Noise2Recon: A Semi-Supervised Framework for Joint MRI Reconstruction and Denoising

• URL: http://arxiv.org/abs/2110.00075v1
• Date: Thu, 30 Sep 2021 20:06:43 GMT
• Title: Noise2Recon: A Semi-Supervised Framework for Joint MRI Reconstruction and Denoising
• Authors: Arjun D Desai, Batu M Ozturkler, Christopher M Sandino, Shreyas Vasanawala, Brian A Hargreaves, Christopher M Re, John M Pauly, Akshay S Chaudhari
• Abstract summary: We propose a semi-supervised, consistency-based framework (termed Noise2Recon) for joint MR reconstruction and denoising. Our method enables the usage of a limited number of fully-sampled and a large number of undersampled-only scans. Results demonstrate that even with minimal ground-truth data, Noise2Recon (1) achieves high performance on in-distribution (low-noise) scans and (2) improves generalizability to OOD, noisy scans.
• Score: 7.121980163238901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, standard supervised DL methods depend on extensive amounts of fully-sampled ground-truth data and are sensitive to out-of-distribution (OOD) shifts, in particular for low signal-to-noise ratio (SNR) acquisitions. To alleviate this challenge, we propose a semi-supervised, consistency-based framework (termed Noise2Recon) for joint MR reconstruction and denoising. Our method enables the usage of a limited number of fully-sampled and a large number of undersampled-only scans. We compare our method to augmentation-based supervised techniques and fine-tuned denoisers. Results demonstrate that even with minimal ground-truth data, Noise2Recon (1) achieves high performance on in-distribution (low-noise) scans and (2) improves generalizability to OOD, noisy scans.

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