Impact of Diffusion–Perfusion Mismatch on Predicting Final Infarction Lesion Using Deep Learning

We report a study that validates the impact of diffusion-perfusion mismatch in a deep learning (DL) model predicting the final infarction lesion from baseline magnetic resonance imaging (MRI). From 472 consecutive patients with acute ischemic stroke, we gathered baseline and follow-up MRI having intervals of 3–7 days, and initial and final infarction lesions were segmented. Four U-Net-based DL models from baseline MRI with different combinations of diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI) maps, and initial diffusion-restricted lesion prediction map (
$\text {Pred}_{\text {init}}$
map) were trained to predict the final infarction lesion. Five-fold cross-validation was used for training and testing. As an external test set, 55 patients from another institution were analyzed. Dice similarity coefficient (DSC) was compared between the models and subgroups according to the presence of lesion growth and/or diffusion-perfusion mismatch. The model using the PWI maps and
$\text {Pred}_{\text {init}}$
map showed the best mean DSC (0.422 and 0.486 for internal and external test set, respectively). This model showed better performance in predicting rapid lesion growth compared with the baseline model (mean DSC difference, 0.040; 95% confidence interval: 0.018–0.062). Using the PWI map with initial diffusion-restricted lesion prediction improved the performance of DL model in predicting the final infarction lesion from baseline MRI.