Highly undersampling dynamic cardiac MRI based on low-rank tensor coding

Abstract

Dynamic cardiac magnetic resonance imaging (CMRI) is an important tool for the non-invasive assessment of cardiovascular disease. However, dynamic CMRI suffers from long acquisition times due to the need of obtaining images with high temporal and spatial resolution, whole-heart coverage. Conventionally, a multidimensional dataset in dynamic CMRI is treated as a series of two-dimensional matrices, and then various matrix/vector transforms are used to explore the sparsity of MR images. In this paper, we propose a low-rank tensor coding (LRTC) model with tensor sparsity for the application of compressive sensing (CS) in dynamic CMRI. In this framework, each group of 3D similar patches extracted from high-dimensional images is considered to be a low-rank tensor. LRTC can better capture the sparse part of dynamic CMRI and make full use of the redundancy between the feature vectors of adjacent positions. ADMM technique is introduced to tackle the proposed model, where soft threshold operator is used to solving the l1 norm relaxation. Higher-order singular value decomposition (HOSVD) is exploited to process high-dimensional tensors and mine correlations in space-time dimensions. Validations based on cardiac cine and myocardial perfusion datasets indicate that the proposed method achieved comparable reconstruction accuracy with the low-rank matrix recovery methods, and outperformed the conventional sparse recovery methods.