Method and system for low-field mri denoising with a deep complex-valued convolutional neural network

Abstract

MR image data can be improved by using a complex de-noising convolutional neural network such as a non-blind C-DnCNN, a network for MRI denoising that leverages complex-valued data with phase information and noise level information to improve denoising performance in various settings. The proposed method achieved superior performance on both simulated and in vivo testing data compared to other algorithms. The utilization of complex-valued operations allows the network to better exploit the complex-valued MRI data and preserve the phase information. The MR image data is subject to complex de-noising operations directly and simultaneously on both real and imaginary parts of the image data. Complex and real values are also utilized for block normalization and rectified linear units applied to the noisy image data. A residual image is predicted by the C-DnCNN and a clean MR image is available for extraction.