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Updated: Jan 10, 2026
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Prediction of Malignant Acute Middle Cerebral Artery Infarction Via Dual-Energy Computed Tomography-Derived
Kai Shang1, Shuhao Wang2, Lifang Ye1
1Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Objective:
This study evaluated the utility of dual-energy computed tomography angiography (DECT) in predicting malignant middle cerebral artery infarction (MMI) in acute ischemic stroke (AIS).
Methods:
A total of 153 AIS patients undergoing DECT within 12 hours of symptom onset and follow-up imaging within 48 hours were included. DECT-derived parameters-virtual non-contrast (VNC) images, virtual monoenergetic (VM) images (40/60 keV), iodine concentration (IC), effective atomic number (Zeff), and spectral Hounsfield unit curve slope (λHU)-were analyzed. Clinical and imaging parameters were compared between MMI (n = 34, 22.2%) and non-MMI groups.
Results:
MMI patients exhibited higher National Institute of Health Stroke Scale (NIHSS) scores, more frequent internal carotid artery (ICA) occlusion, larger baseline infarct volumes, and significantly lower IC, λHU, Zeff, VNC, and VM values (40/60 keV) compared with non-MMI patients (all P < 0.05). Combined DECT parameters demonstrated superior diagnostic performance for MMI prediction (area under the curve [AUC] 0.98; sensitivity 88%, specificity 98%), outperforming individual parameters and clinical predictors (all P < 0.05). Integrating clinical features (NIHSS, admission infarct volume, ICA occlusion) with DECT parameters achieved optimal performance (AUC 1.00; sensitivity 100%, specificity 93%), comparable to final infarct volume (P = 0.08).
Conclusions:
DECT-derived quantitative parameters, particularly when combined with clinical data, serve as reliable early biomarkers for identifying stroke patients at high risk of MMI, offering predictive accuracy akin to final infarct outcomes. This approach may guide timely intervention in malignant cerebral edema.
