Arjun D Desai1,2, Batu M Ozturkler1, Christopher M Sandino3, Brian A Hargreaves2,3,4, John M Pauly3,5, and Akshay S Chaudhari2,5,6
1Electrical Engineering (Equal Contribution), Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States, 3Electrical Engineering, Stanford University, Stanford, CA, United States, 4Bioengineering, Stanford University, Stanford, CA, United States, 5Equal Contribution, Stanford University, Stanford, CA, United States, 6Biomedical Data Science, Stanford University, Stanford, CA, United States
1Electrical Engineering (Equal Contribution), Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States, 3Electrical Engineering, Stanford University, Stanford, CA, United States, 4Bioengineering, Stanford University, Stanford, CA, United States, 5Equal Contribution, Stanford University, Stanford, CA, United States, 6Biomedical Data Science, Stanford University, Stanford, CA, United States
We propose Noise2Recon, a semi-supervised, deep-learning framework for accelerated MR reconstruction and denoising. Despite limited training data, Noise2Recon reconstructs both high and low-SNR scans with higher fidelity than supervised DL algorithms.
Fig. 1: Noise2Recon complements supervised training (blue pathway) with unsupervised training (red pathway) by enforcing network consistency between reconstructions of both unsupervised data and their noise-augmented counterparts. Unsupervised data is augmented by a noise map ε=U*F*N(0,σ), where σ is selected uniformly at random and the undersampling operator U is determined from the undersampled k-space. Scans with fully-sampled references follow the supervised training paradigm. The total loss is the weighted sum of the supervised (Lsup) and consistency (Lcons) losses.
Fig. 4: Reconstruction performance for 12x (top) and 16x (bottom) undersampled scans corrupted at varying noise levels (σ). Supervised models were trained with one (solid) or fourteen (dashed) supervised scans. Noise2Recon outperformed both 1-subject supervised methods at all noise levels. Noise2Recon achieved similar nRMSE and pSNR as noise-augmented supervised training but outperformed the latter in SSIM. Noise2Recon image quality metrics had low sensitivity to increasing σ and acceleration, which may indicate higher reconstruction robustness in noisy settings.