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Extensive T1-weighted MRI preprocessing improves generalizability of deep brain age prediction models

Unknown authors · 2024
hash_id: d3b445f806b7b74011c55ada75296c6dde977a55d84a9d5ea07488135efc6cf4 · DOI: 10.1016/j.compbiomed.2024.108320

Brain age is an estimate of chronological age obtained from T1-weighted magnetic resonance images (T1w MRI), representing a straightforward diagnostic biomarker of brain aging and associated diseases. While the current best accuracy of brain age predictions on T1w MRIs of healthy subjects ranges from two to three years, comparing results across studies is challenging due to differences in the datasets, T1w preprocessing pipelines, and evaluation protocols used. This paper investigates the impact of T1w image preprocessing on the performance of four deep learning brain age models from recent literature. Four preprocessing pipelines, which differed in terms of registration transform, grayscale correction, and software implementation, were evaluated. The results showed that the choice of software or preprocessing steps could significantly affect …

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