Submillimeter MR fingerprinting using deep learning-based tissue quantification

Magn Reson Med. 2020 Aug;84(2):579-591. doi: 10.1002/mrm.28136. Epub 2019 Dec 19.


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.

Keywords: MR fingerprinting; deep learning; pediatric imaging; quantitative imaging.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adult
  • Algorithms
  • Brain / diagnostic imaging
  • Child
  • Deep Learning*
  • Humans
  • Magnetic Resonance Imaging
  • Neuroimaging