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 becoming box of diffusion MRI. the themes fluctuate from primary theoretical paintings on mathematical modeling, to the advance and evaluate of strong algorithms, new computational equipment utilized to diffusion magnetic resonance imaging info, and functions in neuroscientific reports and medical practice.

over the past decade curiosity in diffusion MRI has exploded. The approach offers exact insights into the microstructure of dwelling tissue and permits in-vivo connectivity mapping of the mind. Computational ideas are key to the ongoing luck and improvement of diffusion MRI and to its common move into medical perform. New processing equipment are crucial for addressing concerns at every one degree of the diffusion MRI pipeline: acquisition, reconstruction, modeling and version becoming, photo processing, fiber monitoring, connectivity mapping, visualization, crew reports and inference.

This quantity, which include either cautious mathematical derivations and a wealth of wealthy, full-color visualizations and biologically or clinically suitable effects, deals a precious place to begin for someone attracted to studying approximately computational diffusion MRI and mathematical tools for mapping mind connectivity, in addition to new views and insights on present examine demanding situations for these presently operating within the box. it is going to be of curiosity to researchers and practitioners within the fields of desktop technological know-how, MR physics, and utilized mathematics.​

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

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NeuroImage 80, 105–124 (2013) 25. : FSL. NeuroImage 62(2), 782–790 (2012) 26. : FreeSurfer. NeuroImage 62(2), 774–781 (2012) 27. : Validation of diffusion spectrum magnetic resonance imaging with manganese-enhanced rat optic tracts and ex vivo phantoms. NeuroImage 19, 482–495 (2003) 28. : Use of spin echoes in a pulsed magnetic-field gradient to study anisotropic, restricted diffusion and flow. J. Chem. Phys. 43(10), 3597–3603 (1965) 29. : An algorithm for minimizing the MumfordShah functional.

TDFAxD (grey) was more prevalent as the “top metric” toward the internal bundled structures. Even though all of the dMRI measures described are correlated with each other, each captures the microstructure in a slightly different way. Furthermore, clusters of surviving voxels from the hierarchical regularized logistic model were all non-tensor metric clusters largely in the temporal lobes and hippocampus (Fig. 3). While these regions are often implicated in AD pathology, the hippocampus is not a coherent WM structure, thus requiring a non-tensor model to pick up differences.

Advances in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage 80, 125–143 (2013) 21. : Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004) Holistic Image Reconstruction for Diffusion MRI Vladimir Golkov, Jorg M. Portegies, Antonij Golkov, Remco Duits, and Daniel Cremers Abstract Diffusion MRI provides unique information on the microarchitecture of biological tissues. One of the major challenges is finding a balance between image resolution, acquisition duration, noise level and image artifacts.

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