Investig Magn Reson Imaging.  2021 Dec;25(4):293-299. 10.13104/imri.2021.25.4.293.

Accelerating Magnetic Resonance Fingerprinting Using Hybrid Deep Learning and Iterative Reconstruction

• Cao P
• Cui D
• Ming Y
• Vardhanabhuti V
• Lee E
• Hui E

• KMID: 2526482
• DOI: http://doi.org/10.13104/imri.2021.25.4.293

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.

Keyword

Deep learning; Iterative reconstruction; MRF reconstruction; MRI reconstruction