Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple...

Area of Science:

• Neuroimaging
• Medical Physics
• Artificial Intelligence

Background:

• Multiple sclerosis (MS) is a leading cause of demyelination in the central nervous system, necessitating accurate myelin content measurement for disease management.
• Positron emission tomography (PET) with [11C]PIB accurately measures in vivo myelin but faces limitations in clinical availability, cost, and invasiveness.
• Conventional magnetic resonance imaging (MRI) is widely accessible and non-invasive but lacks direct, reliable myelin quantification capabilities.

Purpose of the Study:

• To develop and validate a novel method for predicting in vivo myelin content maps from multimodal MRI data.
• To overcome the limitations of PET imaging in terms of accessibility, cost, and invasiveness for myelin quantification in MS.
• To establish a reliable, non-invasive imaging biomarker for MS assessment using advanced AI techniques.

Main Methods:

• Introduction of a novel generative adversarial network (GAN) framework named Sketcher-Refiner, comprising two networks for sequential myelin content prediction.
• Development of specialized adversarial loss functions tailored for myelin content estimation and image quality enhancement.
• Integration of a visual attention saliency map for interpreting the neural network's focus during myelin prediction.

Main Results:

• The proposed Sketcher-Refiner GAN approach significantly outperforms existing state-of-the-art methods in both image quality and myelin content prediction accuracy.
• Predicted myelin content maps demonstrated high concordance with the gold standard PET-derived measurements at both global and voxel-wise levels.
• The method shows potential for accurate, non-invasive assessment of myelin in the clinical management of multiple sclerosis.

Conclusions:

• The developed AI-driven MRI method provides a promising, non-invasive alternative for accurate myelin quantification in multiple sclerosis.
• This approach enhances the clinical utility of widely available MRI technology for tracking MS progression and treatment response.
• Further validation could integrate this technique into routine clinical practice for improved patient care in MS.