Predicting Quantitative Myelin Images from Clinical Scans and DTI using Linear and Neural Network Models

Motivation: Quantitative myelin imaging methods are usually not included in clinical MRI scans for persons with multiple sclerosis. Goal(s): Predict macromolecular pool-size-ratio maps from standard clinical scans. Approach: Compare PSR map prediction results from linear model and neural network model. Results: Most of the relationship between clinical scans plus DTI and PSR maps is captured by a simple linear model and the more complex nonlinear model offers minimal improvements. Impact: Leveraging typical clinical scans to predict PSR maps using a simple linear model can improve diagnostic capabilities for multiple.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/3402
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Predicting Quantitative Myelin Images from Clinical Scans and DTI using Linear and Neural Network Models

Richard Dortch, Ashley M. Stokes, Francesca Bagnato, Kayla Bohlke
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
AI in cancer detection
article

Predicting Quantitative Myelin Images from Clinical Scans and DTI using Linear and Neural Network Models

Richard Dortch, Ashley M. Stokes, Francesca Bagnato, Kayla Bohlke
article en

Abstract

Motivation: Quantitative myelin imaging methods are usually not included in clinical MRI scans for persons with multiple sclerosis. Goal(s): Predict macromolecular pool-size-ratio maps from standard clinical scans. Approach: Compare PSR map prediction results from linear model and neural network model. Results: Most of the relationship between clinical scans plus DTI and PSR maps is captured by a simple linear model and the more complex nonlinear model offers minimal improvements. Impact: Leveraging typical clinical scans to predict PSR maps using a simple linear model can improve diagnostic capabilities for multiple.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 87%
AI in cancer detection
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