期刊
MAGNETIC RESONANCE IN MEDICINE
卷 82, 期 6, 页码 2133-2145出版社
WILEY
DOI: 10.1002/mrm.27894
关键词
convolutional neural network; deep learning; Gibbs-ringing artifact; machine learning; MRI
资金
- National Natural Science Foundation of China [81371539, 61671228, 61471188]
- Guangdong Natural Science Foundation [2014A030313316, 2016A030313574]
- National Key Technology RAMP
- D Program of China [2015BAI01B03]
- Hong Kong Research Grant Council [RGC C7048-16G]
- NIH Blueprint for Neuroscience Research [1U54MH091657]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据