Generative Aging of Brain Images with Diffeomorphic Registration

• URL: http://arxiv.org/abs/2205.15607v1
• Date: Tue, 31 May 2022 08:37:24 GMT
• Title: Generative Aging of Brain Images with Diffeomorphic Registration
• Authors: Jingru Fu, Antonios Tzortzakakis, Jos\'e Barroso, Eric Westman, Daniel Ferreira, Rodrigo Moreno
• Abstract summary: Analyzing and predicting brain aging is essential for early prognosis and accurate diagnosis of cognitive diseases. This paper proposes a methodology for generating longitudinal MRI scans that capture subject-specific neurodegeneration and retain anatomical plausibility in aging.
• Score: 3.645542167239258
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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