Optimized nonlinear conjugate gradient algorithm for MR imaging reconstruction using compressed sensing

Compressed sensing (CS) is an innovative theory of signal acquisition and processing based on the areas of applied mathematics. CS-based MRI exploits the sparsity of an image in an appropriate transform domain to reconstruct images from incoherently under-sampled k-space data. However, it has proven that CS-MRI suffers sharply loss of low-contrast image features with increasing reduction factors. In this work, we explored an optimized nonlinear conjugate gradient (NLCG) procedure aiming to improve peak signal to noise ratio (PSNR) of sub-sampled MRI liver T2 map and shorten the scan time markedly. Data processing and analysis were being done by the software of Matlab. Our findings indicate that using the proposed algorithm, at least 60% of the k-space data measurements necessitates for recovery. This study demonstrated the feasibility of the proposed CS approach to accelerate MRI T2 map. Further studies are needed to design the clinical sequence with sparse acquisition strategy which may be a developing technique with clinical value.