Abstract #1773

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

Arjun D Desai^1,2, Batu M Ozturkler¹, Christopher M Sandino³, Brian A Hargreaves^2,3,4, John M Pauly^3,5, and Akshay S Chaudhari^2,5,6

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

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), particularly low-SNR, data. In this work, we propose a semi-supervised, consistency-based framework (termed Noise2Recon) for joint MR reconstruction and denoising that uses a limited number of fully-sampled references. Results demonstrate that even with minimal ground-truth data, Noise2Recon can use unsupervised, undersampled data to 1) achieve high performance on in-distribution (noise-free) scans and 2) improve generalizability to noisy, OOD scans compared to both standard and augmentation-based supervised methods.

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