3 research outputs found
Predicting Age from White Matter Diffusivity with Residual Learning
Imaging findings inconsistent with those expected at specific chronological
age ranges may serve as early indicators of neurological disorders and
increased mortality risk. Estimation of chronological age, and deviations from
expected results, from structural MRI data has become an important task for
developing biomarkers that are sensitive to such deviations. Complementary to
structural analysis, diffusion tensor imaging (DTI) has proven effective in
identifying age-related microstructural changes within the brain white matter,
thereby presenting itself as a promising additional modality for brain age
prediction. Although early studies have sought to harness DTI's advantages for
age estimation, there is no evidence that the success of this prediction is
owed to the unique microstructural and diffusivity features that DTI provides,
rather than the macrostructural features that are also available in DTI data.
Therefore, we seek to develop white-matter-specific age estimation to capture
deviations from normal white matter aging. Specifically, we deliberately
disregard the macrostructural information when predicting age from DTI scalar
images, using two distinct methods. The first method relies on extracting only
microstructural features from regions of interest. The second applies 3D
residual neural networks (ResNets) to learn features directly from the images,
which are non-linearly registered and warped to a template to minimize
macrostructural variations. When tested on unseen data, the first method yields
mean absolute error (MAE) of 6.11 years for cognitively normal participants and
MAE of 6.62 years for cognitively impaired participants, while the second
method achieves MAE of 4.69 years for cognitively normal participants and MAE
of 4.96 years for cognitively impaired participants. We find that the ResNet
model captures subtler, non-macrostructural features for brain age prediction.Comment: SPIE Medical Imaging: Image Processing. San Diego, CA. February 2024
(accepted as poster presentation