Diffusion-weighted and spectroscopic MRI super-resolution using sparse representations

Abstract

Diffusion-weighted magnetic resonance imaging (DW-MRI) and spectroscopic MRI (MRSI) are powerful diagnostic imaging tools as they provide complementary information over conventional MRI. Imaging is done at a low-resolution (LR) as the scanning time for high-resolution (HR) MR images would be very long and not practical besides being expensive for imaging. In this paper, we propose a novel single image super-resolution (SISR) scheme to improve spatial resolution of DW and MRS images. It is based on patch-wise sparse reconstruction of HR patches from LR feature patches utilizing a pair of learned overcomplete dictionaries. Reconstruction not only exploits the sparsity of MR image but also utilize the non-local self-similarity of patches of the input LR image as prior knowledge. Experiments are done using magnitude images of DW-MRI and MRSI along with a synthetic image. Performance evaluations based on different matrices besides visual analysis are carried out to validate and compare the obtained results with the state-of-the-art. It is observed that the proposed method clearly outperforms recent methods in terms of both quantitative and visual analysis. Finally, the proposed algorithm is also implemented using the GP-GPU based parallel hardware along with sequential implementations in order to showcase its potential for real clinical applications.