The Age-specific Alzheimer 's Disease Prediction with Characteristic Constraints in Nonuniform Time Span

TL;DR

This paper introduces a novel age-specific MRI image generation method for Alzheimer's disease prediction, addressing irregular time intervals and enhancing image quality for better disease progression modeling.

Contribution

It proposes a sequential image generation approach guided by quantitative metrics and incorporates an age-scaling factor for improved disease stage prediction.

Findings

Quantitative metrics improve MRI synthesis accuracy

Age-scaled pixel loss enhances image generation

Structural Similarity Index of 0.882 indicates high image quality

Abstract

Alzheimer's disease is a debilitating disorder marked by a decline in cognitive function. Timely identification of the disease is essential for the development of personalized treatment strategies that aim to mitigate its progression. The application of generated images for the prediction of Alzheimer's disease poses challenges, particularly in accurately representing the disease's characteristics when input sequences are captured at irregular time intervals. This study presents an innovative methodology for sequential image generation, guided by quantitative metrics, to maintain the essential features indicative of disease progression. Furthermore, an age-scaling factor is integrated into the process to produce age-specific MRI images, facilitating the prediction of advanced stages of the disease. The results obtained from the ablation study suggest that the inclusion of quantitative metrics significantly improves the accuracy of MRI image synthesis. Furthermore, the application of age-scaled pixel loss contributed to the enhanced iterative generation of MRI images. In terms of long-term disease prognosis, the Structural Similarity Index reached a peak value of 0.882, indicating a substantial degree of similarity in the synthesized images.