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检索条件"丛书名=Mathematics and Visualization"

共 253 条记录，以下是31-40 订阅

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Edge Weights and Network Properties in Multiple Sclerosis

Edge Weights and Network Properties in Multiple Sclerosis

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21st International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) / 8th Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM) / International Workshop on Computational Diffusion MRI (CDMRI)

作者： Powell, Elizabeth Prados, Ferran Chard, Declan Toosy, Ahmed Clayden, Jonathan D. Wheeler-Kingshott, Claudia Gandini A. M. UCL Dept Med Phys & Biomed Engn London England UCL Fac Brain Sci Inst Neurol MS Ctr Queen Sq London England UCL Ctr Med Image Comp Dept Med Phys & Bioengn London England Univ Coll London Hosp Natl Inst Hlth Res Biomed Res Ctr London England UCL Great Ormond St Inst Child Hlth Dev Imaging & Biophys Sect London England Univ Pavia Dept Brain & Behav Sci Pavia Italy IRCCS Mondino Fdn Brain MRI 3T Ctr Pavia Italy

ISBN: (纸本)9783030058319;9783030058302

Graph theory is able to provide quantitative parameters that describe structural and functional characteristics of human brain networks. Comparisons between subject populations have demonstrated topological disruptions in many neurological disorders;however interpreting network parameters and assessing the extent of the damage is challenging. The abstraction of brain connectivity to a set of nodes and edges in a graph is non-trivial, and factors from image acquisition, post-processing and network construction can all influence derived network parameters. We consider here the impact of edge weighting schemes in a comparative analysis of structural brain networks, using healthy control and relapsing-remitting multiple sclerosis subjects as test groups. We demonstrate that the choice of edge property can substantially affect inferences of network disruptions in disease, ranging from 'primarily intact connectivity' to 'complete disruption'. Although study design should predominantly dictate the choice of edge weight, it is important to consider how study outcomes may be affected.

关键词： Graph theory Network Edge weight Graph property Connectivity Permutation Multiple sclerosis

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Return-to-Axis Probability Calculation from Single-Shell Acquisitions

Return-to-Axis Probability Calculation from Single-Shell Acq...

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作者： Aja-Fernandez, Santiago Tristan-Vega, Antonio Molendowska, Malwina Pieciak, Tomasz de Luis-Garcia, Rodrigo Univ Valladolid ETS Ingenieros Telecomunicac LPI Valladolid Spain AGH Univ Sci & Technol Krakow Poland

ISBN: (纸本)9783030058319;9783030058302

The Ensemble Average diffusion Propagator (EAP) provides relevant microstructural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like the Diffusion Tensor. The direct estimation of the EAP requires a dense sampling of the q-space data. Although alternative techniques have been proposed, all of them require a high number of gradients and several b-values to be calculated. Once the EAP is calculated scalar measures must be directly derived. In this work, we propose a method to drastically reduce the number of points needed for the estimation of one of the measures, the return-to-axis probability (RTAP), efficiently estimating the q-space diffusion measure from a single shell acquisition. The proposal avoids the calculation of the EAP assuming that the diffusion does not depend on the radial direction. By applying this assumption locally, we achieve closed-form expressions of the measure using information from only one b-value, compatible with acquisitions protocols used for HARDI. Results have shown that the measures are highly correlated with the same measures calculated with state-of-the-art EAP estimators and highly accelerated execution times.

关键词： Diffusion MRI RTAP Ensemble average diffusion propagator Microstructure HARDI

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Effects of Diffusion MRI Model and Harmonization on the Consistency of Findings in an International Multi-cohort HIV Neuroimaging Study

Effects of Diffusion MRI Model and Harmonization on the Cons...

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作者： Nir, Talia M. Lam, Hei Y. Ananworanich, Jintanat Boban, Jasmina Brew, Bruce J. Cysique, Lucette Fouche, J. P. Kuhn, Taylor Porges, Eric S. Law, Meng Paul, Robert H. Thames, April Woods, Adam J. Valcour, Victor G. Thompson, Paul M. Cohen, Ronald A. Stein, Dan J. Jahanshad, Neda Univ Southern Calif Imaging Genet Ctr Mark & Mary Stevens Neuroimaging & Informat Inst Marina Del Rey CA 90292 USA Thai Red Cross Aids Res Ctr HIV NAT Bangkok Thailand Univ Novi Sad Fac Med Diagnost Imaging Ctr Novi Sad Serbia St Vincents Hosp Dept Neurol Sydney NSW Australia Univ New South Wales Sydney NSW Australia Univ Cape Town Dept Psychiat & Mental Hlth Cape Town South Africa Univ Calif Los Angeles Semel Inst Neurosci & Human Behav Los Angeles CA 90024 USA Univ Florida Sch Med Inst Aging Dept Aging & Geriatr Res Gainesville FL USA Univ Southern Calif Keck Sch Med Dept Radiol Los Angeles CA USA Univ Missouri Missouri Inst Mental Hlth St Louis MO 63121 USA Univ Southern Calif Dept Psychol Los Angeles CA USA Univ Calif San Francisco Dept Neurol Memory & Aging Ctr San Francisco CA USA Brown Univ Dept Psychiat & Human Behav Warren Alpert Med Sch Providence RI 02912 USA

ISBN: (纸本)9783030058319;9783030058302

HIV-related white matter (WM) differences reported across studies are inconsistent. This is due to clinical and demographic heterogeneity of HIV infected populations, and variations in diffusion MRI (dMRI) acquisition, processing, and analysis methods across studies. Therefore, reliable neuroanatomical consequences of infection and therapeutic targets are difficult to identify. Here, we pooled data from six existing HIV studies from around the world as part of the ENIGMA-HIV consortium to evaluate (1) the effects of harmonization of dMRI measures across sites using ComBat, and (2) whether an improved, higher-order tensor dMRI model, the tensor distribution function (TDF), and derived scalar index (FATDF) conferred higher sensitivity across heterogeneous sites to understand the effect of HIV on WM microstructure. This study suggests that improved dMRI indices and harmonization of these measures across cohorts, may be helpful for detecting consistent effects of disease on the brain in international multi-site studies, while preserving biological differences.

关键词： HIV Multi-site Harmonization TDF Diffusion MRI ComBat DTI

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Deterministic Group Tractography with Local Uncertainty Quantification

Deterministic Group Tractography with Local Uncertainty Quan...

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作者： Holm, Andreas Nugaard Feragen, Aasa Dela Haije, Tom Darkner, Sune Univ Copenhagen Dept Comp Sci Copenhagen Denmark

ISBN: (纸本)9783030058319;9783030058302

While tractography is routinely used to trace the white-matter connectivity in individual subjects, the population analysis of tractography output is hampered by the difficulty of comparing populations of curves. As a result, analysis is often reduced to population summaries such as TBSS, or made pointwise with similar interaction of remote and nearby tracts. As an easy-to-use alternative, we propose population-wide tractography in MNI space, by simultaneously considering diffusion data from the entire population, registered to MNI. We include voxel-wise quantification of population variability as a measure of uncertainty. The group tractography algorithm is illustrated on a population of subjects from the Human Connectome Project, obtaining robust population estimates of the white matter tracts.

关键词： Tractography Population analysis Uncertainty quantification

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Obtaining Representative Core Streamlines for White Matter Tractometry of the Human Brain

Obtaining Representative Core Streamlines for White Matter T...

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作者： Chamberland, Maxime St-Jean, Samuel Tax, Chantal M. W. Jones, Derek K. Cardiff Univ CUBRIC Sch Psychol Cardiff S Glam Wales Univ Med Ctr Utrecht Ctr Image Sci Dept Radiol Utrecht Netherlands Australian Catholic Univ Mary McKillop Inst Hlth Res Sydney NSW Australia

ISBN: (纸本)9783030058319;9783030058302

Diffusion MRI infers information about the micro-structural architecture of the brain by probing the diffusion of water molecules. The process of virtually reconstructing brain pathways based on these measurements is called tractography. Various metrics can be mapped onto pathways to study their micro-structural properties. Tractometry is an along-tract profiling technique that often requires the extraction of a representative streamline for a given bundle. This is traditionally computed by local averaging of the spatial coordinates of the vertices, and constructing a single streamline through those averages. However, the resulting streamline can end up being highly non-representative of the shape of the individual streamlines forming the bundle. In particular, this occurs when there is variation in the topology of streamlines within a bundle (e.g., differences in length, shape or branching). We propose an envelope-based method to compute a representative streamline that is robust to these individual differences. We demonstrate that this method produces a more representative core streamline, which in turn should lead to more reliable and interpretable tractometry analyses.

关键词： Tractography Tractometry Bundle envelope Core streamline Diffusion MRI

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Spherical Harmonic Residual Network for Diffusion Signal Harmonization

Spherical Harmonic Residual Network for Diffusion Signal Har...

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作者： Koppers, Simon Bloy, Luke Berman, Jeffrey, I Tax, Chantal M. W. Edgar, J. Christopher Merhof, Dorit Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany Childrens Hosp Philadelphia Dept Radiol Philadelphia PA 19104 USA Cardiff Univ CURBRIC Cardiff S Glam Wales

ISBN: (纸本)9783030058319;9783030058302

Diffusion imaging is an important method in the field of neuroscience, as it is sensitive to changes within the tissue microstructure of the human brain. However, a major challenge when using MRI to derive quantitative measures is that the use of different scanners, as used in multi-site group studies, introduces measurement variability. This can lead to an increased variance in quantitative metrics, even if the same brain is scanned. Contrary to the assumption that these characteristics are comparable and similar, small changes in these values are observed in many clinical studies, hence harmonization of the signals is essential. In this paper, we present a method that does not require additional preprocessing, such as segmentation or registration, and harmonizes the signal based on a deep learningresidual network. For this purpose, a training database is required, which consist of the same subjects, scanned on different scanners. The results show that harmonized signals are significantly more similar to the ground truth signal compared to no harmonization, but also improve in comparison to another deep learning method. The same effect is also demonstrated in commonly used metrics derived from the diffusion MRI signal.

关键词： Harmonization Diffusion imaging Magnetic resonance imaging Machine learning Deep learning SHResNet

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q-Space Learning with Synthesized Training Data

q-Space Learning with Synthesized Training Data

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作者： Ye, Chuyang Cui, Yue Li, Xiuli Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China Chinese Acad Sci Brainnetome Ctr Inst Automat Beijing Peoples R China Deepwise Inc Beijing Peoples R China

ISBN: (纸本)9783030058319;9783030058302

q-Space learning has been developed to improve tissue microstructure estimation on diffusion magnetic resonance imaging (dMRI) scans when only a limited number of diffusion gradients are applied. However, the training samples for q-space learning are obtained from high-quality diffusion signals densely sampled in the q-space, which are acquired on the same scanner of the test scans with a large number of diffusion gradients, and they may not be available for existing or ongoing datasets. In this work, we explore q-space learning with synthesized training data so that it can be applied to datasets where training signals are not available. We seek to synthesize diffusion signals densely sampled in the q-space, whose corresponding undersampled signals should match the distribution of observed undersampled diffusion signals. Specifically, by drawing samples from a simple distribution and feeding them into a generator defined by a multiple layer perceptron, we synthesize the continuous SHORE signal representation, from which both densely sampled and undersampled synthesized diffusion signals can be computed. The weights in the generator are learned by minimizing the distribution difference, which is measured by the maximum mean discrepancy, between the synthesized and observed undersampled signals. In addition, regularization terms are added to discourage unrealistic synthetic signals. With the learned generator, densely sampled diffusion signals can be synthesized for q-space learning. The proposed approach was applied to microstructure estimation on dMRI scans acquired with a limited number of diffusion gradients. The results demonstrate the benefit of using synthetic training signals for q-space learning when actual training data are not acquired.

关键词： Diffusion MRI q-space learning Training data synthesis

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A Framework for Calculating Time-Efficient Diffusion MRI Protocols for Anisotropic IVIM and An Application in the Placenta

A Framework for Calculating Time-Efficient Diffusion MRI Pro...

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作者： Slator, Paddy J. Hutter, Jana Ianus, Andrada Panagiotaki, Eleftheria Rutherford, Mary A. Hajnal, Joseph, V Alexander, Daniel C. UCL Ctr Med Image Comp London England UCL Dept Comp Sci London England Kings Coll London Ctr Developing Brain London England Kings Coll London Biomed Engn Dept London England

ISBN: (纸本)9783030058319;9783030058302

We develop a framework for calculating clinically-viable diffusion MRI (dMRI) protocols for anisotropic IVIM modelling. The proposed multi-stage framework combines previous approaches to dMRI protocol optimisation: first optimising b-values by minimizing Cramer-Rao lower bounds on parameter variances, and subsequently optimising gradient directions jointly to provide maximum angular coverage across all shells. This removes unnecessary measurements of closely spaced b-values with the same gradient directions, which encode very similar information, and hence reduces the total number of dMRI measurements. We applied the framework to establish an organ-specific, data-driven, set of optimised b-values and gradient directions for dMRI of the placenta. The optimised protocol leads to higher contrast-to-noise ratios in parameter maps compared to a naive protocol of comparable scan time. Applying this framework in other organs has the potential to reduce scanning times required for anisotropic IVIM modelling.

关键词： Diffusion MRI IVIM Anisotropic IVIM Placenta Microstructural modelling Experimental design Cramer-Rao lower bound

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Supervised Classification of White Matter Fibers Based on Neighborhood Fiber Orientation Distributions Using an Ensemble of Neural Networks

Supervised Classification of White Matter Fibers Based on Ne...

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作者： Ugurlu, Devran Firat, Zeynep Ture, Ugur Unal, Gozde Sabanci Univ Istanbul Turkey Yeditepe Univ Hosp Istanbul Turkey Istanbul Tech Univ Istanbul Turkey

ISBN: (纸本)9783030058319;9783030058302

White matter fibers constitute the main information transfer network of the brain and their accurate digital representation and classification is an important goal of neuroscience image computing. In current clinical practice, the reconstruction of desired fibers generally involves manual selection of regions of interest by an expert, which is time-consuming and subject to user bias, expertise and fatigue. Hence, automation of the process is desired. To that end, we propose a supervised classification approach that utilizes an ensemble of neural networks. Each streamline is represented by the fiber orientation distributions in its neighborhood, while the resolved fiber orientations are obtained by generalized q-sampling imaging (GQI) and a subsequent diffusion decomposition method. In order to make the supervised fiber classification succeed in a real scenario where a substantial portion of reconstructed fiber tracts contain spurious fibers, we present a way to create an "invalid" class label through a dedicated training set creation scheme with an ensemble of networks. The performance of the proposed classification method is demonstrated on major fiber pathways in the brainstem. 30 subjects from Human Connectome Project (HCP)'s publicly available "WU-Minn 500 Subjects + MEG2 dataset" are used as the dataset.

关键词： Ensemble neural networks Supervised white matter fiber classification Brain fiber pathways

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Longitudinal Harmonization for Improving Tractography in Baby Diffusion MRI

Longitudinal Harmonization for Improving Tractography in Bab...

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作者： Khoi Minh Huynh Kim, Jaeil Chen, Geng Wu, Ye Shen, Dinggang Pew-Thian Yap Univ N Carolina Biomed Engn Dept Chapel Hill NC 27515 USA Univ N Carolina Dept Radiol Chapel Hill NC 27515 USA Univ N Carolina BRIC Chapel Hill NC 27515 USA

ISBN: (纸本)9783030058319;9783030058302

The human brain develops very rapidly in the first years of life, resulting in significant changes in water diffusion anisotropy. Developmental changes pose significant challenges to longitudinally consistent white matter tractography. In this paper, we will introduce a method to harmonize infant diffusion MRI data longitudinally across time. Specifically, we harmonize diffusion MRI data collected at an earlier time point to data collected at a later time point. This will promote longitudinal consistency and allow sharpening of fiber orientation distribution functions (ODFs) based on information available at the later time point. For this purpose, we will introduce an approach that is based on the method of moments, which allows harmonization to be performed directly on the diffusion-attenuated signal without the need to fit any diffusion models to the data. Given two diffusion MRI datasets, our method harmonizes them voxel-wise using well-behaving mapping functions (i.e., monotonic, diffeomorphic, etc.), parameters of which are determined by matching the spherical moments (i.e., mean, variance, skewness, etc.) of signal measurements on each shell. The mapping functions we use is isotropic and does not introduce new orientations that are not already in the original data. Our analysis indicates that longitudinal harmonization sharpens ODFs and improves tractography in infant diffusion MRI.

关键词： Longitudinal harmonization Diffusion MRI Method of moments Tractography

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