Noise-induced Variability Quantification in Deep Learning-Based MRI Reconstructions

Motivation: While noise propagation in linear imaging methods like SENSE is well-studied, similar analysis is not common in deep learning-based MRI reconstructions. Goal(s): Evaluate, characterize, and compare noise propagation in deep learning-based and linear MRI reconstruction under varying conditions. Approach: We uses Monte Carlo simulations to empirically analyze mean and variance of knee MRI images reconstructed by SENSE and deep learning methods. Results: SENSE yields unstructured, relatively-uniform noise distribution, while deep learning methods produce anatomically structured noise with substantial variability across tissues, acceleration factors, and noise levels. Noise-aware deep learning reconstruction shows more uniform noise propagation and reduced tissue-specific variability. Impact: Elucidating noise propagation in deep MRI reconstructions could direct algorithm refinement, optimizing image quality and reliability for clinical application. Simply reconstructing noise-propagation maps in routine protocols may help in image interpretation.