Calibrationless joint compressed sensing reconstruction for rapid parallel MRI Abstract   3D magnetic resonance imaging (3D MRI) or multislice MRI involves significant data acquisition time. Traditionally, scanning rate of conventional MRI is restricted due to inherent physiological and instrumental limitations. In multislice parallel MRI (multislice pMRI) adjacent slices are highly correlated, so one can interpolate missing kspace data of any slice from its adjacent slices. Moreover, images corresponding to different coils are also highly correlated as they represent the same field of view (FoV) with different spatial sensitivity functions. Exploiting above redundancies in multislice pMRI, we propose a joint compressed sensing (CS) reconstruction model, which contains two joint sparsity promoting regularization terms based on wavelet forest sparsity and joint total variations (JTV). Extensive experiments are carried out for performance evaluations and compared with existing methods using real multislice pMRI datasets. Results in terms of CS reconstruction quality show significant improvements compared to the stateoftheart. Additionally, implementation of the proposed method is done using multicore CPU and GPGPU in a hybrid computing environment to study its clinical feasibility in terms of computational time.

Compressed Sensing: From Research to Clinical Practice With Deep Neural Networks: Shortening Scan Times for Magnetic Resonance Imaging Compressed sensing (CS) reconstruction methods leverage sparse structure in underlying signals to recover highresolution images from highly undersampled measurements. When applied to magnetic resonance imaging (MRI), CS has the potential to dramatically shorten MRI scan times, increase diagnostic value, and improve the overall patient experience. However, CS has several shortcomings that limit its clinical translation. These include 1) artifacts arising from inaccurate sparse modeling assumptions, 2) extensive parameter tuning required for each clinical application, and 3) clinically infeasible reconstruction times. Recently, CS has been extended to incorporate deep neural networks as a way of learning complex image priors from historical exam data. Commonly referred to as unrolled neural networks, these techniques have proven to be a compelling and practical approach to address the challenges of sparse CS. In this tutorial, we review the classical CS formulation and outline steps needed to transform this formulation into a deeplearningbased reconstruction framework. Supplementary opensource code in Python is used to demonstrate this approach with open databases. Further, we discuss considerations in applying unrolled neural networks in the clinical setting.