Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains

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

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


Ultra-high field 7T MRI scanners, while producing images with exceptional anatomical details, are cost prohibitive and hence highly inaccessible. In this paper, we introduce a novel deep learning network that fuses complementary information from spatial and wavelet domains to synthesize 7T T1-weighted images from their 3T counterparts. Our deep learning network leverages wavelet transformation to facilitate effective multi-scale reconstruction, taking into account both low-frequency tissue contrast and high-frequency anatomical details. Our network utilizes a novel wavelet-based affine transformation (WAT) layer, which modulates feature maps from the spatial domain with information from the wavelet domain. Extensive experimental results demonstrate the capability of the proposed method in synthesizing high-quality 7T images with better tissue contrast and greater details, outperforming state-of-the-art methods.

Original languageEnglish
Article number101663
JournalMedical Image Analysis
Publication statusPublished - 2020 May


  • Image synthesis
  • Magnetic resonance imaging (MRI)
  • Spatial and wavelet domains

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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