Scan-specific deep learning-based denoising method for low-field MR images

Motivation: Low SNR per unit-time in low-field MRI results in noisy images when targeting both clinically acceptable resolution and acquisition times which may limit their diagnostic effectiveness. Goal(s): We seek to improve low-field MRI SNR by means of deep-learning while overcoming the limitations of traditional supervised learning and without compromising denoising performance. Approach: We build on:1)self-supervised method enabling training without having to collect ‘noise-free’ data and 2)zero-shot concept to achieve dataset-free and scan-specific denoising.Additionally,we adopted simplified architecture for fast training times. Results: Our method showed high denoising performance for different SNR levels and contrasts within few seconds of processing time competing with well-established BM4D. Impact: Our proposed denoising method, based on self-supervised zero-shot deep-learning, enables high-performance denoising within short processing times. This approach shows promise for speedy acquisitions and enhanced imaged quality in low-field, point-of-care settings.