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High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning:
Fan Lam1,2, Xi Peng3, Zhi-Pei Liang2
1Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801 USA.
IEEE Signal Processing Magazine
|August 4, 2023
Summary
Magnetic Resonance Spectroscopic Imaging (MRSI) applications are advancing rapidly. New physics-based modeling and machine learning methods are overcoming technical challenges for faster, high-resolution, quantitative MRSI.
Area of Science:
- Biomedical Imaging
- Spectroscopy
- Medical Physics
Background:
- Magnetic Resonance Spectroscopic Imaging (MRSI) provides crucial molecular insights into human physiology and pathology.
- Traditional MRSI faces limitations like high dimensionality and low signal-to-noise ratio (SNR), hindering widespread clinical application.
Purpose of the Study:
- To systematically review recent technological advancements in MRSI.
- To highlight the integration of physics-based modeling and machine learning in addressing MRSI challenges.
- To offer perspectives on future directions for MRSI development.
Main Methods:
- Review of recent literature on MRSI technological developments.
- Analysis of physics-based modeling approaches for MRSI signal processing.
- Evaluation of data-driven machine learning techniques applied to MRSI data.
- Exploration of the interplay between MRSI physics and computational methods.
Main Results:
- Recent innovations combining physics-based modeling and machine learning show significant improvements in MRSI.
- These integrated approaches effectively address challenges of dimensionality and SNR.
- Demonstrated success in achieving rapid, high-resolution, and quantitative MRSI.
Conclusions:
- The integration of physics-based modeling and machine learning is revolutionizing MRSI.
- These advancements are paving the way for more accessible and powerful molecular imaging.
- Future research should focus on further exploiting these synergistic approaches for clinical translation.
