Deep learning-based prediction of cerebral white matter hyperintensity burden using carotid magnetic resonance angiography.

Purpose: White matter hyperintensities (WMHs) are key neuroimaging markers of cerebral small vessel disease (cSVD), associated with cognitive decline and increased stroke risk. We aimed to investigate whether carotid time-of-flight (TOF) magnetic resonance angiography (MRA), a routinely acquired and non-invasive vascular imaging modality, can be utilized to independently predict WMH burden using deep learning. Methods: We developed a deep learning-based framework to predict WMH presence and severity using only 3D carotid TOF MRA. Two classification tasks were defined: binary (grade 0 vs. grades 1-3) and three-class (grade 0, 1, 2-3) classification. Four model architectures- simple fully convolutional network (SFCN), ResNet10, MedicalNet, and Medical Slice Transformer-were evaluated. To enhance model interpretability, we performed saliency mapping and occlusion analysis. Results: SFCN performed the best, achieving an accuracy of 76.5% and an area under the receiver operating characteristic curve (AUC) of 0.874 in binary classification, along with a 63.5% accuracy and a 0.827 AUC in WMH severity classification. Interpretability analyses confirmed that models predominantly focused on carotid vessel regions, which supports known vascular associations with WMH burden. Conclusion: Carotid TOF MRA alone can serve as a predictive marker for WMH burden when analyzed using deep learning. This approach highlights the potential utility of extracranial carotid imaging as a non-invasive surrogate for early and accessible assessment of cerebrovascular risk.