Holistic Image Reconstruction for Diffusion MRI

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. V. Golkov ( ) • D. Cremers Department of Informatics, Technische Universität München, Garching, Germany e-mail: [email protected]; [email protected] J.M. Portegies Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands e-mail: [email protected] A. Golkov Department of Mathematics, Augsburg University, Augsburg, Germany e-mail: [email protected] R. Duits Department of Mathematics and Computer Science, and Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands e-mail: [email protected] © Springer International Publishing Switzerland 2016 A. Fuster et al. (eds.), Computational Diffusion MRI, Mathematics and Visualization, DOI 10.1007/978-3-319-28588-7_3 27