Predictive MR Image Generation for Alzheimer’s Disease and Normal Aging Using Diffeomorphic Registration

Abstract: Alzheimer´s Disease (AD) is the most prevalent cause of dementia, signifying a progressive and degenerative brain disorder that causes cognitive function deterioration including memory loss, communication difficulties, impaired judgment, and changes in behavior and personality. Compared to normal aging, AD introduces more profound cognitive impairments and brain morphology changes. Understanding these morphological changes associated with both normal aging and AD holds pivotal significance for the study of brain health. In recent years, the flourishing development of Artificial Intelligence (AI) has facilitated the analysis of medical images and the study of longitudinal brain morphology evolution. Numerous advanced AI-based frameworks have emerged to generate unbiased and realistic medical templates that represent the common characteristics within a cohort, providing valuable insights for cohort studies. Among these, Atlas-GAN is a state-of-the-art framework which can generate high-quality conditional deformable templates using diffeomorphic registration. However, cohort studies are not sufficient for individualized healthcare and treatment as each patient has a unique condition. Fortunately, the introduction of a mathematical mechanism, parallel transport, enables the inference of individual brain morphological evolution from cohort-level longitudinal templates. This project proposed an image generator that integrates the pole ladder, a tool for parallel transport implementation, into Atlas-GAN, to translate the cohort-level brain morphological evolution onto individual subjects, enabling the synthesis of anatomically plausible and personalized longitudinal Magnetic Resonance (MR) images based on one individual Magnetic Resonance Imaging (MRI) scan. In clinics, the synthesized images empower the physicians to retrospectively understand the patient's premorbid brain states and prospectively predict their brain morphology changes over time. Such capabilities are of paramount importance for the prognosis, diagnosis, and early-stage intervention of AD, especially given the current absence of a cure for AD. The primary contributions of this project include: (1) Introduction of an image generator that combines parallel transport with Atlas-GAN to synthesize individual longitudinal MR images for both the normal aging cohort and the cohort suffering from AD with both anatomical plausibility and preservation of individualized characteristics; (2) exploration into the prediction of individual longitudinal MR images in the case of an individual undergoing a state transition using the proposed generator; (3) conduction of both qualitative and quantitative evaluations and analyses for the synthesized images.