A multi-view learning approach with diffusion model to synthesize FDG PET from MRI T1WI for diagnosis of Alzheimer's disease

Chen, Ke; Weng, Ying; Huang, Yueqin; Zhang, Yiming; Dening, Tom; Hosseini, Akram A.; Xiao, Weizhong

Abstract

INTRODUCTION

This study presents a novel multi-view learning approach for machine learning (ML)–based Alzheimer's disease (AD) diagnosis.

METHODS

A diffusion model is proposed to synthesize the fluorodeoxyglucose positron emission tomography (FDG PET) view from the magnetic resonance imaging T1 weighted imaging (MRI T1WI) view and incorporate two synthesis strategies: one-way synthesis and two-way synthesis. To assess the utility of the synthesized views, we use multilayer perceptron (MLP)–based classifiers with various combinations of the views.

RESULTS

The two-way synthesis achieves state-of-the-art performance with a structural similarity index measure (SSIM) at 0.9380 and a peak-signal-to-noise ratio (PSNR) at 26.47. The one-way synthesis achieves an SSIM at 0.9282 and a PSNR at 23.83. Both synthesized FDG PET views have shown their effectiveness in improving diagnostic accuracy.

DISCUSSION

This work supports the notion that ML-based cross-domain data synthesis can be a useful approach to improve AD diagnosis by providing additional synthesized disease-related views for multi-view learning.

Citation

Chen, K., Weng, Y., Huang, Y., Zhang, Y., Dening, T., Hosseini, A. A., & Xiao, W. (2024). A multi-view learning approach with diffusion model to synthesize FDG PET from MRI T1WI for diagnosis of Alzheimer's disease. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, https://doi.org/10.1002/alz.14421

• Journal Article Type: Article
• Acceptance Date: Nov 1, 2024
• Online Publication Date: Dec 6, 2024
• Publication Date: Dec 6, 2024
• Deposit Date: Jan 23, 2025
• Publicly Available Date: Jan 24, 2025
• Journal: Alzheimer's & Dementia: The Journal of the Alzheimer's Association
• Print ISSN: 1552-5260
• Electronic ISSN: 1552-5279
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1002/alz.14421
• Public URL: https://nottingham-repository.worktribe.com/output/44234033
• Publisher URL: https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.14421
• Additional Information: Received: 2024-05-17; Accepted: 2024-11-01; Published: 2024-12-06