SNAC-DL: Self-Supervised Network for Adaptive Convolutional Dictionary Learning of MRI Denoising

Motivation: Low-Field MR (LF-MRI) offers greater accessibility and reduced sensitivity to susceptibility artifacts, but it suffers from low SNR. As a result, novel denoising techniques hold great promise to improve image quality and promote broader clinical applications of LF-MRI. Goal(s): This work introduces a novel MRI denoising technique that is based on self-supervised deep learning without requiring high SNR references. Approach: Our technique, called SNAC-DL, employs a Self-supervised Network for Adaptive Convolutional Dictionary Learning using a complex-valued coil-to-coil ($\mathbb{C}$C2C) training strategy. Results: SNAC-DL has been tested for lung MRI denoising at 0.55T to demonstrate efficient denoising while preserving the underlying image structure. Impact: The proposed denoising technique holds significant potential to improve image quality for LF-MRI. This is expected to facilitate the broad adoption of LF-MRI to improve cost-effectiveness and enable new clinical applications that are traditionally challenging at high field strengths.