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Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting
Raffaella Fiamma Cabini1,2, Leonardo Barzaghi1,2,3, Davide Cicolari4,5,6,7
1Department of Mathematics, University of Pavia, Pavia, Italy.
NMR in Biomedicine
|September 5, 2023
Summary
A novel deep learning model significantly improves magnetic resonance fingerprinting (MRF) by reducing T1 and T2 map errors and accelerating reconstruction. This AI approach enhances accuracy and speed for preclinical and clinical MRI scans.
Area of Science:
- Biomedical Imaging
- Artificial Intelligence in Medicine
- Quantitative MRI
Background:
- Magnetic Resonance Fingerprinting (MRF) enables quantitative mapping of tissue properties like T1 and T2 relaxation times.
- Traditional MRF reconstruction relies on dictionary-based methods, which can be computationally intensive and prone to errors.
- Optimizing MRF acquisition and reconstruction is crucial for improving scan efficiency and diagnostic accuracy.
Purpose of the Study:
- To develop and validate a deep learning (DL) model for reconstructing T1 and T2 maps from MRF data.
- To implement an advanced hyperparameter optimization strategy for simultaneous tuning of DL model architecture and learning parameters.
- To evaluate the performance of the DL-based reconstruction against traditional methods in terms of accuracy and speed.
Main Methods:
- Acquisition of MRF data from ex vivo rat brain phantoms using a 7-T preclinical scanner with two different sequence routines.
- Training of the DL model exclusively on experimentally acquired data, avoiding theoretical MRI signal simulators.
- Application of an automatic hyperparameter optimization strategy to simultaneously optimize neural network architecture, DL model structure, and supervised learning algorithm.
- Comparison of DL reconstruction results with a traditional dictionary-based method using an independent dataset.
Main Results:
- The DL approach reduced the mean percentage relative error by a factor of 3 for T1 and by a factor of 2 for T2 compared to the dictionary-based method.
- Computational time for reconstruction was improved by at least a factor of 37 using the DL method.
- Comparable reconstruction performance was maintained with fewer MRF images and reduced k-space sampling.
- The DL model demonstrated superior accuracy and significantly faster reconstruction times.
Conclusions:
- The proposed DL methodology offers a substantial improvement in the accuracy of T1 and T2 map reconstruction from MRF data.
- The DL approach significantly accelerates MRF reconstruction, making it more feasible for rapid preclinical and prospective clinical applications.
- This AI-driven method holds promise for enhancing the efficiency and diagnostic utility of quantitative MRI techniques.
