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检索条件"丛书名=Mathematics and Visualization"

共 253 条记录，以下是1-10 订阅

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In Situ visualization for Computational Science 1

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丛书名： mathematics and visualization

2022年

作者： Hank Childs Janine C. Bennett Christoph Garth

ISBN: (数字)9783030816278

ISBN: (纸本)9783030816261;9783030816292

This book provides an overview of the emerging field of in situ visualization, i.e. visualizing simulation data as it is generated. In situ visualization is a processing paradigm in response to recent trends in the development of high-performance computers. It has great promise in its ability to access increased temporal resolution and leverage extensive computational power. However, the paradigm also is widely viewed as limiting when it comes to exploration-oriented use cases. Furthermore, it will require visualization systems to become increasingly complex and constrained in usage. As research efforts on in situ visualization are growing, the state of the art and best practices are rapidly maturing.;Specifically, this book contains chapters that reflect state-of-the-art research results and best practices in the area of in situ visualization. Our target audience are researchers and practitioners from the areas of mathematics computational science, high-performance computing, and computer science that work on or with in situ techniques, or desire to do so in future.

关键词： visualization Computational Science and Engineering

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Stick Stippling for Joint 3D visualization of Diffusion MRI Fiber Orientations and Density

Stick Stippling for Joint 3D Visualization of Diffusion MRI ...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Cabeen, Ryan P. Laidlaw, David H. Toga, Arthur W. Univ Southern Calif Lab Neuro Imaging LONI USC Mark & Mary Stevens Neuroimaging & Informat I Keck Sch Med USC Los Angeles CA 90007 USA Brown Univ Dept Comp Sci Providence RI 02912 USA

ISBN: (纸本)9783030730185;9783030730178

We investigate a stick stippling approach for glyph-based visualization of neural fiber architecture derived from diffusion magnetic resonance imaging. The presence of subvoxel crossing fibers in the brain has prompted the development of advanced modeling techniques;however, there remains a need for improved visualization techniques to more clearly convey their complex structure. While tractography can illustrate large scale anatomy, visualization of diffusion models can provide a more complete picture of local anatomy without the known limitations of tracking. We identify challenges and evaluate techniques for visualizing multi-fiber models and identify benefits of a stick stippling technique relative to existing methods. We conducted experiments to compare these representations and evaluated them with in vivo diffusion MR datasets that vary in voxel resolution and anisotropy. We found that stick rendering as 3D tubes increased legibility of fiber orientation and that encoding fiber density by tube radius reduced clutter and reduced dependence on viewing orientation. Furthermore, we identified techniques to reduce the negative perceptual effects of voxel gridding through a jittering and resampling approach to produce a stippling effect. Looking forward, this approach provides a new way to explore diffusion MRI datasets that may aid in the visual analysis of white matter fiber architecture and microstructure. Our software implementation is available in the Quantitative Imaging Toolkit (QIT).

关键词： Diffusion MR imaging Fiber orientation distributions Fixels Glyphs Multi-fiber modeling Neuroimaging visualization

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Longitudinal Parcellation of the Infant Cortex Using Multi-modal Connectome Harmonics

Longitudinal Parcellation of the Infant Cortex Using Multi-m...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Taylor, Hoyt Patrick Ahmad, Sahar Wu, Ye Huynh, Khoi Minh Zhou, Zhen Wu, Zhengwang Lin, Weili Wang, Li Li, Gang Zhang, Han Yap, Pew-Thian Univ N Carolina Dept Radiol Chapel Hill NC 27515 USA Univ N Carolina Biomed Res Imaging Ctr BRIC Chapel Hill NC 27515 USA

ISBN: (纸本)9783030730185;9783030730178

Functional segregation and specialization of cortical regions is central to the significant changes that take place during early brain development. We present an automated scheme that harnesses local and long-range connectivity features of the cortex-derived from multiple imaging modalities-for longitudinal parcellation of the early developing human cortex. This is realized by using multi-modal connectome harmonics in a hierarchical agglomerative clustering (HAC) framework to produce group correspondent, individual-specific, and age-dependent parcellation maps for the first two years of development. We observed decreased regularity in cluster size and shape with increasing time, reflecting cortical specialization known to emerge during early development. Investigation of the modularity of structural connectivity defined by our parcellations suggests convergence toward adult resting-state networks as the brain develops.

关键词： Cortical parcellation Spectral graph analysis Structure and function

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Learning Anatomical Segmentations for Tractography from Diffusion MRI

Learning Anatomical Segmentations for Tractography from Diff...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Ewert, Christian Kugler, David Yendiki, Anastasia Reuter, Martin German Ctr Neurodegenerat Dis DZNE Bonn Germany Massachusetts Gen Hosp Athinoula A Martinos Ctr Biomed Imaging Boston MA 02114 USA Harvard Med Sch Dept Radiol Boston MA 02115 USA

ISBN: (纸本)9783030730185;9783030730178

Deep learning approaches for diffusion MRI have so far focused primarily on voxel-based segmentation of lesions or white-matter fiber tracts. A drawback of representing tracts as volumetric labels, rather than sets of streamlines, is that it precludes point-wise analyses of microstructural or geometric features along a tract. Traditional tractography pipelines, which do allow such analyses, can benefit from detailed whole-brain segmentations to guide tract reconstruction. Here, we introduce fast, deep learning-based segmentation of 170 anatomical regions directly on diffusion-weighted MR images, removing the dependency of conventional segmentation methods on T1-weighted images and slow pre-processing pipelines. Working natively in diffusion space avoids non-linear distortions and registration errors across modalities, as well as interpolation artifacts. We demonstrate consistent segmentation results between 0.70 and 0.87 Dice depending on the tissue type. We investigate various combinations of diffusion-derived inputs and show generalization across different numbers of gradient directions. Finally, integrating our approach to provide anatomical priors for tractography pipelines, such as TRACULA, removes hours of pre-processing time and permits processing even in the absence of high-quality T1-weighted scans, without degrading the quality of the resulting tract estimates.

关键词： Deep learning Diffusion MRI Segmentation Tractography

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Manifold-Aware CycleGAN for High-Resolution Structural-to-DTI Synthesis

Manifold-Aware CycleGAN for High-Resolution Structural-to-DT...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Anctil-Robitaille, Benoit Desrosiers, Christian Lombaert, Herve ETS Montreal Dept IT & Software Engn Montreal PQ Canada

ISBN: (纸本)9783030730185;9783030730178

Unpaired image-to-image translation has been applied successfully to natural images but has received very little attention for manifold-valued data such as in diffusion tensor imaging (DTI). The non-Euclidean nature of DTI prevents current generative adversarial networks (GANs) from generating plausible images and has mainly limited their application to diffusion MRI scalar maps, such as fractional anisotropy (FA) or mean diffusivity (MD). Even if these scalar maps are clinically useful, they mostly ignore fiber orientations and therefore have limited applications for analyzing brain fibers. Here, we propose amanifold-aware CycleGAN that learns the generation of high-resolution DTI from unpaired T1w images. We formulate the objective as a Wasserstein distance minimization problem of data distributions on a Riemannian manifold of symmetric positive definite 3 x 3 matrices SPD(3), using adversarial and cycle-consistency losses. To ensure that the generated diffusion tensors lie on the SPD(3) manifold, we exploit the theoretical properties of the exponential and logarithm maps of the Log-Euclidean metric. We demonstrate that, unlike standard GANs, our method is able to generate realistic high-resolution DTI that can be used to compute diffusion-based metrics and potentially run fiber tractography algorithms. To evaluate our model's performance, we compute the cosine similarity between the generated tensors principal orientation and their ground-truth orientation, the mean squared error (MSE) of their derived FA values and the Log-Euclidean distance between the tensors. We demonstrate that our method produces 2.5 times better FAMSE than a standard CycleGAN and up to 30% better cosine similarity than a manifold-aware Wasserstein GAN while synthesizing sharp high-resolution DTI.

关键词： High-resolution DTI Log-Euclidean metric Manifold-valued data learning

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Deep Learning Model Fitting for Diffusion-Relaxometry: A Comparative Study

Deep Learning Model Fitting for Diffusion-Relaxometry: A Com...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Grussu, Francesco Battiston, Marco Palombo, Marco Schneider, Torben Wheeler-Kingshott, Claudia A. M. Gandini Alexander, Daniel C. Univ Coll London UCL Fac Brain Sci Queen Sq Inst Neurol Queen Sq MS Ctr London England Univ Coll London UCL Fac Engn Sci Ctr Med Image Comp Dept Comp Sci London England Philips UK Guildford Surrey England DeepSpin GmbH Berlin Germany Univ Pavia Dept Brain & Behav Sci Pavia Italy IRCCS Mondino Fdn Brain MRI 3T Ctr Pavia Italy

ISBN: (纸本)9783030730185;9783030730178

Quantitative Magnetic Resonance Imaging (qMRI) signal model fitting is traditionally performed via non-linear least square (NLLS) estimation. NLLS is slow and its performance can be affected by the presence of different local minima in the fitting objective function. Recently, machine learning techniques, including deep neural networks (DNNs), have been proposed as robust alternatives to NLLS. Here we present a deep learning implementation of qMRI model fitting, which uses DNNs to perform the inversion of the forward signal model. We compare two DNN training strategies, based on two alternative definitions of the loss function, since at present it is not known which definition leads to the most accurate, precise and robust parameter estimation. In strategy 1 we define the loss as the l(2)-norm of tissue parameter prediction errors, while in strategy 2 as the l(2)-norm ofMRI signal prediction errors. We compare the two approaches on synthetic and 3T in vivo saturation inversion recovery (SIR) diffusion-weighted (DW) MRI data, using amodel for joint diffusion-T1 mapping. Strategy 1 leads to lower tissue parameter root mean squared errors (RMSEs) when realistic noise distributions are considered (e.g. Rician versus Gaussian). However, strategy 2 offers lower signal reconstruction RMSE, and allows training to be performed on both synthetic and actual in vivo MRI measurements. In conclusion, for the qMRI model considered here both strategies are valid choices for DNN-based fitting. Strategy 2 is more practical, as it does not require pre-computation of reference tissue parameters, but may lead to worse parameter estimation.

关键词： Deep learning Diffusion-relaxometry MRI Model fitting

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Beyond Lesion-Load: Tractometry-Based Metrics for Characterizing White Matter Lesions within Fibre Pathways

Beyond Lesion-Load: Tractometry-Based Metrics for Characteri...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Chamberland, Maxime Winter, Mia Brice, Thomas A. W. Jones, Derek K. Tallantyre, Emma C. Cardiff Univ Cardiff Univ Brain Res Imaging Ctr CUBRIC Sch Psychol Cardiff Wales Univ Hosp Wales Dept Clin Neuropsychol Cardiff Wales Cardiff Univ Div Psychol Med & Clin Neurosci Sch Med Cardiff Wales Univ Hosp Wales Helen Durham Ctr Neuroinflammat Cardiff Wales

ISBN: (纸本)9783030730185;9783030730178

In multiple sclerosis studies, lesion volume (or lesion load) derived from conventional T2 imaging correlates modestly with clinical assessment. Determining which specific white matter pathways are impacted by lesions may provide additional insights regarding task-specific clinical impairment. Using diffusion MRI, we introduce a set of tract-based metrics that go beyond traditional lesion load approaches and show how they relate to task performance (i.e., working memory, information processing and verbal fluency) in a cohort of 40 patients with multiple sclerosis.

关键词： Apparent fiber density Diffusion MRI Lesion load Multiple sclerosis Tractography Tractometry

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Repeatability of Soma and Neurite Metrics in Cortical and Subcortical Grey Matter

Repeatability of Soma and Neurite Metrics in Cortical and Su...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Genc, Sila Chamberland, Maxime Koller, Kristin Tax, Chantal M. W. Zhang, Hui Palombo, Marco Jones, Derek K. Cardiff Univ Cardiff Univ Brain Res Imaging Ctr Cubric Cardiff Wales Univ Coll London UCL Ctr Med Image Comp Dept Comp Sci London England Australian Catholic Univ Mary MacKillop Inst Hlth Res Melbourne Vic Australia

ISBN: (纸本)9783030730185;9783030730178

Diffusion magnetic resonance imaging is a technique which has long been used to study white matter microstructure in vivo. Recent advancements in hardware and modelling techniques have opened up interest in disentangling tissue compartments in the grey matter. In this study, we evaluate the repeatability of soma and neurite density imaging in a sample of six healthy adults scanned five times on an ultra-strong gradient magnetic resonance scanner (300 mT/m). Repeatability was expressed as an intraclass correlation coefficient (ICC). Our findings reveal that measures of soma density (mean ICC = 0.976), neurite density (mean ICC = 0.959) and apparent soma size (mean ICC = 0.923) are highly reliable across multiple cortical and subcortical networks. Overall, we demonstrate the promise of moving advanced grey matter microstructural imaging towards applications of development, ageing, and disease.

关键词： Grey matter Microstructure Neurite density Repeatability SANDI Soma density Soma radius

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Two Parallel Stages Deep Learning Network for Anterior Visual Pathway Segmentation

Two Parallel Stages Deep Learning Network for Anterior Visua...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Li, Siqi Chen, Zan Guo, Wenlong Zeng, Qingrun Feng, Yuanjing Zhejiang Univ Technol Coll Informat Engn Inst Informat Proc & Automat Hangzhou 310023 Peoples R China Zhejiang Prov United Key Lab Embedded Syst Hangzhou 310023 Peoples R China

ISBN: (纸本)9783030730185;9783030730178

The segmentation of the anterior visual pathway(AVP) from MRI sequences is challenging because of the thin long architecture, structural variations along the path, and poor contrast with adjacent anatomic structures. The AVP plays a critical role in many devastating pathological conditions (e.g., pituitary tumors and craniopharyngiomas). However, most of the existing methods segment AVP on T1w images merely and often fail to achieve good results that cannot meet clinical needs. In this work, we introduced fractional anisotropy(FA) images into the training data set and proposed a deep learning network with two parallel stages for AVP segmentation. On an MRI dataset consisting of 102 subjects selected from the Human Connectome Project (HCP), we demonstrate that the proposed framework consistently improves the accuracy of AVP segmentation.

关键词： Anterior visual pathway Deep learning Fractional anisotropy MRI Segmentation

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DW-MRI Microstructure Model of Models Captured Via Single-Shell Bottleneck Deep Learning

DW-MRI Microstructure Model of Models Captured Via Single-Sh...

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International Workshop on Computational Diffusion MRI (CDMRI)

作者： Nath, Vishwesh Ramadass, Karthik Schilling, Kurt G. Hansen, Colin B. Fick, Rutger Pathak, Sudhir K. Anderson, Adam W. Landman, Bennett A. Vanderbilt Univ Comp Sci 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Radiol 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Biomed Engn 221 Kirkland Hall Nashville TN 37235 USA Therapanacea Paris France Univ Pittsburgh Learning Res & Dev Ctr Pittsburgh PA USA

ISBN: (纸本)9783030730185;9783030730178

In the last two decades, numerous methods have been proposed to capture biological meaning from observed diffusion-weighted magnetic resonance imaging (DW-MRI) signals each addressing recovery of specific tissue properties (e.g. pathology based, volume fraction of tissues). Generically, specific tissue properties are recovered via a category of methods termed as tissue compartment modeling. Many recent compartmental approaches require two or more diffusivity shells. We hypothesize existence of a low dimension common representation for a wide variety of commonly used microstructural measures in common use. To test this hypothesis, we constructed 13 voxel-wise measurements from 5 distinct model-based approaches and used a multi-task deep convolutional network with a variable width bottleneck to infer these metrics from empirical DW-MRI. This is the first study to use data-driven exploration to map a common basis among DW-MRI modeling approaches. We propose to capture a compact feature space in the form of a bottleneck that preserves common features to all methods and retrieve information from single shell DW-MRI. We train on 3D patches of 40 Human Connectome Project (HCP) subjects (similar to 27 million patches) where input is based on single shell DW-MRI and ground truth is estimated from all three shells of HCP data. We validate on 24 subjects and test on 25 subjects. The error reported for 5 microstructure methods on test set: 4.0%, 2.7%, 6.3%, 4.5% and 3.6%. We find that 6 features in the bottleneck layer efficiently capture an intrinsic feature space for the range of DW-MRI metrics explored.

关键词： Bottleneck Deep Learning DW-MRI Microstructure

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