Dynamic cardiac magnetic resonance image reconstruction algorithm based on optimal low-rank constraints

Abstract:

Dynamic cardiac magnetic resonance imaging (CMRI) is an important tool for noninvasive assessment of cardiovascular disease. In dynamic CMRI, a low-rank tensor recovery method is usually employed to explore the sparsity of dynamic magnetic resonance images; however, different modes along the tensor have different low-rank properties. The studies have found that the nonlocal self-similarity mode along the tensor can best improve the reconstruction quality of dynamic CMRI. Therefore, this paper proposes an optimal low-rank matrix recovery (OLRMR) model with matrix sparsity based on the nonlocal low-rank (NLR) method by treating each set of similar blocks extracted from a high-dimensional image as a matrix. The model uses the weighted Schatten p-norm as the rank proxy function and was solved using the alternating direction multiplier method (ADMM) and a fast soft-threshold iterative algorithm. Experimental results based on the cardiac dataset show that the OLRMR algorithm is more effective in improving the quality of the reconstructed image than the BCS, k-t SLR, and k-t LRTC algorithms and can better keep the detail of the image and the edge contour information intact. The experimental results also show that OLRMR improves the reconstruction speed by a factor of 2.6-3 over k-t LRTC.