Investigating the stability of fast iterative shrinkage thresholding algorithm for MR imaging reconstruction using compressed sensing

Compressed sensing (CS) has been applied to the field of sub-sampled magnetic resonance imaging (MRI) reconstruction (CS-MRI). Fast iterative shrinkage-thresholding algorithm (FISTA) is an effective method for CS-MR images reconstruction. To investigate the accuracy and efficiency of the proposed algorithm, we applied it to the under-sampling MR images gained by different MRI scanning sequences. We found the peak signal to noise ratio (PSNR) of reconstructed MRI with varying sampling ratios diminished from Axial T1 weighted images (Ax T1) (45.13±12.97 dB), Axial T2 weighted images (Ax T2) (42.8 ± 14.95 dB), FLAIR (41.74 ± 14.15 dB), Diffusion Weighted Imaging (DWI) (40.23 ±17.40 dB) and Sagittal T2 weighted images (Sag T2) (36.28±12.32 dB), but there was no significant difference among the groups. In addition, the changes to the elapsed time of them was minor, Ax T1 (1.09±0.13 s), Ax T2 (1.30±0.13s), FLAIR (1.02±0.12s), DWI (1.07±0.13s) and Sag T2 (1.12±0.07s). Our results confirmed the stability of the proposed fast MRI reconstruction method for different scanning sequences. Further efforts are still needed to design the clinical sequence with sub-sampled acquisition strategy which may be a developing technique with clinical value.