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Abstract #3392

Noise2Noise MRI for High-resolution Diffusion-weighted Imaging of the Brain: Deep Learning-based denoising without need for Highly Averaged Ground-truth Images

Motohide Kawamura1, Daiki Tamada1, Satoshi Funayama1, Hiroshi Onishi1, and Utaroh Motosugi1

1Department of Radiology, University of Yamanashi, Chuo-shi, Japan

Deep learning (DL)-based denoising is promising to achieve high resolution diffusion-weighted imaging (HR-DWI) by improving SNR without signal averaging. Training supervised DL-based algorithm, however, requires thousands of teaching data, which need long acquisition time. In this study, we propose to use noise2noise (N2N) theory to develop DL-based denoising algorithm, which does not need teaching data with high SNR. In the results, the proposed method (N2N-MRI-based algorithm) outperformed conventional ground-truth-based algorithm in terms of maximum peak SNRs on validation sets during training. The image quality of HR-DWI denoised by N2N-MRI-based algorithm was equivalent to that denoised by conventional algorithm.

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