Abstract Body: Purpose: White matter hyperintensities (WMH), also known as leukoaraiosis (LA), are common brain abnormalities in elderly individuals. Evaluating LA on CT is challenging due to the less distinguishable hypoattenuation against white matter. We aimed to develop and validate a segmenting algorithm of LA using CT-MRI paired data.
Methods: We included 744 CT-MRI paired dataset of patients with ischemic stroke from 4 stroke centers. An external test set comprised 411 patients from non-overlapping hospitals. WMH on MRI was segmented using validated software and the segmentation mask was registered onto NCCT space. We compared predicted LA versus ground-truth registered LA and WMH on MRI using Dice similarity coefficient (DSC) and concordance correlation coefficient (ρ).
Results: Mean age (SD) for training and external test datasets were 68.1 (SD 12.7) and 69.2 (SD 13.5) years and 33.2% and 47.9% were female, respectively. In the internal validation dataset, the algorithm achieved a DSC of 0.53, with a volumetric correlation (ρ) of 0.848 with registered LA volume on CT. External validation showed a DSC of 0.527, with ρ values of 0.919 and 0.760 for predicted LA volumes compared to registered LA and WMH volumes on MRI, respectively. Subgroup analysis demonstrated consistent performance across different CT vendors and infarct volumes.
Conclusion: Our deep learning algorithm offers a significant advancement in LA segmentation on CT, bridging the gap between CT and MRI assessments. It improves patient care by providing a consistent, accessible, and accurate method for evaluating LA, supporting both clinical practice and large-scale research.