Fast Dictionary-Based Reconstruction for Diffusion Spectrum Imaging
B. Bilgic, I. Chatnuntawech, K. Setsompop, S. Cauley, A. Yendiki, L. Wald, E. Adalsteinsson
IEEE Trans Med Imaging, vol. 32, issue 11, November 2013
Diffusion spectrum imaging reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in qspace, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours. Our work presents dictionary-based techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The proposed methods achieve processing times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm. Matlab code is available at martinos.org/~berkin/software.html.