Deep learning enabled MRI general denoising at 0.55T

Motivation: Recent advancements in 0.55T MRI systems present promising opportunities for affordable and accessible MRI. Enhancing SNR to mitigate the inherent limitations of mid field strength is a crucial step in advancing this technology. Goal(s): In this study, we aim to advance 0.55T MRI for speed and quality through a deep-learning-driven general denoise method processing low-SNR scans of various body parts and sequences. Approach: We constructed a model with a spatial-temporal attention mechanism and employed massive complex image data for training. Results: The proposed method significantly improves low SNR single-repetition images at 0.55T, making the results comparable or superior to the averages of multi-repetitions. Impact: With robust denoising on mid-field systems, enhanced image quality and quicker scans can be expected for more accurate diagnoses and improved patient experience. New sequences can be developed and paired to further advance the system.