Arooshi Kumar, MD
Bathla G, Liu Y, Zhang H, Sonka M, Derdeyn C. Computed Tomography Perfusion–Based Prediction of Core Infarct and Tissue at Risk: Can Artificial Intelligence Help Reduce Radiation Exposure? Stroke. 2021;52:e755–e759.
It is well accepted that advanced CT perfusion (CTP) technology can help select patients who could benefit from endovascular treatment (EVT).1 However, CTP imaging is arterial input dependent, requires higher radiation than CT angiography, and utilizes timely and costly post-processing software.2,3 Authors Bathla et al. in this study explored if a machine learning algorithm, a convolutional neural network, which establishes complex relationships between many layers of visual imagery, could estimate cerebral blood flow and area-at-risk similar to the standard arterial dependent prediction method (RAPID).
Retrospective CTP data from 57 patients was split into training/validation (60%/40%) sets. The authors devised and validated separate U-net models, allowing for imaging segmentation, to predict core infarct (CBF) and tissue at risk (Tmax). Once trained, the full sets of 28 input images were sequentially reduced to equitemporal 14, 10, and 7 time points (tp) to further investigate if suboptimal arterial capture could be overcome by this predictive algorithm. The averaged structural similarity index measure (SSIM), a measure of similarity between two images, between the model-derived images and true perfusion maps was compared. For reference, the higher the SSIM, the better the reconstruction technique.