Predicting individual brain MRIs at any age using style encoding generative adversarial networks

Shruti P. Gadewar; Abhinaav Ramesh; Mengting Liu; Iyad Ba Gari; Talia M. Nir; Paul Thompson; Neda Jahanshad

doi:10.1117/12.2669741

6 March 2023 Predicting individual brain MRIs at any age using style encoding generative adversarial networks

Shruti P. Gadewar, Abhinaav Ramesh, Mengting Liu, Iyad Ba Gari, Talia M. Nir, Paul Thompson, Neda Jahanshad

Proceedings Volume 12567, 18th International Symposium on Medical Information Processing and Analysis; 125671H (2023) https://doi.org/10.1117/12.2669741
Event: 18th International Symposium on Medical Information Processing and Analysis, 2022, Valparaíso, Chile

Abstract

Brain structural changes in older adults over time can help identify and predict which individuals are at risk for neurodegenerative disorders and dementias. These trajectories are traditionally calculated by assessing localized rates of brain tissue atrophy from a longitudinal series of brain MRIs as compared to group averages. However, these methods do not preserve individual differences in brain structure, which may provide added information regarding risk. A map of how an individual’s brain may look at a given age - in the case of a normal, healthy, aging trajectory - may help to identify deviations and abnormalities when presented with a true scan at that age. Here, we consider estimating the age-related brain changes as a domain transfer problem. We develop a fully unsupervised generative adversarial network (GAN) with cycle consistency reconstruction losses, trained on cross-sectional brain MRI data from participants of the UK Biobank aged 45 to 81. We show that brain MRIs for males and females at a given age can be predicted by converting the content information encoded in a T1-weighted MRI (i.e., the individual’s identifying anatomical features), accompanied by adding the style (age/sex) information from a reference group. Results on the PREVENT-AD cohort demonstrated that our style-encoding domain transfer model can predict follow-up brain MRIs, successfully, without relying on longitudinal data from the subjects. We show how deviations from the predicted images are indicative of factors related to neurodegenerative disease risk.

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Shruti P. Gadewar, Abhinaav Ramesh, Mengting Liu, Iyad Ba Gari, Talia M. Nir, Paul Thompson, and Neda Jahanshad "Predicting individual brain MRIs at any age using style encoding generative adversarial networks", Proc. SPIE 12567, 18th International Symposium on Medical Information Processing and Analysis, 125671H (6 March 2023); https://doi.org/10.1117/12.2669741

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KEYWORDS

Brain

Magnetic resonance imaging

3D modeling

Neuroimaging

Data modeling

3D image processing

Anatomy

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