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

Stephen Gregory, Hu Cheng, Sharlene Newman, Yu Gan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

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.

Original languageEnglish
Title of host publicationMedical Imaging 2021
Subtitle of host publicationImage Processing
EditorsIvana Isgum, Bennett A. Landman
ISBN (Electronic)9781510640214
DOIs
StatePublished - 2021
EventMedical Imaging 2021: Image Processing - Virtual, Online, United States
Duration: 15 Feb 202119 Feb 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11596
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2021: Image Processing
Country/TerritoryUnited States
CityVirtual, Online
Period15/02/2119/02/21

Keywords

  • Convolutional Neural Network
  • Denoising
  • MRI
  • Patch-based
  • Residual

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