SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions

TL;DR

SynthBA is a deep learning model that reliably predicts brain age across diverse MRI sequences and resolutions, addressing variability issues in clinical applications and correlating with Alzheimer's disease markers.

Contribution

Introduces SynthBA, a robust deep-learning model utilizing domain randomization to accurately predict brain age across heterogeneous MRI data.

Findings

SynthBA outperforms existing methods on multiple datasets.

It maintains accuracy across different MRI sequences and resolutions.

Brain PAD from SynthBA correlates with Alzheimer's disease measures.

Abstract

Brain age is a critical measure that reflects the biological ageing process of the brain. The gap between brain age and chronological age, referred to as brain PAD (Predicted Age Difference), has been utilized to investigate neurodegenerative conditions. Brain age can be predicted using MRIs and machine learning techniques. However, existing methods are often sensitive to acquisition-related variabilities, such as differences in acquisition protocols, scanners, MRI sequences, and resolutions, significantly limiting their application in highly heterogeneous clinical settings. In this study, we introduce Synthetic Brain Age (SynthBA), a robust deep-learning model designed for predicting brain age. SynthBA utilizes an advanced domain randomization technique, ensuring effective operation across a wide array of acquisition-related variabilities. To assess the effectiveness and robustness of SynthBA, we evaluate its predictive capabilities on internal and external datasets, encompassing various MRI sequences and resolutions, and compare it with state-of-the-art techniques. Additionally, we calculate the brain PAD in a large cohort of subjects with Alzheimer's Disease (AD), demonstrating a significant correlation with AD-related measures of cognitive dysfunction. SynthBA holds the potential to facilitate the broader adoption of brain age prediction in clinical settings, where re-training or fine-tuning is often unfeasible. The SynthBA source code and pre-trained models are publicly available at https://github.com/LemuelPuglisi/SynthBA.