Holistic image reconstruction for diffusion MRI

Vladimir Golkov, Jorg M. Portegies, Antonij Golkov, Remco Duits, Daniel Cremers

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


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. Recent methods tackle this challenge by performing super-resolution reconstruction in image space or in diffusion space, regularization of the image data or of postprocessed data (such as the orientation distribution function, ODF) along different dimensions, and/or impose data-consistency in the original acquisition space. Each of these techniques has its own advantages; however, it is rare that even a few of them are combined. Here we present a holistic framework for diffusion MRI reconstruction that allows combining the advantages of all these techniques in a single reconstruction step. In proof-of-concept experiments, we demonstrate super-resolution on HARDI shells and in image space, regularization of the ODF and of the images in spatial and angular dimensions, and data consistency in the original acquisition space. Reconstruction quality is superior to standard reconstruction, demonstrating the feasibility of combining advanced techniques into one step.

Original languageEnglish
Title of host publicationComputational Diffusion MRI - MICCAI Workshop, 2015
EditorsYogesh Rathi, Andrea Fuster, Aurobrata Ghosh, Enrico Kaden, Marco Reisert
PublisherSpringer Heidelberg
Number of pages13
ISBN (Print)9783319285863
StatePublished - 2016
EventWorkshop on Computational Diffusion MRI, MICCAI 2015 - Munich, Germany
Duration: 9 Oct 20159 Oct 2015

Publication series

NameMathematics and Visualization
ISSN (Print)1612-3786
ISSN (Electronic)2197-666X


ConferenceWorkshop on Computational Diffusion MRI, MICCAI 2015


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