Improved Dementia Prediction in Cerebral Small Vessel Disease Using Deep Learning-Derived Diffusion Scalar Maps From T1.

ABSTRACT:

Background

Cerebral small vessel disease is the most common pathology underlying vascular dementia. In small vessel disease, diffusion tensor imaging is more sensitive to white matter damage and better predicts dementia risk than conventional magnetic resonance imaging sequences, such as T1 and fluid attenuation inversion recovery, but diffusion tensor imaging takes longer to acquire and is not routinely available in clinical practice. As diffusion tensor imaging-derived scalar maps-fractional anisotropy (FA) and mean diffusivity (MD)-are frequently used in clinical settings, one solution is to synthesize FA/MD from T1 images.

Methods

We developed a deep learning model to synthesize FA/MD from T1. The training data set consisted of 4998 participants with the highest white ma

SUBMITTER: Chen Y 

PROVIDER: S-EPMC11346716 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature