Motion Adaptive Patch-Based Low-Rank Approach for Compressed Sensing Cardiac Cine MRI

Abstract

One of the technical challenges in cine magnetic resonance imaging (MRI) is to reduce the acquisition time to enable the high spatio-temporal resolution imaging of a cardiac volume within a short scan time. Recently, compressed sensing approaches have been investigated extensively for highly accelerated cine MRI by exploiting transform domain sparsity using linear transforms such as wavelets, and Fourier. However, in cardiac cine imaging, the cardiac volume changes significantly between frames, and there often exist abrupt pixel value changes along time. In order to effectively sparsify such temporal variations, it is necessary to exploit temporal redundancy along motion trajectories. This paper introduces a novel patch-based reconstruction method to exploit geometric similarities in the spatio-temporal domain. In particular, we use a low rank constraint for similar patches along motion, based on the observation that rank structures are relatively less sensitive to global intensity changes, but make it easier to capture moving edges. A Nash equilibrium formulation with relaxation is employed to guarantee convergence. Experimental results show that the proposed algorithm clearly reconstructs important anatomical structures in cardiac cine image and provides improved image quality compared to existing state-of-the-art methods such as k-t FOCUSS, k-t SLR, and MASTeR.

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

“…Another group of techniques known as low-rank completion methods uses the idea that the background of the image can be separated mathematically from dynamic portions 58,88–91 . If only a few dynamic components are required to represent signal changes, the signal is sparse in this representation.…”

“…These possible blocks are determined either empirically or using a priori information such as a set of similar images. These techniques can use 2D 70,71 or 3D blocks 72,73 , and can even be combined with time course modeling methods for further sparsification 58,69 .…”

Nonrigid groupwise registration for motion estimation and compensation in compressed sensing reconstruction of breath‐hold cardiac cine MRI

“…A nonlinear conjugate gradient algorithm with backtracking line search (27) has been used to solve the registration problem in Eq. [12]. Additional fixed control points were placed outside the image domain in order to guarantee that the spatial transformation is properly defined inside the domain.…”

Accelerated dynamic MRI using patch regularization for implicit motion compensation

“…Similarly, regional low‐rank constraint, coupled with deformation estimation, was introduced in to account for contrast variations in myocardial perfusion MRI. This approach is similar to the patch‐based low‐rank method introduced for breath‐held cardiac cine MRI . A generalized deformation compensated compressed sensing (DC‐CS) scheme capable to include a variety of penalties (e.g.…”