Computational Diffusion MRI (eBook, PDF)

13th International Workshop, CDMRI 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings
Redaktion: Cetin-Karayumak, Suheyla; Rheault, Francois; Powell, Elizabeth; Pieciak, Tomasz; Guevara, Pamela; Figini, Matteo; Christiaens, Daan

• Format: PDF

Produktbeschreibung

This book constitutes the proceedings of the International Workshop on Computational Diffusion MRI, CDMRI 2022, which was held 22 September 2022, in conjunction with MICCAI 2022.

The 12 full papers included were carefully reviewed and selected for inclusion in the book.

The papers were organized in topical sections as follows: Data processing, Signal representations, Tractography and WM pathways.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 149
• Erscheinungstermin: 1. Dezember 2022
• Englisch
• ISBN-13: 9783031212062
• Artikelnr.: 66772700

Inhaltsangabe

Data preprocessing.- Slice estimation in diffusion MRI of neonatal and fetal brains in image and spherical harmonics domains using autoencoders.- Super-resolution of manifold-valued diffusion MRI refined by multi-modal imaging.- Lossy compression of multidimensional medical images using sinusoidal activation networks: an evaluation study.- Correction of susceptibility distortion in EPI: a semi-supervised approach with deep learning.- The impact of susceptibility distortion correction protocols on adolescent diffusion MRI measures.- Signal representations.- Diffusion MRI Fibre Orientation Distribution Inpainting.- Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine Learning.- Stepwise Stochastic Dictionary Adaptation Improves Microstructure Reconstruction with Orientation Distribution Function Fingerprinting.- How can spherical CNNs benefit ML-based diffusion MRI parameter estimation?.- Tractography and WM pathways.- DC2U-Net: Tract Segmentation in Brain White Matter Using Dense Criss-Cross U-Net.- Clustering in Tractography using Autoencoders (CINTA).- Tractometric Coherence of Fiber Bundles in DTI.