Deformation-aware Temporal Generation for Early Prediction of Alzheimers Disease

TL;DR

This paper introduces DATGN, a novel deformation-aware generative network that predicts future brain MRI sequences to enable early Alzheimer's disease diagnosis, improving classification accuracy and visualizing disease progression.

Contribution

The paper presents a new deformation-aware temporal generative network that handles missing data and predicts MRI sequences for early Alzheimer's detection, outperforming existing methods.

Findings

DATGN generates MRI sequences with high PSNR and MMSE quality metrics.

Incorporating synthetic data improves classification accuracy significantly.

Visualizations align with known brain atrophy patterns in Alzheimer's.

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.