[Oral Presentation]Super Resolution of MR via Learning Virtual Parallel Imaging - Contribution details

Super Resolution of MR via Learning Virtual Parallel Imaging
ID:126 Submission ID:99 View Protection:ATTENDEE Updated Time:2021-11-02 09:36:27 Hits:236 Oral Presentation

Start Time:2021-11-14 16:00 (Asia/Shanghai)

Duration:15min

Session:[MR1] Workshop on MRI Session 1 [MRI2] Workshop on MRI Session 2

No files

Abstract
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.
Keywords
virtual parallel imaging,Super-Resolution imaging,reversible network
Speaker
Cailian Yang
Nanchang University

Submission Author
Cailian Yang Nanchang University
Xianghao Liao Nanchang University
Yifan Liao Huazhong University of Science and Technology
Minghui Zhang Nanchang University
Qiegen Liu Nanchang University
Comment submit
Verification code Change another
All comments

Countdown

  • 00

    Days

  • 00

    Hours

  • 00

    Minutes

  • 00

    Seconds

Important Dates

Abstract submission date:

2021-08-31

2021-10-25

Full paper submission date:

2021-09-15

2021-10-25

Notification of acceptance date: 

2021-09-30

2021-11-01

Conference date: 2021-11-12~2021-11-14

 

Contact Us

Jinying Yang +86 13675518597
Debo Zhi +86 15056085235
Song Gao +86 13121880288
Le Cao +86 15910809908