[口头报告]Super Resolution of MR via Learning Virtual Parallel Imaging - 报告详情

Super Resolution of MR via Learning Virtual Parallel Imaging
编号:126 访问权限:仅限参会人 更新:2021-11-02 09:36:27 浏览:204次 口头报告

报告开始:2021年11月14日 16:00 (Asia/Shanghai)

报告时间:15min

所在会议:[MR1] Workshop on MRI Session 1 [MRI2] Workshop on MRI Session 2

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摘要
Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However, due to the limitations in hardware, it is often clinically challenging to obtain high-quality MR images. Super-resolution (SR) is potentially promising to improve MR image quality without any hardware upgrade. Instead of the classical SR reconstruction method enhance the spatial resolution via utilizing the spatial information itself, in this work, we propose a novel SR method via learning channel information in virtual parallel imaging. Using auxiliary variable technology to make the channel number of network output to be equal to the network input, thereby increasing the number of channels information to achieve SR reconstruction. Compared with state-of-the-art SR methods, the present approach is advantageous in suppressing artifacts and keeping more image details.
关键字
virtual parallel imaging,Super-Resolution imaging,reversible network
报告人
Cailian Yang
Nanchang University

稿件作者
Cailian Yang Nanchang University
Xianghao Liao Nanchang University
Yifan Liao Huazhong University of Science and Technology
Minghui Zhang Nanchang University
Qiegen Liu Nanchang University
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重要日期

摘要提交日期:

2021/08/31

2021/10/25

全文投稿日期:  

2021/09/15

2021/10/25

录取通知日期: 

2021/09/30

2021/11/01

会议日期:   2021-11-12-2021-11-14

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