Neurosciences des Croyances

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

Singer L, Heinze D, Möhle TA, Sekita A, Mennecke A, Lang S, Gerner ST, Schwab S, Dörfler A, Schmidt MA

Brain Sci . 2025;15 (8)

📅 31/07/2025 PMID : 40867153 DOI : 10.3390/brainsci15080821

Résumé

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 CT (DECT) and Single-Energy CT (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