Download Computational Diffusion MRI: MICCAI Workshop, Munich, by Andrea Fuster, Aurobrata Ghosh, Enrico Kaden, Yogesh Rathi, PDF

By Andrea Fuster, Aurobrata Ghosh, Enrico Kaden, Yogesh Rathi, Marco Reisert

These lawsuits of the 2015 MICCAI Workshop “Computational Diffusion MRI” provide a photograph of the present cutting-edge on a wide variety of themes in the hugely energetic and starting to be box of diffusion MRI. the themes differ from basic theoretical paintings on mathematical modeling, to the advance and assessment of strong algorithms, new computational equipment utilized to diffusion magnetic resonance imaging information, and functions in neuroscientific reviews and scientific practice.

during the last decade curiosity in diffusion MRI has exploded. The process offers precise insights into the microstructure of dwelling tissue and permits in-vivo connectivity mapping of the mind. Computational recommendations are key to the continuing luck and improvement of diffusion MRI and to its frequent move into scientific perform. New processing tools are crucial for addressing concerns at each one level of the diffusion MRI pipeline: acquisition, reconstruction, modeling and version becoming, photograph processing, fiber monitoring, connectivity mapping, visualization, staff experiences and inference.

This quantity, together with either cautious mathematical derivations and a wealth of wealthy, full-color visualizations and biologically or clinically suitable effects, deals a important start line for an individual attracted to studying approximately computational diffusion MRI and mathematical equipment for mapping mind connectivity, in addition to new views and insights on present learn demanding situations for these at the moment operating within the box. will probably be of curiosity to researchers and practitioners within the fields of laptop technological know-how, MR physics, and utilized mathematics.​

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Extra resources for Computational Diffusion MRI: MICCAI Workshop, Munich, Germany, October 9th, 2015

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Reson. Med. 68(5), 1450–1457 (2012) 4. : Comparison of sampling strategies and sparsifying transforms to improve compressed sensing diffusion spectrum imaging. Magn. Reson. Med. 73, 401–416 (2015) 5. : Integrated image reconstruction and gradient nonlinearity correction. Magn. Reson. Med. 74(4), 1019–1031 (2015) 6. : Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn. Reson.

Non-local MRI upsampling. Med. Image Anal. 14, 784–792 (2010) 10. : Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions. Med. Image Anal. 16, 1465–1476 (2012) 11. : A compressedsensing approach for super-resolution reconstruction of diffusion MRI. In: Information Processing in Medical Imaging, pp. 57–68. Springer 12. : Image quality transfer via random forest regression: applications in diffusion MRI. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014, pp.

Reson. Med. 72, 707–717 (2014) 7. : Fast reconstruction for multichannel compressed sensing using a hierarchically semiseparable solver. Magn. Reson. Med. 73, 1034–1040 (2015) 8. : Acceleration of high angular and spatial resolution diffusion imaging using compressed sensing with multichannel spiral data. Magn. Reson. Med. 73, 126–138 (2015) 9. : Multi-shell diffusion signal recovery from sparse measurements. Med. Image Anal. 18(7), 1143–1156 (2014) 10. : Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions.

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