Generative Aging of Brain Images with Diffeomorphic Registration

TL;DR

This paper introduces a novel diffeomorphic registration-based method for generating subject-specific, anatomically plausible brain aging MRI scans, improving longitudinal predictions and aiding AI-driven healthcare.

Contribution

It presents a new individualized generative framework with biological aging modeling and quality control, advancing brain aging simulation at the subject level.

Findings

Produced realistic, anatomically plausible aging MRI scans

Validated on large multi-cohort dataset with quantitative and qualitative assessments

Enhanced longitudinal datasets for AI healthcare applications

Abstract

Analyzing and predicting brain aging is essential for early prognosis and accurate diagnosis of cognitive diseases. The technique of neuroimaging, such as Magnetic Resonance Imaging (MRI), provides a noninvasive means of observing the aging process within the brain. With longitudinal image data collection, data-intensive Artificial Intelligence (AI) algorithms have been used to examine brain aging. However, existing state-of-the-art algorithms tend to be restricted to group-level predictions and suffer from unreal predictions. This paper proposes a methodology for generating longitudinal MRI scans that capture subject-specific neurodegeneration and retain anatomical plausibility in aging. The proposed methodology is developed within the framework of diffeomorphic registration and relies on three key novel technological advances to generate subject-level anatomically plausible predictions: i) a computationally efficient and individualized generative framework based on registration; ii) an aging generative module based on biological linear aging progression; iii) a quality control module to fit registration for generation task. Our methodology was evaluated on 2662 T1-weighted (T1-w) MRI scans from 796 participants from three different cohorts. First, we applied 6 commonly used criteria to demonstrate the aging simulation ability of the proposed methodology; Secondly, we evaluated the quality of the synthetic images using quantitative measurements and qualitative assessment by a neuroradiologist. Overall, the experimental results show that the proposed method can produce anatomically plausible predictions that can be used to enhance longitudinal datasets, in turn enabling data-hungry AI-driven healthcare tools.