Synthetic MRI aging

Description

One challenge in image analysis of Alzheimer’s patients is to disentangle the effects of aging from the disease itself. For this, it is necessary to use a large number of images of healthy subjects at every specific age, which is not available in the current longitudinal imaging datasets. In this project, we will develop an AI-based solution to generate magnetic resonance images (MRI) at different ages by simulating the changes in the images due to aging. In particular, given a structural MRI acquired at a specific age, the solution will be able to generate MRI of the same subject at older and/or younger ages. AI-based diffeomorphic registration will be used extensively in this project since, unlike standard generative models, it is able to preserve the anatomy of the brain.

We will validate the method for mild cognitive impairment (MCI) by comparing the subjects' actual time course with the ones of simulated healthy and MCI subjects. Having a tool to accurately generate new time points of MRI data will have a big impact on the diagnostic work-up by helping to predict clinical progression of patients over time (prospective), or providing information about premorbid states (retrospective).

Collaborations

Division of Clinical Geriatrics, Karolinska Institute

Thesis projects

Jingru Fu, doctoral dissertation project, ongoing.

Contact person

Contributors