Wavelet-based semi-supervised adversarial learning for synthesizing realistic 7t from 3t MRI

Liangqiong Qu, Shuai Wang, Pew Thian Yap, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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 languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer
Pages786-794
Number of pages9
ISBN (Print)9783030322502
DOIs
Publication statusPublished - 2019 Jan 1
Externally publishedYes
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 132019 Oct 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11767 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period19/10/1319/10/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Qu, L., Wang, S., Yap, P. T., & Shen, D. (2019). Wavelet-based semi-supervised adversarial learning for synthesizing realistic 7t from 3t MRI. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 786-794). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11767 LNCS). Springer. https://doi.org/10.1007/978-3-030-32251-9_86