HydraNet: a multi-branch convolutional neural network architecture for MRI denoising

Stephen Gregory; Hu Cheng; Sharlene Newman; Yu Gan

doi:10.1117/12.2582286

17 February 2021 HydraNet: a multi-branch convolutional neural network architecture for MRI denoising

Stephen Gregory, Hu Cheng, Sharlene Newman, Yu Gan

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 1159638 (2021) https://doi.org/10.1117/12.2582286
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

The state-of-the-art methods of Magnetic Resonance Imaging (MRI) denoising technologies have improved significantly in the past decade, particularly those based in deep learning. However, the major issues in deep learning based denoising algorithms is both that the model architectures are not built for the complex noise distributions inherent in MRI, and that the data given to these algorithms is typically synthetic, and thus, they fail to generalize to spatially variant noise distributions. The noise varies greatly dependent upon such factors as pulse sequence of the MRI sequence, reconstruction method, coil configuration, physiological activities, etc. To overcome these issues, we have created HydraNet, a multi-branch deep neural network architecture that learns to denoise MR images at a multitude of noise levels, and which has critically been trained using only real image pairs of high and low signal-to-noise ratio (SNR) images. We prove the superiority of HydraNet at denoising complex noise distributions in comparison to the leading deep learning method in our experimentation, in addition to non-local collaborative filtering-based methods, quantitatively in both Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and qualitatively upon inspection of denoised MRI samples.

Conference Presentation

Citation Download Citation

Stephen Gregory, Hu Cheng, Sharlene Newman, and Yu Gan "HydraNet: a multi-branch convolutional neural network architecture for MRI denoising", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 1159638 (17 February 2021); https://doi.org/10.1117/12.2582286

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