Noise-Robust Reconstruction for Accelerated MRI using Contrastive Learning

Motivation: Deep learning-based accelerated MRI reconstruction methods have shown outstanding performance but do not consider noise. Corruption due to noise may lead to wrong diagnosis in clinical practices. Goal(s): Propose a noise-robust reconstruction method, which reconstructs noise-free full-sampled images from noisy undersampled data. Approach: A noise-robust reconstruction method is proposed using contrastive learning framework consisting of two stages. The first stage extracts feature representations related to the noise level, which is used in the second stage to reconstruct alias-free image. Results: Experiment results show that the proposed method provides robust reconstruction with limited training data, yielding superior image reconstruction compared to other reconstruction methods. Impact: The encoder trained in the first stage extracts representation features that contain content-invariant noise level information. Therefore, the trained encoder can be applied to other downstream tasks with limited amount of training data.