Qianqian Zhang1,2, Guohui Ruan1,2, Wei Yang1,2, Yilong Liu3,4, Kaixuan Zhao1,2, Qianjin Feng1,2, Wufan Chen1,2, Ed X Wu3,4, Yanqiu Feng1,2. 1. School of Biomedical Engineering, Southern Medical University, Guangzhou, China. 2. Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China. 3. Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China. 4. Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
Abstract
PURPOSE: To develop a machine learning approach using convolutional neural network for reducing MRI Gibbs-ringing artifact. THEORY AND METHODS: Gibbs-ringing artifact in MR images is caused by insufficient sampling of the high frequency data. Existing methods exploit smooth constraints to reduce intensity oscillations near sharp edges at the cost of blurring details. In this work, we developed a machine learning approach for removing the Gibbs-ringing artifact from MR images. The ringing artifact was extracted from the original image using a deep convolutional neural network and then subtracted from the original image to obtain the artifact-free image. Finally, its low-frequency k-space data were replaced with measured counterparts to enforce data fidelity further. We trained the convolutional neural network using 17,532 T2-weighted (T2W) normal brain images and evaluated its performance on T2W images of normal and tumor brains, diffusion-weighted brain images, and T2W knee images. RESULTS: The proposed method effectively removed the ringing artifact without noticeable smoothing in T2W and diffusion-weighted images. Quantitatively, images produced by the proposed method were closer to the fully-sampled reference images in terms of the root-mean-square error, peak signal-to-noise ratio, and structural similarity index, compared with current state-of-the-art methods. CONCLUSION: The proposed method presents a novel and effective approach for Gibbs-ringing reduction in MRI. The convolutional neural network-based approach is simple, computationally efficient, and highly applicable in routine clinical MRI.
PURPOSE: To develop a machine learning approach using convolutional neural network for reducing MRI Gibbs-ringing artifact. THEORY AND METHODS: Gibbs-ringing artifact in MR images is caused by insufficient sampling of the high frequency data. Existing methods exploit smooth constraints to reduce intensity oscillations near sharp edges at the cost of blurring details. In this work, we developed a machine learning approach for removing the Gibbs-ringing artifact from MR images. The ringing artifact was extracted from the original image using a deep convolutional neural network and then subtracted from the original image to obtain the artifact-free image. Finally, its low-frequency k-space data were replaced with measured counterparts to enforce data fidelity further. We trained the convolutional neural network using 17,532 T2-weighted (T2W) normal brain images and evaluated its performance on T2W images of normal and tumor brains, diffusion-weighted brain images, and T2W knee images. RESULTS: The proposed method effectively removed the ringing artifact without noticeable smoothing in T2W and diffusion-weighted images. Quantitatively, images produced by the proposed method were closer to the fully-sampled reference images in terms of the root-mean-square error, peak signal-to-noise ratio, and structural similarity index, compared with current state-of-the-art methods. CONCLUSION: The proposed method presents a novel and effective approach for Gibbs-ringing reduction in MRI. The convolutional neural network-based approach is simple, computationally efficient, and highly applicable in routine clinical MRI.
Authors: Matthew J Muckley; Benjamin Ades-Aron; Antonios Papaioannou; Gregory Lemberskiy; Eddy Solomon; Yvonne W Lui; Daniel K Sodickson; Els Fieremans; Dmitry S Novikov; Florian Knoll Journal: Magn Reson Med Date: 2020-07-14 Impact factor: 4.668
Authors: Akshay S Chaudhari; Christopher M Sandino; Elizabeth K Cole; David B Larson; Garry E Gold; Shreyas S Vasanawala; Matthew P Lungren; Brian A Hargreaves; Curtis P Langlotz Journal: J Magn Reson Imaging Date: 2020-08-24 Impact factor: 5.119