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

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978-3-030-52895-9Springer Nature Switzerland AG 2020

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1612-3786 usion 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 frontlin978-3-030-52895-9978-3-030-52893-5Series ISSN 1612-3786 Series E-ISSN 2197-666X

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Conference proceedings 2020tributions 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

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Current Challenges and Future Directions in Diffusion MRI: From Model- to Data- Driven Analysisective of diffusion MRI as a signal that is explained by a tractable biophysical model with one in which data driven machine learning can inform us about detection, localization, and assessment of both normal and abnormal brain tissue in both local (voxels) and global connectivity. Towards this end,

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Spatial Sparse Estimation of Fiber Orientation Distribution Using Deep Alternating Directions Methodssessed using standard tractography and automatic white matter analysis algorithms. Compared with the comparison method, the proposed method has good consistency in sparse fiber reconstruction and fiber continuity.

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