@inproceedings{73b635e9a41147aa9cc2fe44db46073b,
title = "Reduction of Gibbs artifacts in magnetic resonance imaging based on Convolutional Neural Network",
abstract = "In Magnetic Resonance Imaging (MRI), the K-space data is often under-sampled and truncated to shorten the scan time. However, the truncation of K-space also causes Gibbs ringing artifacts in the image, which seriously deteriorates the image quality. Inspired by the recent achievements of deep learning, we propose a novel method to reduce Gibbs artifacts in MRI with Convolutional Neural Network (CNN) in this paper. CNN is trained with a batch of image pairs with and without Gibbs artifacts. Afterwards, images with Gibbs artifacts can be input into the trained network to get the Gibbs-free images. Output of CNN is then transformed into K-space and merged with the sampled K-space data. Finally, inverse Fourier transform is applied to the merged K-space to get the final image. Experiments on both phantoms and real MRI images proved that the proposed method could reduce the Gibbs artifacts to a great degree and keep more image details compared with traditional Tukey filter.",
keywords = "Convolutional Neural Network (CNN), Gibbs artifacts, Magnetic Resonance Imaging (MRI)",
author = "Yida Wang and Yang Song and Haibin Xie and Wenjing Li and Bingwen Hu and Guang Yang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017 ; Conference date: 14-10-2017 Through 16-10-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/CISP-BMEI.2017.8302197",
language = "英语",
series = "Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
editor = "Qingli Li and Lipo Wang and Mei Zhou and Li Sun and Song Qiu and Hongying Liu",
booktitle = "Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017",
address = "美国",
}