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Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic
Malvika Viswanathan1,2, Leqi Yin1,3, Yashwant Kurmi1,4
1Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
NMR in Biomedicine
|October 22, 2024
Summary
Machine learning (ML) improves amide proton transfer (APT) imaging for stroke diagnosis using novel partially synthetic data. This approach enhances accuracy and reduces scan time compared to conventional methods.
Area of Science:
- Biomedical Imaging
- Medical Physics
- Machine Learning Applications
Background:
- Amide proton transfer (APT) imaging is sensitive to tissue pH and crucial for diagnosing ischemic stroke.
- Conventional APT quantification methods are often inaccurate or time-consuming.
- Machine learning (ML) offers a potential solution for improving APT quantification accuracy and speed.
Purpose of the Study:
- To develop and evaluate an ML model for accurate and rapid APT imaging in an animal stroke model.
- To investigate the efficacy of partially synthetic data for training ML models in this context.
- To compare the ML approach with conventional APT quantification techniques.
Main Methods:
- An ML model was trained on a novel type of partially synthetic data, integrating measured and simulated chemical exchange saturation transfer (CEST) components.
- Recursive feature elimination was used for optimization, reducing selected frequency offsets from 69 to 13.
- The ML model's performance was compared against conventional methods like asymmetric analysis and Lorentzian fit.
Main Results:
- The ML model trained on partially synthetic data accurately predicted APT effects, clearly delineating stroke lesions from normal tissue.
- This approach showed significantly enhanced contrast compared to conventional quantification methods.
- ML models trained on fully synthetic or in vivo data demonstrated poorer predictive performance.
- Optimization significantly reduced scan time by selecting fewer frequency offsets.
Conclusions:
- Partially synthetic data provides a practical and effective approach for training ML models for APT imaging.
- The developed ML method offers a more accurate and faster alternative to conventional techniques for stroke diagnosis.
- This ML-based strategy holds significant promise for improving clinical applications of APT imaging in ischemic stroke detection.
