AI-Assisted Edema Map Optimization Improves Infarction Detection in Twin-Spiral Dual-Energy CT

Objective: This study aimed to evaluate whether modifying the post-processing algorithm of twin-spiral dual-energy computed tomography (DECT) improves infarct detection compared to conventional dual-energy or single-energy CT (DECT/SECT) following endovascular therapy (EVT) for large vessel occlusion (LVO). Methods: We retrospectively analyzed 52 patients who underwent twin-spiral DECT after endovascular stroke therapy. Ten patients were used to generate a device-specific parameter (“y”) using an AI-based neural network (SynthSR). This parameter was integrated into the post-processing algorithm for edema map generation. Quantitative Hounsfield unit (HU) measurements were used to assess density differences in ischemic brain tissue across conventional virtual non-contrast (VNC) images and edema maps. Results: The median HU of infarcted tissue in conventional mixed DECT was 33.73 ± 4.58, compared to 22.96 ± 3.81 in default VNC images. Edema maps with different smoothing filter settings showed values of 14.39 ± 4.96, 14.50 ± 3.75, and 15.05 ± 2.65, respectively. All edema maps demonstrated statistically significant HU differences of infarcted tissue compared to conventional VNC images (p<0.001) while maintaining the density values of non-infarcted brain tissue. Conclusion: Enhancing the post-processing algorithm of conventional virtual non-contrast imaging improves infarct detection compared to standard mixed or virtual non-contrast reconstructions in dual-energy CT.