Deformation-aware Temporal Generation for Early Prediction of Alzheimers Disease

2025-11-26

AI Summary

To address the challenge of missing scans in longitudinal brain MRI sequences for early Alzheimer’s disease (AD) prediction, this paper proposes a Deformation-Aware Temporal Generation Network (DATGN). DATGN integrates bidirectional temporal modeling, deformation-guided image interpolation, and adversarial learning to achieve high-fidelity completion of incomplete MRI sequences and synthesize future scans consistent with pathological progression patterns. The method simultaneously serves dual purposes: data augmentation and disease progression forecasting. Evaluated on the ADNI dataset, DATGN significantly improves PSNR of generated images. When integrated with SVM, CNN, and 3D-CNN classifiers, it boosts binary classification accuracy (AD vs. NC) by 6.21–16.0% and three-class accuracy (AD/MCI/NC) by 7.34–21.25%, demonstrating the substantial diagnostic value of explicit morphological dynamics modeling for early AD detection.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Alzheimer's disease (AD), a degenerative brain condition, can benefit from early prediction to slow its progression. As the disease progresses, patients typically undergo brain atrophy. Current prediction methods for Alzheimers disease largely involve analyzing morphological changes in brain images through manual feature extraction. This paper proposes a novel method, the Deformation-Aware Temporal Generative Network (DATGN), to automate the learning of morphological changes in brain images about disease progression for early prediction. Given the common occurrence of missing data in the temporal sequences of MRI images, DATGN initially interpolates incomplete sequences. Subsequently, a bidirectional temporal deformation-aware module guides the network in generating future MRI images that adhere to the disease's progression, facilitating early prediction of Alzheimer's disease. DATGN was tested for the generation of temporal sequences of future MRI images using the ADNI dataset, and the experimental results are competitive in terms of PSNR and MMSE image quality metrics. Furthermore, when DATGN-generated synthetic data was integrated into the SVM vs. CNN vs. 3DCNN-based classification methods, significant improvements were achieved from 6. 21% to 16% in AD vs. NC classification accuracy and from 7. 34% to 21. 25% in AD vs. MCI vs. NC classification accuracy. The qualitative visualization results indicate that DATGN produces MRI images consistent with the brain atrophy trend in Alzheimer's disease, enabling early disease prediction.

Problem

• Automating learning of brain morphological changes for Alzheimer's prediction
• Addressing missing data in temporal MRI sequences through interpolation
• Generating future MRI images that follow disease progression patterns

Innovation

• Automates learning of brain morphological changes
• Interpolates incomplete MRI sequences initially
• Generates future MRI images using deformation-aware module