Abstract
Ultra-high field 7T magnetic resonance imaging (MRI) scanners produce images with exceptional anatomical details, which can facilitate diagnosis and prognosis. However, 7T MRI scanners are often cost prohibitive and hence inaccessible. In this paper, we propose a novel wavelet-based semi-supervised adversarial learning framework to synthesize 7T MR images from their 3T counterparts. Unlike most learning methods that rely on supervision requiring a significant amount of 3T-7T paired data, our method applies a semi-supervised learning mechanism to leverage unpaired 3T and 7T MR images to learn the 3T-to-7T mapping when 3T-7T paired data are scarce. This is achieved via a cycle generative adversarial network that operates in the joint spatial-wavelet domain for the synthesis of multi-frequency details. Extensive experimental results show that our method achieves better performance than state-of-the-art methods trained using fully paired data.
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings |
Editors | Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 786-794 |
Number of pages | 9 |
ISBN (Print) | 9783030322502 |
DOIs | |
Publication status | Published - 2019 |
Event | 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 2019 Oct 13 → 2019 Oct 17 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11767 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 |
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Country/Territory | China |
City | Shenzhen |
Period | 19/10/13 → 19/10/17 |
Bibliographical note
Publisher Copyright:© Springer Nature Switzerland AG 2019.
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science