Investigative Magnetic Resonance Imaging

Korean Society of Magnetic Resonance in Medicine (대한자기공명의과학회)
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Accelerating Magnetic Resonance Fingerprinting Using Hybrid Deep Learning and Iterative Reconstruction

  • Cao, Peng (Department of Diagnostic Radiology, The University of Hong Kong) ;
  • Cui, Di (Department of Diagnostic Radiology, The University of Hong Kong) ;
  • Ming, Yanzhen (Department of Diagnostic Radiology, The University of Hong Kong) ;
  • Vardhanabhuti, Varut (Department of Diagnostic Radiology, The University of Hong Kong) ;
  • Lee, Elaine (Department of Diagnostic Radiology, The University of Hong Kong) ;
  • Hui, Edward (Department of Rehabilitation Science, The Hong Kong Polytechnic University)
  • Received : 2021.02.01
  • Accepted : 2021.06.17
  • Published : 2021.12.30
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Abstract

Purpose: To accelerate magnetic resonance fingerprinting (MRF) by developing a flexible deep learning reconstruction method. Materials and Methods: Synthetic data were used to train a deep learning model. The trained model was then applied to MRF for different organs and diseases. Iterative reconstruction was performed outside the deep learning model, allowing a changeable encoding matrix, i.e., with flexibility of choice for image resolution, radiofrequency coil, k-space trajectory, and undersampling mask. In vivo experiments were performed on normal brain and prostate cancer volunteers to demonstrate the model performance and generalizability. Results: In 400-dynamics brain MRF, direct nonuniform Fourier transform caused a slight increase of random fluctuations on the T2 map. These fluctuations were reduced with the proposed method. In prostate MRF, the proposed method suppressed fluctuations on both T1 and T2 maps. Conclusion: The deep learning and iterative MRF reconstruction method described in this study was flexible with different acquisition settings such as radiofrequency coils. It is generalizable for different in vivo applications.

Keywords

Acknowledgement

The work was partly funded by Hong Kong RGC General Research Fund (17117018) and Hong Kong Health and Medical Research Fund (07182706 and 06172916).

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