Variable Patch Dictionaries for efficient Compressed Sensing based MRI Reconstruction

Compressed Sensing (CS) is a novel framework that facilitates under-sampling of the Fourier space in Magnetic Resonance Imaging (MRI) acquisition without significant loss in image quality. Conventional techniques in Compressed Sensing Magnetic Resonance Imaging (CS-MRI) employ fixed analytic transforms as the sparsifying basis, which are able to sparsely represent only certain type of image features. Adaptive transforms learnt using dictionary learning (DL) techniques, have emerged as an interesting alternative, as they are tailored to a class of MR images. However, due to the complexity involved in the learning process, DL based techniques in MRI have been restricted to learning from small patches within the image, in an online fashion. In this work, a recently proposed dictionary learning framework called Trainlets has been extended to efficiently learn similar and higher order dictionaries from a vast database of MR images belonging to a particular scan type. Moreover, the proposed trainlets-based CS-MRI framework is designed to incorporate multiple offline-learned dictionaries corresponding to varying patch sizes, to adaptively denoise different regions in the image, which overcomes the degradation associated with choosing a fixed patch size. The proposed variable patch size CS-MRI scheme is shown to have superior performance with upto 5 dB improvement in terms of PSNR and good improvement in SSIM in each case, and achieve much faster reconstructions with respect to popular DL based CS-MRI schemes, even when the sampling percentage is higher.