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

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

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

over the past decade curiosity in diffusion MRI has exploded. The approach offers specific insights into the microstructure of residing tissue and permits in-vivo connectivity mapping of the mind. Computational concepts are key to the ongoing good fortune and improvement of diffusion MRI and to its frequent move into scientific perform. New processing tools are crucial for addressing concerns at every one degree of the diffusion MRI pipeline: acquisition, reconstruction, modeling and version becoming, picture processing, fiber monitoring, connectivity mapping, visualization, workforce experiences and inference.

This quantity, together with either cautious mathematical derivations and a wealth of wealthy, full-color visualizations and biologically or clinically correct effects, bargains a necessary start line for a person attracted to studying approximately computational diffusion MRI and mathematical tools for mapping mind connectivity, in addition to new views and insights on present learn demanding situations for these presently operating within the box. will probably be of curiosity to researchers and practitioners within the fields of computing device technology, MR physics, and utilized mathematics.​

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Additional info for Computational Diffusion MRI: MICCAI Workshop, Munich, Germany, October 9th, 2015

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J. Inverse Ill-Posed Prob. 21(3), 355–377 (2013) 19. : The WU-Minn Human Connectome Project: an overview. NeuroImage 80, 62–79 (2013) 20. : Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS ONE 5(12), e15710 (2010) 21. : Improving diffusion MRI using simultaneous multi-slice echo planar imaging. NeuroImage 63(1), 569–580 (2012) 22. : Characterizing tumor response to chemotherapy at various length scales using temporal diffusion spectroscopy. PLoS ONE 7(7), e41714 (2012) 23.

The few that do, exclusively use the DTI model: several have used voxel-wise features derived from full DTI maps, using methods such as Pearson correlation and ReliefF for feature reduction [21–23], yielding accuracies of more than 90 %. In [49] tractography-based connectivity metrics based on fiber count, FA, and diffusivity were used for SVM classification, reporting an accuracy of 88 %, although clearly these accuracies depend on the problem and dataset used, and are not directly comparable with each other.

2 Holistic Reconstruction Our goal is to reconstruct the image magnitude r and phase ' from the acquired data d. In order to improve image quality, such a reconstruction should include state-of-the-art image processing methods, such as denoising, super-resolution reconstruction and orientation distribution function1 (ODF) enhancement. Rather than performing this in a classical manner, where each step is performed separately, we couple all transformations and regularizers into a single optimization problem.

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