Performance Evaluation of New Nonlocal Total Variation Noise Reduction Algorithm in Parallel Magnetic Resonance Imaging with Sensitivity Encoding Reconstruction

Abstract

Parallel magnetic resonance imaging (pMRI) can acquire high temporal resolution to obtain anatomical images. Among the parallel-imaging techniques, sensitivity encoding (SENSE) is the most widely used. During the SENSE process, they are previously limited by signal-to-noise ratio degradation and aliasing artifacts owing to the subsampling effect. Therefore, the objective of this study was to develop and evaluate a novel nonlocal total variation (new-NLTV) noise reduction algorithm in pMRI with SENSE reconstruction in both simulation and experiments. According to the results, the proposed algorithm was able to achieve impressive results using quantitative evaluation factors in simulation and real phantom images. The contrast-to-noise ratio and coefficient of variation for the algorithm, in particular, were 8.24 and 7.15 times better, respectively, than those of the noisy image in the phantom study. In conclusion, this study successfully demonstrated the effectiveness of the new-NLTV noise reduction algorithm in pMRI with SENSE reconstruction.