Submillimeter MR fingerprinting using deep learning–based tissue quantification

Zhenghan Fang, Yong Chen, Sheng Che Hung, Xiaoxia Zhang, Weili Lin, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


Purpose: To develop a rapid 2D MR fingerprinting technique with a submillimeter in-plane resolution using a deep learning–based tissue quantification approach. Methods: A rapid and high-resolution MR fingerprinting technique was developed for brain T1 and T2 quantification. The 2D acquisition was performed using a FISP-based MR fingerprinting sequence and a spiral trajectory with 0.8-mm in-plane resolution. A deep learning–based method was used to replace the standard template matching method for improved tissue characterization. A novel network architecture (i.e., residual channel attention U-Net) was proposed to improve high-resolution details in the estimated tissue maps. Quantitative brain imaging was performed with 5 adults and 2 pediatric subjects, and the performance of the proposed approach was compared with several existing methods in the literature. Results: In vivo measurements with both adult and pediatric subjects show that high-quality T1 and T2 mapping with 0.8-mm in-plane resolution can be achieved in 7.5 seconds per slice. The proposed deep learning method outperformed existing algorithms in tissue quantification with improved accuracy. Compared with the standard U-Net, high-resolution details in brain tissues were better preserved by the proposed residual channel attention U-Net. Experiments on pediatric subjects further demonstrated the potential of the proposed technique for fast pediatric neuroimaging. Alongside reduced data acquisition time, a 5-fold acceleration in tissue property mapping was also achieved with the proposed method. Conclusion: A rapid and high-resolution MR fingerprinting technique was developed, which enables high-quality T1 and T2 quantification with 0.8-mm in-plane resolution in 7.5 seconds per slice.

Original languageEnglish
Pages (from-to)579-591
Number of pages13
JournalMagnetic Resonance in Medicine
Issue number2
Publication statusPublished - 2020 Aug 1

Bibliographical note

Funding Information:
National Institutes of Health; Grant/Award No. EB006733

Publisher Copyright:
© 2019 International Society for Magnetic Resonance in Medicine


  • MR fingerprinting
  • deep learning
  • pediatric imaging
  • quantitative imaging

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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