Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning
Year: 2021
Type: preprint
Abstract: Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD) shifts, particularly low signal-to-noise ratio (SNR) acquisitions. To alleviate this challenge, we propose Noise2Recon, a model-agnostic, consistency training method for joint MRI reconstruction and denoising that can use both fully-sampled (labeled) and undersampled (unlabeled) scans in semi-supervised and self-supervised settings. With limited or no labeled training data, Noise2Recon outperforms compressed sensing and deep learning baselines, including supervised networks, augmentation-based training, fine-tuned denoisers, and self-supervised methods, and matches performance of supervised models, which were trained with 14x more fully-sampled scans. Noise2Recon also outperforms all baselines, including state-of-the-art fine-tuning and augmentation techniques, among low-SNR scans and when generalizing to other OOD factors, such as changes in acceleration factors and different datasets. Augmentation extent and loss weighting hyperparameters had negligible impact on Noise2Recon compared to supervised methods, which may indicate increased training stability. Our code is available at https://github.com/ad12/meddlr.
Source: arXiv (Cornell University)
Institution: Stanford University
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: AF: Medium: Collaborative Research: Beyond Sparsity: Refined Measures of Complexity for Linear Algebra, SHF: Medium: Collaborative Research: From Volume to Velocity: Big Data Analytics in Near-Realtime, G2911450482, Development and Validation of Radiation-Free Pediatric Renal Function Quantification, Center for Advanced Magnetic Resonance Technology at Stanford, G4292887491, G4945478376, Graduate Research Fellowship Program (GRFP), G5925649794, Quantitative 3D Diffusion and Relaxometry MRI of the Knee, G6661425536, Rapid Robust Pediatric MRI, Advanced MR Imaging of Early Osteoarthritis, G8751954850, G9000112150, Designing Novel MR Imaging Tools to Quantify Lower-Limb Exercise Adaptations in Knee Osteoarthritis
OpenAlex ID: W4298201891