Denoising of three-dimensional fast spin echo magnetic resonance images of knee joints using spatial-variant noise-relevant residual learning of convolution neural network
Journal
Computers in Biology and Medicine
Journal Volume
151
Journal Issue
Pt A
Date Issued
2022-12-01
Author(s)
Abstract
Purpose: Two-dimensional (2D) fast spin echo (FSE) techniques play a central role in the clinical magnetic resonance imaging (MRI) of knee joints. Moreover, three-dimensional (3D) FSE provides high-isotropic-resolution magnetic resonance (MR) images of knee joints, but it has a reduced signal-to-noise ratio compared to 2D FSE. Deep-learning denoising methods are a promising approach for denoising MR images, but they are often trained using synthetic noise due to challenges in obtaining true noise distributions for MR images. In this study, inherent true noise information from two number of excitations (2-NEX) acquisition was used to develop a deep-learning model based on residual learning of convolutional neural network (CNN), and this model was used to suppress the noise in 3D FSE MR images of knee joints. Methods: A deep learning-based denoising method was developed. The proposed CNN used two-step residual learning over parallel transporting and residual blocks and was designed to comprehensively learn real noise features from 2-NEX training data. Results: The results of an ablation study validated the network design. The new method achieved improved denoising performance of 3D FSE knee MR images compared with current state-of-the-art methods, based on the peak signal-to-noise ratio and structural similarity index measure. The improved image quality after denoising using the new method was verified by radiological evaluation. Conclusion: A deep CNN using the inherent spatial-varying noise information in 2-NEX acquisitions was developed. This method showed promise for clinical MRI assessments of the knee, and has potential applications for the assessment of other anatomical structures.
Subjects
3D FSE | Denoising | Knee MRI | Neural network | Number of excitation | Residual learning; eess.IV; eess.IV; Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Physics - Medical Physics
Description
6 figures, abstract accepted by Joint Annual Meeting ISMRM-ESMRMB &
ISMRT 31st Annual Meeting
ISMRT 31st Annual Meeting
Type
journal article