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Abstract #4605

Noise-Adaptive MRI Denoising Using Self-Supervised Learning with Average-to-Average (Avg2Avg) Loss

Nikola Janjusevic1,2, Mary Bruno1,2, Yuhui Huang1,2, Jingjia Chen1,2, Yao Wang3, Hersh Chandarana1,2, and Li Feng1,2
1Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University Grossman School of Medicine, New York, NY, United States, 2Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, New York, NY, United States, 3Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, NY, United States

Synopsis

Keywords: New Signal Preparation Schemes, Low-Field MRI, noise-adaptive

Motivation: Low-field MRI scanners offer improved accessibility but are plagued by low-SNR, often requiring averaging of scans which prolongs scan times and reduces accessibility.

Goal(s): To introduce SNAPER, a framework which accelerates the conventional low-SNR MRI acquisition pipeline (scan averaging) via scan denoising.

Approach: SNAPER proposes the collection of two imaging averages, and trains a noise-adaptive denoising neural network with an average-to-average training. After training, averages are combined before denoising to yield improved image quality.

Results: We validate on synthetically contaminated fully-sampled and 2x GRAPPA-reconstructed data. On 0.55T T2-weighted accelerated Prostate image data, preliminary results indicate SNAPER can achieve 4x acceleration by denoising.

Impact: The proposed denoising technique could greatly encourage the use of 0.55T MRI and other low-SNR MRI scanners, making imaging more affordable and accessible.

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Keywords