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检索条件"丛书名=Mathematics and Visualization"

共 253 条记录，以下是91-100 订阅

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Alzheimer's Disease Classification with Novel Microstructural Metrics from Diffusion-Weighted MRI

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MICCAI Workshop on Computational Diffusion MRI

作者： Nir, Talia M. Villalon-Reina, Julio E. Gutman, Boris A. Moyer, Daniel Jahanshad, Neda Dehghani, Morteza Jack, Clifford R., Jr. Weiner, Michael W. Thompson, Paul M. Univ Southern Calif Imaging Genet Ctr Los Angeles CA 90007 USA Univ Southern Calif Brain & Creat Inst Los Angeles CA USA Mayo Clin & Mayo Fdn Dept Radiol Rochester MN USA UCSF Sch Med Dept Radiol San Francisco CA USA

ISBN: (纸本)9783319285887;9783319285863

Alzheimer's disease (AD) deficits may be due in part to declining white matter (WM) integrity and disrupted connectivity. Numerous diffusion-weighted MRI (dMRI) studies of AD report WM deficits based on tensor model metrics. New microstructural measures derived from additional dMRI models may carry different information about WM microstructure including the geometry of diffusion anisotropy, diffusivity, complexity, estimated number of distinguishable fiber compartments, number of crossing fibers and neurite dispersion. Here we aimed to find the most helpful dMRI metrics and brain regions from a set of 17 dMRI-derived feature maps, to predict diagnostic group (AD or healthy control). The best metrics for classification were non-tensor metrics in the hippocampus and temporal lobes, areas consistently implicated in AD.

关键词： Tensors

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Crossing Versus Fanning: Model Comparison Using HCP Data

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MICCAI Workshop on Computational Diffusion MRI

作者： Ghosh, Aurobrata Alexander, Daniel Zhang, Hui UCL Ctr Med Image Comp London England

ISBN: (纸本)9783319285887;9783319285863

This paper assesses the importance of modelling fiber dispersion in brain regions with complex fiber configurations using a model comparison approach. It is well known that DTI, although popular, is insufficient for describing complex fiber configurations that exist in the brain-such as crossings, bendings and fannings. "Higher order" models have been proposed to overcome this limitation bymodelling crossings with greater accuracy and recent works have reported that up to 90% of white matter voxels contain crossings. However, since these models do not account for bending and fanning, i.e. dispersion, it is unknown if some fiber configurations are better explained by dispersion or by crossing (or by both). To address this problem, we take a model comparison approach on the publicly available state-of-the-art HCP dataset. We consider compartment based single fiber, crossing fiber and dispersion models, which are fitted to the data and ranked using several model selection and validation metrics, such as AIC, BIC and k-fold cross-validation. We generate maps of the brain based on these rankings which quantify the voxels where a single fiber or crossing or dispersion is the preferred model. The results show that 45-50% of the brain's parenchyma, including the white matter, are better explained by dispersion models, indicating the importance of modelling dispersion in addition to crossings.

关键词： Brain

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A Framework for Creating Population Specific Multimodal Brain Atlas Using Clinical T1 and Diffusion Tensor Images

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MICCAI Workshop on Computational Diffusion MRI

作者： Gupta, Vikash Malandain, Gregoire Ayache, Nicholas Pennec, Xavier Univ Southern Calif Imaging Genet Ctr Los Angeles CA 90007 USA INRIA Sophia Antipolis MORPHENE Team Sophia Antipolis France INRIA Sophia Antipolis ASCLEPIOS Project F-06902 Sophia Antipolis France

ISBN: (纸本)9783319285887;9783319285863

Spatial normalization is one of the most important steps in population based statistical analysis of brain images. This involves normalizing all the brain images to a pre-defined template or a population specific template. With multiple emerging imaging modalities, it is quintessential to develop a method for building a joint template that is a statistical representation of the given population across different modalities. It is possible to create different population specific templates in different modalities using existing methods. However, they do not give an opportunity for voxelwise comparison of different modalities. A multimodal brain template with probabilistic region of interest (ROI) definitions will give opportunity for multivariate statistical frameworks for better understanding of brain diseases. In this paper, we propose a methodology for developing such a multimodal brain atlas using the anatomical T1 images and the diffusion tensor images (DTI), along with an automated workflow to probabilistically define the different white matter regions on the population specific multimodal template. The method will be useful to carry out ROI based statistics across different modalities even in the absence of expert segmentation. We show the effectiveness of such a template using voxelwise multivariate statistical analysis on population based group studies on HIV/AIDS patients.

关键词： Population statistics

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On the Use of Antipodal Optimal Dimensionality Sampling Scheme on the Sphere for Recovering Intra-Voxel Fibre Structure in Diffusion MRI

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MICCAI Workshop on Computational Diffusion MRI

作者： Bates, Alice P. Khalid, Zubair Kennedy, Rodney A. Australian Natl Univ Res Sch Engn Canberra ACT 2601 Australia

ISBN: (纸本)9783319285887;9783319285863

In diffusion magnetic resonance imaging (dMRI), the diffusion signal can be reconstructed from measurements collected on single or multiple spheres in q-space using a spherical harmonic expansion. The number of measurements that can be acquired is severely limited and should be as small as possible. Previous sampling schemes have focused on using antipodal symmetry to reduce the number of samples and uniform sampling to achieve rotationally invariant reconstruction accuracy, but do not allow for an accurate or computationally efficient spherical harmonic transform (SHT). The recently proposed antipodal optimal dimensionality sampling scheme on the sphere requires the minimum number of samples, equal to the number of degrees of freedom for the representation of the antipodal symmetric band-limited diffusion signal in the spherical harmonic domain. In addition, it allows for the accurate and efficient computation of the SHT. In this work, we evaluate the use of this recently proposed scheme for the reconstruction of the diffusion signal and subsequent intra-voxel fibre structure estimation in dMRI. We show, through numerical experiments, that the use of this sampling scheme allows accurate and computationally efficient reconstruction of the diffusion signal, and improved estimation of intra-voxel fibre structure, in comparison to the antipodal electrostatic repulsion and spherical code sampling schemes with the same number of samples. We also demonstrate that it achieves rotationally invariant reconstruction accuracy to the same extent as the other two sampling schemes.

关键词： Harmonic analysis

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Computational Diffusion MRI 1st ed. 2016

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丛书名： mathematics and visualization

2016年

作者： Andrea Fuster Aurobrata Ghosh Enrico Kaden Yogesh Rathi Marco Reisert

ISBN: (数字)9783319285887

ISBN: (纸本)9783319285863

These Proceedings of the 2015 MICCAI Workshop Computational Diffusion MRI offer a snapshot of the current state of the art on a broad range of topics within the highly active and growing field of diffusion MRI. The topics varyfrom fundamental theoretical work on mathematical modeling, to the development and evaluation of robust algorithms, new computational methods applied to diffusion magnetic resonance imaging data, and applications in neuroscientific studies and clinical practice. Over the last decade interest in diffusion MRI has exploded. The technique provides unique insights into the microstructure of living tissue and enables in-vivo connectivity mapping of the brain. Computational techniques are key to the continued success and development of diffusion MRI and to its widespread transfer into clinical practice. New processing methods are essential for addressing issues at each stage of the diffusion MRI pipeline: acquisition, reconstruction, modeling and model fitting, image processing, fiber tracking, connectivity mapping, visualization, group studies and inference. This volume, which includes both careful mathematical derivations and a wealth of rich, full-color visualizations and biologically or clinically relevant results, offers a valuable starting point for anyone interested in learning about computational diffusion MRI and mathematical methods for mapping brain connectivity, as well as new perspectives and insights on current research challenges for those currently working in the field. It will be of interest to researchers and practitioners in the fields of computer science, MR physics, and applied mathematics.

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Accelerating Global Tractography Using Parallel Markov Chain Monte Carlo

引用

MICCAI Workshop on Computational Diffusion MRI

作者： Wu, Haiyong Chen, Geng Yang, Zhongxue Shen, Dinggang Yap, Pew-Thian Xiaozhuang Univ Key Lab Trusted Cloud Comp & Big Data Anal Nanjing Jiangsu Peoples R China Univ N Carolina Dept Radiol Chapel Hill NC 27599 USA Univ N Carolina BRIC Chapel Hill NC 27599 USA Northwestern Polytech Univ Data Proc Ctr Xian Peoples R China

ISBN: (纸本)9783319285887;9783319285863

Global tractography estimates brain connectivity by determining the optimal configuration of signal-generating fiber segments that best describes the measured diffusion-weighted data, promising better stability than local greedy methods with respect to imaging noise. However, global tractography is computationally very demanding and requires computation times that are often prohibitive for clinical applications. We present here a reformulation of the global tractography algorithm for fast parallel implementation amendable to acceleration using multicore CPUs and general-purpose GPUs. Our method is motivated by the key observation that each fiber segment is affected by a limited spatial neighborhood. That is, a fiber segment is influenced only by the fiber segments that are (or can potentially be) connected to its both ends and also by the diffusion-weighted signal in its proximity. This observation makes it possible to parallelize the Markov chain Monte Carlo (MCMC) algorithm used in the global tractography algorithm so that updating of independent fiber segments can be done concurrently. The experiments show that the proposed algorithm can significantly speed up global tractography, while at the same time maintain or improve tractography performance.

关键词： Program processors

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Alignment of Tractograms as Linear Assignment Problem

引用

MICCAI Workshop on Computational Diffusion MRI

作者： Sharmin, Nusrat Olivetti, Emanuele Avesani, Paolo Bruno Kessler Fdn NeuroInformat Lab NILab Trento Italy Univ Trento Ctr Mind & Brain Sci CIMeC Trento Italy

ISBN: (纸本)9783319285887;9783319285863

Diffusion magnetic resonance imaging (dMRI) offers a unique approach to study the structural connectivity of the brain. DMRI allows to reconstruct the 3D pathways of axons within the white matter as a set of polylines (streamlines), called the tractogram. Tractograms of different brains need to be aligned in a common representation space for various purposes, such as group-analysis, segmentation or atlas construction. Typically, such alignment is obtained with affine registration, through which tractograms are globally transformed, with the limit of not reconciling local differences. In this paper, we propose to improve registration-based alignment by what we call mapping. The goal of mapping is to find the correspondence between streamlines across brains, i. e. to find the map of which streamline in one tractogram correspond to which streamline in the other tractogram. We frame the mapping problem as a rectangular linear assignment problem (RLAP), a cornerstone of combinatorial optimization. We adopt a variant of the famous Hungarian method to get the optimal solution of the RLAP. We validate the proposed method with a tract alignment application, where we register two tractograms and, given one anatomical tract, we segment the corresponding one in the other tractogram. On dMRI data from the Human Connectome Project, we provide experimental evidence that mapping, implemented as a RLAP, can vastly improve both the true positive rate and false discovery rate of registration-based alignment, establishing a strong argument in favor of what we propose. We conclude by discussing the limitations of the current approach, which gives perspective for future work.

关键词： Mapping

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Adaptive Enhancement in Diffusion MRI Through Propagator Sharpening

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MICCAI Workshop on Computational Diffusion MRI

作者： Dela Haije, Tom Sepasian, Neda Fuster, Andrea Florack, Luc Eindhoven Univ Technol Dolech 2POB 513 NL-5600 MB Eindhoven Noord Brabant Netherlands

ISBN: (纸本)9783319285887;9783319285863

In this short note we consider a method of enhancing diffusion MRI data based on analytically deblurring the ensemble average propagator. Because of the Fourier relationship between the normalized signal and the propagator, this technique can be applied in a straightforward manner to a large class of models. In the case of diffusion tensor imaging, a commonly used 'ad hoc' min-normalization sharpening method is shown to be closely related to this deblurring approach. The main goal of this manuscript is to give a formal description of the method for (generalized) diffusion tensor imaging and higher order apparent diffusion coefficient-based models. We also show how the method can be made adaptive to the data, and present the effect of our proposed enhancement on scalar maps and tractography output.

关键词： Diffusion tensor imaging

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Angular Resolution Enhancement of Diffusion MRI Data Using Inter-Subject Information Transfer

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MICCAI Workshop on Computational Diffusion MRI

作者： Chen, Geng Zhang, Pei Li, Ke Wee, Chong-Yaw Wu, Yafeng Shen, Dinggang Yap, Pew-Thian Northwestern Polytech Univ Data Proc Ctr Xian Peoples R China Univ N Carolina Dept Radiol Chapel Hill NC 27599 USA Univ N Carolina BRIC Chapel Hill NC 27599 USA Beihang Univ Fundamental Sci Ergon & Environm Control Lab Beijing Peoples R China

ISBN: (纸本)9783319285887;9783319285863

Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brain connectivity. Diffusion axonal tracing, namely tractography, relies on local directional information provided by the orientation distribution functions (ODFs) estimated at each voxel. To accurately estimate ODFs, data of good signal-to-noise ratio and sufficient angular samples are desired, but unfortunately, are not always practically available. In this paper, we propose to improve ODF estimation by using inter-subject correlation. Specifically, diffusion-weighted images acquired from different subjects, when transformed to the space of a target subject, can not only provide signal denoising with additional information, but also drastically increase the number of angular samples for better ODF estimation. This is largely because of the incoherence of the angular samples generated when the diffusion signals are reoriented and warped to the target space. Experiments on both synthetic data and real data show that our method can reduce noise-induced artifacts, such as spurious ODF peaks, and yield more coherent orientations.

关键词： Diffusion

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Perspectives in shape analysis /

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2016年

作者： edited by Michael Breuß Alfred Bruckstein Petros Maragos Stefanie Wuhrer.

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