Toward effective noise reduction for sub-Nyquist high-frame-rate MRI techniques with deep learning

Cine Cardiac Magnetic Resonance (Cine-CMR) is one example of dynamic MRI approaches to image organs that exhibit periodic motion. Conventional routine clinical Cine-CMR are typically obtained at 20-35 frames per second (fps) with temporal window sizes of 40-50 milliseconds. We have recently shown the feasibility of significantly increasing this overall frame rate by an acquisition of MRI k-space using a highly optimized radial sampling pattern with respect to both spatial and temporal coverage. In brief, our proposed approach acquires a significantly undersampled radial MRI k-space while encoding spatially and temporally periodic noise characteristics through the undersampled radial MRI acquisition; however, remnant radial streaking noise remain under physiologic imaging conditions. In this research, we propose to further remove these streaking noise, employing a Spatio-Temporal Denoising Auto-Encoder (ST-DAE) based on deep learning. We evaluate performance of our method in addressing such remnant artifact using ST-DAE; PSNR is used to evaluate image quality, and computational time is also discussed.