Abstract #4811

Improving the accessibility of deep learning-based denoising for MRI using transfer learning and self-supervised learning

Qiyuan Tian¹, Ziyu Li², Wei-Ching Lo³, Berkin Bilgic¹, Jonathan R. Polimeni¹, and Susie Y. Huang¹

¹Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States, ²Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ³Siemens Medical Solutions, Charlestown, MA, United States

Synopsis

The requirement for high-SNR reference data for training reduces the practical feasibility of supervised deep learning-based denoising. This study improves the accessibility of deep learning-based denoising for MRI using transfer learning that only requires high-SNR data of a single subject for fine-tuning a pre-trained convolutional neural network (CNN), or self-supervised learning that can train a CNN using only the noisy image volume itself. The effectiveness is demonstrated by denoising highly accelerated (R=3×3) Wave-CAIPI T1w MPRAGE images. Systematic and quantitative evaluation shows that deep learning without or with very limited high-SNR data can achieve high-quality image denoising and brain morphometry.

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