Image Quality Improvements by adopting MR Phase Scrambling Fourier Transform Imaging in Deep Learning Reconstruction

Abstract

Deep learning has received much attention because of its excellent performance in image quality and reconstruction time. It was reported that image quality can be improved in MR compressed sensing (CS) by using the phase scrambling Fourier transform imaging (PSFT) that uses quadratic phase modulation to the subject. In this paper, an image reconstruction using Generic-ADMM-Net as a CNN image reconstruction was examined. Simulation studies showed that sharpness, preservation of structure and image contrast were improved compared to standard Fourier transform based CS-CNN or iterative image reconstruction method. These studies indicate that PSFT has the possibility to reconstruct higher quality images in deep learning image reconstruction as well as iterative reconstruction.