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Titlebook: Computational Diffusion MRI; International MICCAI Noemi Gyori,Jana Hutter,Fan Zhang Conference proceedings 2021 The Editor(s) (if applicabl

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41#
發(fā)表于 2025-3-28 15:22:41 | 只看該作者
Learning Anatomical Segmentations for Tractography ?from Diffusion MRIck 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 g
42#
發(fā)表于 2025-3-28 21:44:17 | 只看該作者
43#
發(fā)表于 2025-3-28 23:31:56 | 只看該作者
44#
發(fā)表于 2025-3-29 06:46:33 | 只看該作者
Q-Space Quantitative Diffusion MRI Measures Using a Stretched-Exponential Representationiffusion representation is related to the restricted diffusion and can provide information about the underlying tissue properties. In this paper, we analytically derive .th order statistics of the signal considering a stretched-exponential representation of the diffusion. Then, we retrieve the Q-spa
45#
發(fā)表于 2025-3-29 08:59:26 | 只看該作者
46#
發(fā)表于 2025-3-29 14:00:14 | 只看該作者
DW-MRI Microstructure Model of Models Captured Via Single-Shell Bottleneck Deep Learning (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
47#
發(fā)表于 2025-3-29 18:07:09 | 只看該作者
Deep Learning Model Fitting for Diffusion-Relaxometry: A Comparative Studyslow 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 qMR
48#
發(fā)表于 2025-3-29 23:02:13 | 只看該作者
Pretraining Improves Deep Learning Based Tissue Microstructure Estimations of the brain. Due to the constraint of imaging time, the quality of dMRI scans can be limited by the number of diffusion gradients and the spatial resolution, and deep learning based approaches have been developed to provide high-quality estimation of tissue microstructure from the low-quality dif
49#
發(fā)表于 2025-3-30 03:06:46 | 只看該作者
50#
發(fā)表于 2025-3-30 04:05:41 | 只看該作者
Computational Diffusion MRI978-3-030-73018-5Series ISSN 1612-3786 Series E-ISSN 2197-666X
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