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Titlebook: Computational Diffusion MRI; MICCAI Workshop, She Elisenda Bonet-Carne,Jana Hutter,Fan Zhang Conference proceedings 2020 Springer Nature Sw

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發(fā)表于 2025-3-21 18:13:52 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computational Diffusion MRI
副標(biāo)題MICCAI Workshop, She
編輯Elisenda Bonet-Carne,Jana Hutter,Fan Zhang
視頻videohttp://file.papertrans.cn/233/232240/232240.mp4
概述Contributions on new important topics that are gaining momentum within the diffusion MRI community.Careful mathematical derivations and large number of rich full-color visualizations.Biologically or c
叢書名稱Mathematics and Visualization
圖書封面Titlebook: Computational Diffusion MRI; MICCAI Workshop, She Elisenda Bonet-Carne,Jana Hutter,Fan Zhang Conference proceedings 2020 Springer Nature Sw
描述.This volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI 2019), held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), which took place in Shenzhen, China on October 17, 2019. .This book presents the latest advances in the rapidly expanding field of diffusion MRI. It shares new perspectives on the latest research challenges for those currently working in the field, but also offers a valuable starting point for anyone interested in learning about computational techniques in diffusion MRI. The book includes rigorous mathematical derivations, a wealth of rich, full-colour visualisations and extensive clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics. Readers will find contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the .in vivo. recovery of microstructural and connectivity features, as well as diffusion-relaxometry and frontlin
出版日期Conference proceedings 2020
關(guān)鍵詞diffusion MRI; multidimensional diffusion MRI; combined diffusion-relaxometry MRI; computational techni
版次1
doihttps://doi.org/10.1007/978-3-030-52893-5
isbn_softcover978-3-030-52895-9
isbn_ebook978-3-030-52893-5Series ISSN 1612-3786 Series E-ISSN 2197-666X
issn_series 1612-3786
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

書目名稱Computational Diffusion MRI影響因子(影響力)




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Sabine Dittrich,Ilse Jürgenliemkng the inner product of signals, a closed form expression is obtained, which allows its computation using spherical harmonics from a reduced set of acquired data, compatible with most popular diffusion MRI acquisition protocols. Results show that the proposed metric (1) is able to discriminate among
板凳
發(fā)表于 2025-3-22 01:57:10 | 只看該作者
https://doi.org/10.1007/978-3-322-84746-1etic resonance imaging. CSD models the diffusion-weighted signal as the convolution of a fiber orientation distribution function and a “single fiber response function”, representing the signal profile of a population of aligned fibers. The performance of CSD relies crucially on the robust and accura
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Manfred Bornhofen,Martin C. Bornhofenneuro- and body imaging. The promise of micro-scale analyses has been in the creation of . that provide information in place of physical histology, while tractography and its related methods offer maps of the neuronal wiring through .. While both approaches have had strong successes at the group lev
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Manfred Bornhofen,Martin C. Bornhofeniber tractography. Both are impacted by the free-water partial volume effect that arises at the border of cerebrospinal fluid or in presence of vasogenic edema. Hence, in order to robustly track white matter fibers close to cerebrospinal fluid and in presence of edema, or to extract consistent bioma
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Manfred Bornhofen,Martin C. Bornhofenrocess is estimating fiber orientation distribution?(FOD), often done from a model such as constrained spherical deconvolution?(CSD). Multi-shell?(MS) multi-tissue CSD?(M-CSD) provides a robust WM FOD?by estimating the relative contribution to the dMRI?signal from each tissue type (WM, grey matter,
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