas can be problematic. Atlases may contain hundreds of regions with complex shapes which can be tightly packed together. This makes visualisation difficult since the shapes can fit together in complex ways and visually obscure each other. In this work, we describe a technique which enables interactive exploration of medical image atlases that permits the hierarchical structure of the atlas and the content of an underlying medical image to be investigated simultaneously. Our method enables a user to create visualizations of the atlas similar to the exploded views used in technical illustrations to show the structure of mechanical assemblies. These views are constrained by the geometry of the atlas and the hierarchical structure to reduce the complexity of user interaction. We also enable the user to explode the atlas meshes themselves. The atlas meshes are registered with a medical image which is displayed on the cut surfaces of the meshes using raycasting. Results from the AAL human brain atlas are presented and discussed.},
author = {McGraw, Tim and Guayaquil-Sosa, Alejandro},
doi = {10.1080/21681163.2017.1343686},
issn = {21681171},
journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization},
keywords = {Biomedical visualisation,brain atlas,volume rendering},
number = {6},
pages = {668--677},
title = {{Hybrid rendering of exploded views for medical image atlas visualization}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85024392095{\&}doi=10.1080{\%}2F21681163.2017.1343686{\&}partnerID=40{\&}md5=627a45812ac92f205dffd7dce1423f77},
volume = {6},
year = {2018}
}
@incollection{RN881,
abstract = {Thoracic Aortic Aneurysm (TAA) is an enlargement of the aortic lumen at chest level. An accurate assessment of the geometry of the enlarged vessel is crucial when planning vascular interventions. This study developed an automatic method to extract aortic geometry and supra-aortic vessels from computerized tomography (CT) images. The proposed method consists of a fast-marching level-set method for detection of the initial aortic region from multiple seed points automatically selected along the pre-extracted vessel centerline, and a level-set method for extraction of the detailed aortic geometry from the initial aortic region. The automatic method was implemented inside Endosize (Therenva, Rennes), a commercially available software used for planning minimally invasive techniques. The performance of the algorithm was compare with the existing Endosize segmentation method (based on the region growing approach). For this comparison a CT dataset from an open source data file system (Osirix Advanced Imaging in 3D, 2016) was used. Results showed that, whilst the segmentation time increased (956 s for the new method, 0.308 s for the existing one), the new method produced a more accurate aortic segmentation, particularly in the region of supra-aortic branches. Further work to examine the efficacy of the proposed method should include a statistical study of performance across many datasets.},
author = {Mercuri, Massimiliano and Narracott, Andrew J. and Hose, Dr and G{\"{o}}ksu, Cemil},
booktitle = {Lecture Notes in Computational Vision and Biomechanics},
doi = {10.1007/978-3-319-68195-5_95},
isbn = {22129391 (ISSN)},
issn = {22129413},
keywords = {Automatic segmentation, Level-set method,Thoracic Aortic Aneurysm (TAA),Virtual aortic surgery planning},
pages = {875--882},
publisher = {Springer Netherlands},
title = {{An automatic method for aortic segmentation based on level-set methods using multiple seed points}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032380517{\&}doi=10.1007{\%}2F978-3-319-68195-5{\_}95{\&}partnerID=40{\&}md5=332ed513ca2cfe55ca8c7c67df29dd9d},
volume = {27},
year = {2018}
}
@inproceedings{RN896,
abstract = {Lagrangian carotid strain imaging (LCSI) involves estimation of deformation in the carotid artery due to blood pressure variations under cardiac pulsation. Local strain over a cardiac cycle is tracked, which is computationally intensive. We incur long offline processing times for LCSI which becomes a limiting factor for clinical adoption. We report on the computational speedup obtained for a parallelized implementation of LCSI using CUDA programming for fast computation. LCSI is currently performed using a multi-level block matching algorithm written in C++ using the Insight Toolkit (ITK) system. We have implemented this code on a NVIDIA k40 GPU for running CUDA kernels called from the ITK C++ code. The multi-level algorithm consists of three processing stages; stage 1 performs block matching at the coarsest level while level 3 performs block-matching at the finest scale on radiofrequency signals. The regularization step which incurred the largest computational time was implemented on the GPU. Cross-correlation was then implemented with the regularization step thereby avoiding a CPU to GPU data transfer. Shared memory was used in the regularization step to further reduce processing time. The computation time per frame pairs for LCSI with our initial implementation was about 316.41 secs for an in vivo human carotid data set, thereby taking 131 minutes for an entire loop over a cardiac cycle with 25 frames. GPU implementation of regularization provided per frame results in 99.92 secs, a speedup of 3.16X. Further optimization with implementation of cross correlation on the GPU and use of shared memory improved the computation time to 23 secs per frame, a speed up of 13.75X, reducing processing time to 9.5 minutes over a cardiac cycle.},
author = {Meshram, Nirvedh H. and Varghese, Tomy},
booktitle = {IEEE International Ultrasonics Symposium, IUS},
doi = {10.1109/ULTSYM.2017.8092085},
isbn = {9781538633830},
issn = {19485727},
keywords = {CUDA,Carotid Plaque,GPU,Lagrangian,Strain Imaging,Ultrasound},
publisher = {IEEE Computer Society},
title = {{Fast multilevel Lagrangian carotid strain imaging with GPU computing}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039446737{\&}doi=10.1109{\%}2FULTSYM.2017.8092085{\&}partnerID=40{\&}md5=6bb0c6a43db0421599b9f25e0926d036},
year = {2017}
}
@article{RN871,
abstract = {A multilevel Lagrangian carotid strain imaging algorithm is analyzed to identify computational bottlenecks for implementation on a graphics processing unit (GPU). Displacement tracking including regularization was found to be the most computationally expensive aspect of this strain imaging algorithm taking about 2.2 h for an entire cardiac cycle. This intensive displacement tracking was essential to obtain Lagrangian strain tensors. However, most of the computational techniques used for displacement tracking are parallelizable, and hence GPU implementation is expected to be beneficial. A new scheme for subsample displacement estimation referred to as a multilevel global peak finder was also developed since the Nelder-Mead simplex optimization technique used in the CPU implementation was not suitable for GPU implementation. GPU optimizations to minimize thread divergence and utilization of shared and texture memories were also implemented. This enables efficient use of the GPU computational hardware and memory bandwidth. Overall, an application speedup of 168.75 × was obtained enabling the algorithm to finish in about 50 s for a cardiac cycle. Last, comparison of GPU and CPU implementations demonstrated no significant difference in the quality of displacement vector and strain tensor estimation with the two implementations up to a 5{\%} interframe deformation. Hence, a GPU implementation is feasible for clinical adoption and opens opportunity for other computationally intensive techniques.},
author = {Meshram, Nirvedh H. and Varghese, Tomy},
doi = {10.1109/TUFFC.2018.2841346},
issn = {08853010},
journal = {IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control},
keywords = {Carotid strain imaging,compute unified device architecture (CUDA),elastography,graphics processing unit (GPU),ultrasound},
number = {8},
pages = {1370--1379},
title = {{GPU accelerated multilevel lagrangian carotid strain imaging}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047652346{\&}doi=10.1109{\%}2FTUFFC.2018.2841346{\&}partnerID=40{\&}md5=635a26fdcb2c9b17367aeddc67197444},
volume = {65},
year = {2018}
}
@article{RN817,
abstract = {In this article we present the NeuTomPy Toolbox, a new Python package for tomographic data processing and reconstruction. The toolbox includes pre-processing algorithms, artifacts removal and a wide range of iterative reconstruction methods as well as the Filtered Back Projection algorithm. The NeuTomPy toolbox was conceived primarily for neutron tomography datasets and developed to support the need of users and researchers to compare state-of-the-art reconstruction methods and choose the optimal data processing workflow for their data. In fact, in several cases sparse-view datasets are acquired to reduce scan time during a neutron tomography experiment. Hence, there is great interest in improving quality of the reconstructed images by means of iterative methods and advanced image-processing algorithms. The toolbox has a modular design, multi-threading capabilities and it supports Windows, Linux and Mac OS operating systems. The NeuTomPy toolbox is open source and it is released under the GNU General Public License v3, encouraging researchers and developers to contribute. In this paper we present an overview of the main toolbox functionalities and finally we show a typical usage example.},
author = {Micieli, Davide and Minniti, Triestino and Gorini, Giuseppe},
doi = {10.1016/j.softx.2019.01.005},
issn = {23527110},
journal = {SoftwareX},
keywords = {Neutron imaging,Tomographic reconstruction software,Tomography},
pages = {260--264},
title = {{NeuTomPy toolbox, a Python package for tomographic data processing and reconstruction}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {9},
year = {2019}
}
@inproceedings{RN857,
author = {Nain, Jagjeet and Mueller, Johannes},
booktitle = {Image and Signal Processing for Remote Sensing XXV 2019},
doi = {10.1117/12.2532730},
editor = {Bruzzone, L and Bovolo, F and Benediktsson, J A},
isbn = {9781510630130},
issn = {1996756X},
pages = {2},
publisher = {SPIE},
title = {{Improving band to band registration accuracy of SEVIRI level 1.5 products}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078146492{\&}doi=10.1117{\%}2F12.2532730{\&}partnerID=40{\&}md5=9a2bd6db58344dccac47650014f184cb},
volume = {11155},
year = {2019}
}
@article{RN947,
abstract = {Classical surgery is being overtaken by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected anatomy, advanced imaging techniques such as 3D C-arm computed tomography (CT) and C-arm fluoroscopy are routinely used in clinical practice for intraoperative guidance. However, due to constraints regarding acquisition time and device configuration, intraoperative modalities have limited soft tissue image quality and reliable assessment of the cardiac anatomy typically requires contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a probabilistic sparse matching approach to fuse high-quality preoperative CT images and nongated, noncontrast intraoperative C-arm CT images by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the preoperative CT and mapped to the intraoperative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments on 95 clinical datasets demonstrate that our model-based fusion approach has an average execution time of 1.56 s, while the accuracy of 5.48 mm between the anchor anatomy in both images lies within expert user confidence intervals. In direct comparison with image-to-image registration based on an open-source state-of-the-art medical imaging library and a recently proposed quasi-global, knowledge-driven multi-modal fusion approach for thoracic-abdominal images, our model-based method exhibits superior performance in terms of registration accuracy and robustness with respect to both target anatomy and anchor anatomy alignment errors.},
author = {Neumann, Dominik and Grbic, Sasa and John, Matthias and Navab, Nassir and Hornegger, Joachim and Ionasec, Razvan},
doi = {10.1109/TMI.2014.2343936},
issn = {1558254X},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Anatomical overlay,Procedure guidance,computed tomography (CT),model-based cardiac image registration},
number = {1},
pages = {49--60},
pmid = {25095250},
title = {{Probabilistic sparse matching for robust 3D/3D fusion in minimally invasive surgery}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920159378{\&}doi=10.1109{\%}2FTMI.2014.2343936{\&}partnerID=40{\&}md5=a81867f10d05f5ddb53549f54f45df01},
volume = {34},
year = {2015}
}
@article{RN794,
abstract = {Background and objective: Stereotactical procedures require exact trajectory planning to avoid blood vessels in the trajectory path. Innovation in imaging and image recognition techniques have facilitated the automatic detection of blood vessels during the planning process and may improve patient safety in the future. To assess the feasibility of a vessel detection and warning system using currently available imaging and vessel segmentation techniques. Methods: Image data were acquired from post-contrast, isovolumetric T1-weighted sequences (T1CE) and time.-of-flight MR angiography at 3T or 7T from a total of nine subjects. Vessel segmentation by a combination of a vessel-enhancement filter with subsequent level-set segmentation was evaluated using three different methods (Vesselness, FastMarching and LevelSet) in 45 stereotactic trajectories. Segmentation results were compared to a gold-standard of manual segmentation performed jointly by two human experts. Results: The LevelSet method performed best with a mean interclass correlation coefficient (ICC) of 0.76 [0.73, 0.81] compared to the FastMarching method with ICC 0.70 [0.67, 0.73] respectively. The Vesselness algorithm achieved clearly inferior overall performance with a mean ICC of 0.56 [0.53, 0.59]. The differences in mean ICC between all segmentation methods were statistically significant (p {\textless} 0.001 with post-hoc p {\textless} 0.026). The LevelSet method performed likewise good in MPRAGE and 3T-TOF images and excellent in 7T-TOF image data. The negative predictive value (NPV) was very high ({\textgreater}97{\%}) for all methods and modalities. Positive predictive values (PPV) were found in the overall range of 65–90{\%} likewise depending on algorithm and modality. This pattern reflects the disposition of all segmentation methods – in case of misclassification - to produce preferentially false-positive than false-negative results. In a clinical setting, two to three potential collision warnings would be given per trajectory on average with a PPV of around 50{\%}. Conclusions: It is feasible to integrate a clinically meaningful vessel detection and collision warning system into stereotactical planning software. Both, T1CE and MRA sequences are suitable as image data for such an application.},
author = {Neumann, Jan Oliver and Campos, Benito and Younes, B. and Jakobs, Martin and Unterberg, Andreas and Kiening, Karl and Hubert, Alexander},
doi = {10.1016/j.cmpb.2019.105037},
issn = {18727565},
journal = {Computer Methods and Programs in Biomedicine},
keywords = {Decision support,Stereotaxy,Vessel segmentation},
pages = {8},
title = {{Evaluation of three automatic brain vessel segmentation methods for stereotactical trajectory planning}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {182},
year = {2019}
}
@inproceedings{RN944,
abstract = {We analyse CT image denoising when applied to vessel segmentation. Proposed semi-global quality metric based on the contrast-to-noise ratio allowed us to estimate initial image quality and efficiency of denoising procedures without prior knowledge about a noise-free image. We show that the total variance filtering in L1 metric provides the best denoising when compared to other well-known denoising procedures such as nonlocal means denoising or anisotropic diffusion. Computational complexity of this denoising algorithm is addressed by comparing its implementation for Intel MIC and for NVIDIA CUDA HPC systems.},
author = {Nikonorov, A. and Kolsanov, A. and Petrov, M. and Yuzifovich, Y. and Prilepin, E. and Bychenkov, K.},
booktitle = {SIGMAP 2015 - 12th International Conference on Signal Processing and Multimedia Applications, Proceedings; Part of 12th International Joint Conference on e-Business and Telecommunications, ICETE 2015},
doi = {10.5220/0005542400590067},
editor = {Obaidat, M S and Lorenz, P and Cabello, E},
isbn = {9789897581182},
keywords = {CUDA,Contrast to noise ratio,Fast marching,GPGPU,Geodesic active contours,Liver,Proximal algorithms,Total variance de-noising,Vessels segmentation,Xeon phi},
pages = {59--67},
publisher = {SciTePress},
title = {{Contrast-to-noise based metric of denoising algorithms for liver vein segmentation}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84970959837{\&}partnerID=40{\&}md5=0d3090fef893d468c72b58001dc5eceb},
year = {2015}
}
@incollection{RN930,
abstract = {This paper describes a comprehensive multi-step algorithm for vascular structure segmentation in CT scan data, from raw slice images to a 3D object, with an emphasis on improving segmentation quality and assessing computational complexity. To estimate initial image quality and to evaluate denoising in the absence of the noise-free image, we propose a semi-global contrast-to-noise quality metric. We show that total variation-based filtering in the L1 metric results in the best denoising when compared to widely used nonlocal means or anisotropic diffusion denoising. To address higher computational complexity of our denoising algorithm, we created two high performance implementations, using Intel MIC and NVIDIA CUDA and compared results. In combination with proposed nearly real-time incremental segmentation technique, it provides fast and framework with controlled quality.},
author = {Nikonorov, A. and Kolsanov, A. and Petrov, M. and Yuzifovich, Y. and Prilepin, E. and Chaplygin, S. and Zelter, P. and Bychenkov, K.},
booktitle = {Communications in Computer and Information Science},
doi = {10.1007/978-3-319-30222-5_23},
editor = {Obaidat, M S and Lorenz, P},
isbn = {9783319302218},
issn = {18650929},
keywords = {CUDA,Contrast to noise ratio,Fast marching,GPGPU,Geodesic active contours,Liver,Proximal algorithms,Total variance de-noising,Vessels segmentation,Xeon phi},
pages = {490--507},
publisher = {Springer Verlag},
title = {{Vessel segmentation for noisy CT data with quality measure based on single-point contrast-to-noise ratio}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960396227{\&}doi=10.1007{\%}2F978-3-319-30222-5{\_}23{\&}partnerID=40{\&}md5=00b082a6118fc326c7a012f9772959cf},
volume = {585},
year = {2016}
}
@article{RN806,
abstract = {Patient-specific finite element (FE) modeling of atherosclerotic plaque is challenging, as there is limited information available clinically to characterize plaque components. This study proposes that for the limited data available in vivo, material properties of plaque and artery can be identified using inverse FE analysis and either a simple neo-Hookean constitutive model or assuming linear elasticity provides sufficient accuracy to capture the changes in vessel deformation, which is the available clinical metric. To test this, 10 human cadaveric femoral arteries were each pressurized ex vivo at 6 pressure levels, while intravascular ultrasound (IVUS) and virtual histology (VH) imaging were performed during controlled pull-back to determine vessel geometry and plaque structure. The VH images were then utilized to construct FE models with heterogeneous material properties corresponding to the vessel plaque components. The constitutive models were then fit to each plaque component by minimizing the difference between the experimental and the simulated geometry using the inverse FE method. Additionally, we further simplified the analysis by assuming the vessel wall had a homogeneous structure, i.e. lumping artery and plaque as one tissue. We found that for the heterogeneous wall structure, the simulated and experimental vessel geometries compared well when the fitted neo-Hookean parameters or elastic modulus, in the case of linear elasticity, were utilized. Furthermore, taking the median of these fitted parameters then inputting these as plaque component mechanical properties in the finite element simulation yielded differences between simulated and experimental geometries that were on average around 2{\%} greater (1.30–5.55{\%} error range to 2.33–11.71{\%} error range). For the homogeneous wall structure the simulated and experimental wall geometries had an average difference of around 4{\%} although when the difference was calculated using the median fitted value this difference was larger than for the heterogeneous fits. Finally, comparison to uniaxial tension data and to literature constitutive models also gave confidence to the suitability of this simplified approach for patient-specific arterial simulation based on data that may be acquired in the clinic.},
author = {Noble, Christopher and Carlson, Kent D. and Neumann, Erica and Dragomir-Daescu, Dan and Erdemir, Ahmet and Lerman, Amir and Young, Melissa},
doi = {10.1016/j.jmbbm.2019.103453},
issn = {18780180},
journal = {Journal of the Mechanical Behavior of Biomedical Materials},
keywords = {Intravascular ultrasound,Inverse finite element analysis,Peripheral artery disease,Pressure inflation testing,Virtual histology},
pages = {14},
title = {{Patient specific characterization of artery and plaque material properties in peripheral artery disease}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {101},
year = {2020}
}
@incollection{RN836,
abstract = {We present a new framework for fine-scale vessel segmentation from fundus images through registration and segmentation of corresponding fluorescein angiography (FA) images. In FA, fluorescent dye is used to highlight the vessels and increase their contrast. Since these highlights are temporally dispersed among multiple FA frames, we first register the FA frames and aggregate the per-frame segmentations to construct a detailed vessel mask. The constructed FA vessel mask is then registered to the fundus image based on an initial fundus vessel mask. Postprocessing is performed to refine the final vessel mask. Registration of FA frames, as well as registration of FA vessel mask to the fundus image, are done by similar hierarchical coarse-to-fine frameworks, both comprising rigid and non-rigid registration. Two CNNs with identical network structures, both trained on public datasets but with different settings, are used for vessel segmentation. The resulting final vessel segmentation contains fine-scale, filamentary vessels extracted from FA and corresponding to the fundus image. We provide quantitative evaluation as well as qualitative examples which support the robustness and the accuracy of the proposed method.},
author = {Noh, Kyoung Jin and Park, Sang Jun and Lee, Soochahn},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-030-32239-7_86},
editor = {Shen, D and Yap, P T and Liu, T and Peters, T M and Khan, A and Staib, L H and Essert, C and Zhou, S},
isbn = {9783030322380},
issn = {16113349},
keywords = {Filamentary vessels,Fine-scale vessel segmentation,Fluorescein angiography,Fundus images,Registration},
pages = {779--787},
publisher = {Springer},
title = {{Fine-scale vessel extraction in fundus images by registration with fluorescein angiography}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075630951{\&}doi=10.1007{\%}2F978-3-030-32239-7{\_}86{\&}partnerID=40{\&}md5=e5b88e05907541abbf2b36b425c9f544},
volume = {11764 LNCS},
year = {2019}
}
@inbook{RN822,
abstract = {Convolutional neural networks (CNNs) have been widely used to address various image analysis problems at the cost of intensive computational load and large amounts of annotated training data. When it comes to Medical Imaging, annotation is often complicated and/or expensive, and innovative methods for dealing with small or very imbalanced training sets are mostly welcome. In this context, this paper proposes a novel approach for efficiently synthesizing volumetric patch data from a small amount of samples using their latent data. Our method consists of two major steps. First, we train a 3D CNN auto-encoder for unsupervised learning of volumetric latent data by means of multivariate Gaussian mixture models (GMMs): while the encoder finds latent representations of volumes using GMMs, the decoder uses the estimated GMMs parameters to reconstruct the volume observed in the input. Then, we modify latent data of samples at training time to generate similar, but different, new samples: we run non-rigid registrations between patches decoded from real latent data and patches decoded from modified latent data, and warp the corresponding original image patches using the resulting displacement fields. We evaluated our method in the context of lung nodules synthesis using the publicly available LUNA challenge dataset, and generated new realistic samples out of real lung nodules, preserving their original texture and neighbouring anatomical structures. Our results demonstrate that 3D CNNs trained using our synthesis method were able to consistently deliver lower lung nodule false positive rates, which indicates an improvement in the networks discriminant power.},
address = {Cham},
author = {Oliveira, Dario Augusto Borges and Viana, Matheus Palhares},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-030-00536-8_12},
editor = {Gooya, A and Goksel, O and Oguz, I and Burgos, N},
isbn = {9783030005351},
issn = {16113349},
keywords = {Convolutional neural networks,Generative models,Lung nodule false positive reduction,Multivariate Gaussian mixture models,Nodules synthesis},
pages = {111--118},
publisher = {Springer International Publishing Ag},
series = {Lecture Notes in Computer Science},
title = {{Lung nodule synthesis using cnn-based latent data representation}},
type = {Book Section},
url = {{\%}3CGo to},
volume = {11037 LNCS},
year = {2018}
}
@article{RN768,
abstract = {The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a “silver standard” annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the “improved” segmentation from T1-sequences alone.},
author = {Pagnozzi, Alex M. and Fripp, Jurgen and Rose, Stephen E.},
doi = {10.1016/j.neuroimage.2019.116018},
issn = {10959572},
journal = {NeuroImage},
keywords = {Deep grey matter,Magnetic resonance imaging,Segmentation,Subcortical anatomies},
pages = {20},
title = {{Quantifying deep grey matter atrophy using automated segmentation approaches: A systematic review of structural MRI studies}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {201},
year = {2019}
}
@inproceedings{RN915,
abstract = {Current prostate cancer screening methods involve non-targeted needle biopsies and detection of clinically-insignificant lesions that receive excessive treatments, exposing patients to unnecessary adverse side effects and placing a burden on our health care systems. There is a strong clinical need for improved prostate imaging methods that are sensitive and specific for clinically-significant prostate cancer lesions to guide needle biopsies, target focal treatments, and improve overall patient outcomes. In this study, we compared 3D in vivo Acoustic Radiation Force Impulse (ARFI) imaging with 3 Tesla, endorectal coil, multi-parametric magnetic resonance imaging (mpMRI) to correlate the ability for each modality to identify clinically-significant prostate cancer lesions. We also correlated Apparent Diffusion Coefficient (ADC) values from Diffusion Weighted Imaging (DWI) MR sequences with ARFI indices of suspicion and MR Prostate Imaging - Reporting and Data Systems (PI-RADS) scores, testing the hypothesis that increased cellular density is associated with regions suspicious for prostate cancer in ARFI images. Overall, ARFI and mpMR imaging were well-correlated in identifying clinically-significant prostate cancer lesions. There were several cases where only one of the imaging modalities was able to identify the prostate cancer lesion, highlighting the potential to further improve prostate cancer lesion detection and localization with a fused ARFI:mpMRI imaging system. ADC values were decreased in all prostate cancer lesions identified with mpMRI, but there were no obvious trends between the absolute ADC values and the ARFI image indices of suspicion.},
author = {Palmeri, Mark and Glass, Tyler and Gupta, Rajan and McCormick, Matt and Brown, Alison and Polascik, Thomas and Rosenzweig, Stephen and Buck, Andrew and Nightingale, Kathy},
booktitle = {IEEE International Ultrasonics Symposium, IUS},
doi = {10.1109/ULTSYM.2016.7728618},
isbn = {9781467398978},
issn = {19485727},
publisher = {IEEE Computer Society},
title = {{Comparison between 3D ARFI imaging and mpMRI in detecting clinically-significant prostate cancer lesions}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84996562155{\&}doi=10.1109{\%}2FULTSYM.2016.7728618{\&}partnerID=40{\&}md5=06c5d391a733c4454dbbf008fae5b2ca},
volume = {2016-Novem},
year = {2016}
}
@article{RN807,
abstract = {Abstract: Absolute quantification of radiotracer distribution using SPECT/CT imaging is of great importance for dosimetry aimed at personalized radionuclide precision treatment. However, its accuracy depends on many factors. Using phantom measurements, this multi-vendor and multi-center study evaluates the quantitative accuracy and inter-system variability of various SPECT/CT systems as well as the effect of patient size, processing software and reconstruction algorithms on recovery coefficients (RC). Methods: Five SPECT/CT systems were included: Discovery™ NM/CT 670 Pro (GE Healthcare), Precedence™ 6 (Philips Healthcare), Symbia Intevo™, and Symbia™ T16 (twice) (Siemens Healthineers). Three phantoms were used based on the NEMA IEC body phantom without lung insert simulating body mass indexes (BMI) of 25, 28, and 47 kg/m2. Six spheres (0.5–26.5 mL) and background were filled with 0.1 and 0.01 MBq/mL 99mTc-pertechnetate, respectively. Volumes of interest (VOI) of spheres were obtained by a region growing technique using a 50{\%} threshold of the maximum voxel value corrected for background activity. RC, defined as imaged activity concentration divided by actual activity concentration, were determined for maximum (RCmax) and mean voxel value (RCmean) in the VOI for each sphere diameter. Inter-system variability was expressed as median absolute deviation (MAD) of RC. Acquisition settings were standardized. Images were reconstructed using vendor-specific 3D iterative reconstruction algorithms with institute-specific settings used in clinical practice and processed using a standardized, in-house developed processing tool based on the SimpleITK framework. Additionally, all data were reconstructed with a vendor-neutral reconstruction algorithm (Hybrid Recon™; Hermes Medical Solutions). Results: RC decreased with decreasing sphere diameter for each system. Inter-system variability (MAD) was 16 and 17{\%} for RCmean and RCmax, respectively. Standardized reconstruction decreased this variability to 4 and 5{\%}. High BMI hampers quantification of small lesions ({\textless} 10 ml). Conclusion: Absolute SPECT quantification in a multi-center and multi-vendor setting is feasible, especially when reconstruction protocols are standardized, paving the way for a standard for absolute quantitative SPECT.},
author = {Peters, Steffie M.B. and van der Werf, Niels R. and Segbers, Marcel and van Velden, Floris H.P. and Wierts, Roel and Blokland, Koos (J ).A.K. and Konijnenberg, Mark W. and Lazarenko, Sergiy V. and Visser, Eric P. and Gotthardt, Martin},
doi = {10.1186/s40658-019-0268-5},
issn = {21977364},
journal = {EJNMMI Physics},
keywords = {SPECT/CT,absolute quantification,performance evaluation,recovery coefficient},
number = {1},
pages = {14},
title = {{Towards standardization of absolute SPECT/CT quantification: a multi-center and multi-vendor phantom study}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {6},
year = {2019}
}
@article{RN804,
abstract = {Background: Quantitative SPECT imaging in targeted radionuclide therapy with lutetium-177 holds great potential for individualized treatment based on dose assessment. The establishment of dose-effect relations requires a standardized method for SPECT quantification. The purpose of this multi-center study is to evaluate quantitative accuracy and inter-system variations of different SPECT/CT systems with corresponding commercially available quantitative reconstruction algorithms. This is an important step towards a vendor-independent standard for quantitative lutetium-177 SPECT. Methods: Four state-of-the-art SPECT/CT systems were included: Discovery™ NM/CT 670Pro (GE Healthcare), Symbia Intevo™, and two Symbia™ T16 (Siemens Healthineers). Quantitative accuracy and inter-system variations were evaluated by repeatedly scanning a cylindrical phantom with 6 spherical inserts (0.5 – 113 ml). A sphere-to-background activity concentration ratio of 10:1 was used. Acquisition settings were standardized: medium energy collimator, body contour trajectory, photon energy window of 208 keV (± 10{\%}), adjacent 20{\%} lower scatter window, 2 × 64 projections, 128 × 128 matrix size, and 40 s projection time. Reconstructions were performed using GE Evolution with Q.Metrix™, Siemens xSPECT Quant™, Siemens Broad Quantification™ or Siemens Flash3D™ algorithms using vendor recommended settings. In addition, projection data were reconstructed using Hermes SUV SPECT™ with standardized reconstruction settings to obtain a vendor-neutral quantitative reconstruction for all systems. Volumes of interest (VOI) for the spheres were obtained by applying a 50{\%} threshold of the sphere maximum voxel value corrected for background activity. For each sphere, the mean and maximum recovery coefficient (RCmean and RCmax) of three repeated measurements was calculated, defined as the imaged activity concentration divided by the actual activity concentration. Inter-system variations were defined as the range of RC over all systems. Results: RC decreased with decreasing sphere volume. Inter-system variations with vendor-specific reconstructions were between 0.06 and 0.41 for RCmean depending on sphere size (maximum 118{\%} quantification difference), and improved to 0.02–0.19 with vendor-neutral reconstructions (maximum 38{\%} quantification difference). Conclusion: This study shows that eliminating sources of possible variation drastically reduces inter-system variation in quantification. This means that absolute SPECT quantification for 177Lu is feasible in a multi-center and multi-vendor setting; however, close agreement between vendors and sites is key for multi-center dosimetry and quantitative biomarker studies.},
author = {Peters, Steffie M.B. and {Meyer Viol}, Sebastiaan L. and van der Werf, Niels R. and de Jong, Nick and van Velden, Floris H.P. and Meeuwis, Antoi and Konijnenberg, Mark W. and Gotthardt, Martin and de Jong, Hugo W.A.M. and Segbers, Marcel},
doi = {10.1186/s40658-020-0278-3},
issn = {21977364},
journal = {EJNMMI Physics},
number = {1},
pages = {13},
title = {{Variability in lutetium-177 SPECT quantification between different state-of-the-art SPECT/CT systems}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {7},
year = {2020}
}
@article{RN856,
abstract = {Purpose The widely known field ‘Radiomics' aims to provide an extensive image based phenotyping of e.g. tumors using a wide variety of feature values extracted from medical images. Therefore, it is of utmost importance that feature values calculated by different institutes follow the same feature definitions. For this purpose, the imaging biomarker standardization initiative (IBSI) provides detailed mathematical feature descriptions, as well as (mathematical) test phantoms and corresponding reference feature values. We present here an easy to use radiomic feature calculator, RaCaT, which provides the calculation of a large number of radiomic features for all kind of medical images which are in compliance with the standard. Methods The calculator is implemented in C++ and comes as a standalone executable. Therefore, it can be easily integrated in any programming language, but can also be called from the command line. No programming skills are required to use the calculator. The software architecture is highly modularized so that it is easily extendible. The user can also download the source code, adapt it if needed and build the calculator from source. The calculated feature values are compliant with the ones provided by the IBSI standard. Source code, example files for the software configuration, and documentation can be found online on GitHub (https://github.com/ellipfaehlerUMCG/RaCat). Results The comparison with the standard values shows that all calculated features as well as image preprocessing steps, comply with the IBSI standard. The performance is also demonstrated on clinical examples. Conclusions The authors successfully implemented an easy to use Radiomics calculator that can be called from any programming language or from the command line. Image preprocessing and feature settings and calculations can be adjusted by the user.},
author = {Pfaehler, Elisabeth and Zwanenburg, Alex and de Jong, Johan R. and Boellaard, Ronald},
doi = {10.1371/journal.pone.0212223},
issn = {19326203},
journal = {PLoS ONE},
number = {2},
title = {{RACAT: An open source and easy to use radiomics calculator tool}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061901064{\&}doi=10.1371{\%}2Fjournal.pone.0212223{\&}partnerID=40{\&}md5=3929eeb04d5b73fc66f936910e38e854},
volume = {14},
year = {2019}
}
@incollection{RN964,
abstract = {The Virtual Pediatric Airways Workbench (VPAW) is a patient-centered surgical planning software system targeted to pediatric patients with airway obstruction. VPAW provides an intuitive surgical planning interface for clinicians and supports quantitative analysis regarding prospective surgeries to aid clinicians deciding on potential surgical intervention. VPAW enables a full surgical planning pipeline, including importing DICOM images, segmenting the airway, interactive 3D editing of airway geometries to express potential surgical treatment planning options, and creating input files for offline geometric analysis and computational fluid dynamics simulations for evaluation of surgical outcomes. In this paper, we describe the VPAW system and its use in one case study with a clinician to successfully describe an intended surgery outcome.},
author = {Quammen, Cory W. and Taylor, Russell M. and Krajcevski, Pavel and Mitran, Sorin and Enquobahrie, Andinet and Superfine, Richard and Davis, Brad and Davis, Stephanie and Zdanski, Carlton},
booktitle = {Studies in Health Technology and Informatics},
doi = {10.3233/978-1-61499-625-5-295},
editor = {Fellander-Tsai, L and Vosburgh, K G and Westwood, J D and Senger, S and Westwood, S W and Fidopiastis, C M and Liu, A},
isbn = {9781614996248},
issn = {18798365},
keywords = {3D modeling,Surgery planning,Virtual reality},
pages = {295--300},
publisher = {IOS Press},
title = {{The virtual pediatric airways workbench}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84965026283{\&}doi=10.3233{\%}2F978-1-61499-625-5-295{\&}partnerID=40{\&}md5=a0854888010

license_type: permissive

branch_name: refs/heads/master

extension: bib