A Compressed-Sensing Approach for Super-Resolution Reconstruction of Diffusion MRI


We present an innovative framework for reconstructing high-spatial-resolution diffusion magnetic resonance imaging (dMRI) from multiple low-resolution (LR) images. Our approach combines the twin concepts of compressed sensing (CS) and classical super-resolution to reduce acquisition time while increasing spatial resolution. We use subpixel-shifted LR images with down-sampled and non-overlapping diffusion directions to reduce acquisition time. The diffusion signal in the high resolution (HR) image is represented in a sparsifying basis of spherical ridgelets to model complex fiber orientations with reduced number of measurements. The HR image is obtained as the solution of a convex optimization problem which can be solved using the proposed algorithm based on the alternating direction method of multipliers (ADMM). We qualitatively and quantitatively evaluate the performance of our method on two sets of in-vivo human brain data and show its effectiveness in accurately recovering very high resolution diffusion images.

DOI: 10.1007/978-3-319-19992-4_5

Cite this paper

@article{Ning2015ACA, title={A Compressed-Sensing Approach for Super-Resolution Reconstruction of Diffusion MRI}, author={Lipeng Ning and Kawin Setsompop and Oleg V. Michailovich and Nikos Makris and Carl-Fredrik Westin and Yogesh Rathi}, journal={Information processing in medical imaging : proceedings of the ... conference}, year={2015}, volume={24}, pages={57-68} }