Denoising very low field magnetic resonance images using native noise modeling and deep learning

Motivation: Low-field MRI (LF-MRI) can increase accessibility in low-income countries where high-field MRI is not available due to cost, power and siting requirements. However, noise significantly affects LF-MR image quality. Goal(s): This study aims to enhance the signal-to-noise ratio (SNR) in very LF-MRI (0.05T) images using native noise modeling and deep learning. Approach: We extracted noise from 0.05T phantom MRI images, modeled it, added it to high-field brain MRI (1.5T & 3T), trained two deep-learning algorithms, and evaluated them on in vivo brain MRI images. Results: Our approach improves the SNR of in-vivo LF images by a factor of approximately two. Impact: Using native noise while developing deep-learning denoising algorithms for LF-MRI images is better than using synthetic random noise. As a result, the developed algorithms are more explainable and follow domain knowledge on noise in LF-MRI improving trust in the models.