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 complaints of the 2015 MICCAI Workshop “Computational Diffusion MRI” provide a picture of the present state-of-the-art on a large variety of themes in the hugely lively and starting to be box of diffusion MRI. the subjects differ from basic theoretical paintings on mathematical modeling, to the improvement and overview of sturdy algorithms, new computational equipment utilized to diffusion magnetic resonance imaging info, and purposes in neuroscientific stories and medical practice.

over the past decade curiosity in diffusion MRI has exploded. The method offers precise insights into the microstructure of dwelling tissue and allows in-vivo connectivity mapping of the mind. Computational recommendations 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 matters at each one degree of the diffusion MRI pipeline: acquisition, reconstruction, modeling and version becoming, photograph processing, fiber monitoring, connectivity mapping, visualization, staff reports and inference.

This quantity, together with either cautious mathematical derivations and a wealth of wealthy, full-color visualizations and biologically or clinically appropriate effects, deals a important place to begin for an individual attracted to studying approximately computational diffusion MRI and mathematical tools for mapping mind connectivity, in addition to new views and insights on present study demanding situations for these presently operating within the box. it is going to be of curiosity to researchers and practitioners within the fields of machine technological know-how, 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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2 Image Preprocessing Images were preprocessed as in [24]. To summarize, raw DWI images were corrected for motion and eddy current distortions, while T1-weighted images underwent inhomogeneity normalization. Extra-cerebral tissue was removed from both scan types. Each T1-weighted anatomical image was linearly aligned to a standard brain template (the down-sampled Colin27 [25]: 110 110 110, with 2 mm isotropic voxels). The diffusion images were linearly and then elastically registered [26] to their respective T1-weighted structural scans to correct for echoplanar imaging (EPI) induced susceptibility artifacts.

In recent years a surge of new models have been proposed to overcome some of the tensor model limitations, including multi-tensor models, such as the tensor distribution function (TDF) [11], q-ball imaging and the orientation distribution function (ODF) [12], constrained spherical deconvolution [13], the “ball and stick” model [7], diffusion spectrum MRI (DSI) [14], and neurite orientation dispersion and density imaging (NODDI) [15], among many others. Not only do these models allow for the reconstruction of multiple underlying fibers, but they may give a richer understanding of the underlying structure than FA does, by providing information on diffusivity, complexity, estimated numbers of fiber compartments, number of crossing fibers and neurite dispersion.

The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013) 20. : 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.

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