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snapMRF: GPU-accelerated magnetic resonance fingerprinting dictionary generation and matching using extended phase
Dong Wang1, Jason Ostenson2, David S Smith2
1School of Science, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
Magnetic Resonance Imaging
|November 20, 2019
Summary
This study introduces snapMRF, a fast, open-source Magnetic Resonance Fingerprinting (MRF) reconstruction tool. It significantly accelerates MRF analysis, improving the accuracy of quantitative MRI parameter mapping for clinical applications.
Area of Science:
- Medical Imaging
- Quantitative MRI
- Computational Science
Background:
- Magnetic Resonance Fingerprinting (MRF) is a cutting-edge quantitative MRI technique.
- MRF reconstruction is computationally intensive, impacting clinical application accuracy.
- Accurate signal modeling is crucial for dependable MRF results.
Purpose of the Study:
- To develop a fast, validated, open-source MRF reconstruction package.
- To enhance the dependability and accuracy of clinical MRF applications.
- To improve the computational efficiency of MRF data processing.
Main Methods:
- Parallelized dictionary generation and signal matching on GPUs.
- Utilized Bloch equation simulation and Extended Phase Graph (EPG) formalism for signal modeling.
- Developed and tested the snapMRF package on calibration phantoms and in vivo brain data.
Main Results:
- Achieved 10-1000x acceleration in dictionary generation and 10-100x in signal matching compared to existing packages.
- snapMRF demonstrated comparable T1 and T2 measurement accuracy to other packages on phantoms.
- snapMRF showed superior accuracy with variable sequences not supported by competitors.
Conclusions:
- The open-source snapMRF package offers significant speed improvements and accurate parameter retrieval.
- snapMRF may enable real-time quantitative parameter map generation.
- Further optimization of acquisition schemes and dictionary setup can enhance quantitative accuracy.
