TY - GEN
T1 - HydraNet
T2 - Medical Imaging 2021: Image Processing
AU - Gregory, Stephen
AU - Cheng, Hu
AU - Newman, Sharlene
AU - Gan, Yu
N1 - Publisher Copyright:
© 2021 SPIE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Convolutional Neural Network
KW - Denoising
KW - MRI
KW - Patch-based
KW - Residual
UR - http://www.scopus.com/inward/record.url?scp=85103618645&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103618645&partnerID=8YFLogxK
U2 - 10.1117/12.2582286
DO - 10.1117/12.2582286
M3 - Conference contribution
AN - SCOPUS:85103618645
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2021
A2 - Isgum, Ivana
A2 - Landman, Bennett A.
Y2 - 15 February 2021 through 19 February 2021
ER -