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Updated: Jun 18, 2026
Simultaneous PET/MRI Imaging During Mouse Cerebral Hypoxia-ischemia
Published on: September 20, 2015
Deep learning based ischemic lesion markers on non-contrast head CT compared to CTP and DWI
Background:
Quantification of ischemic brain tissue on non-contrast CT (NCCT) in acute ischemic stroke is challenging in the acute setting.
Purpose:
To compare the spatial overlap and imaging marker agreement of acute ischemic regions of interest (ROIs) using deep-learning NCCT (DLNCCT) versus manual NCCT, CTP, and DWI-based ischemic segmentations.
Methods:
We trained a deep learning model to segment ischemic ROIs using manual lesion annotations on admission NCCTs (DLNCCT). DLNCCT ischemic ROIs were compared with manual NCCT delineation, CTP (rCBF<30%/38%), and DWI within 5 hours after the NCCT or after recanalization in four external test sets. Spatial overlap was measured using the Dice Similarity Coefficient (DSC; mean±SD). For each ROI, we derived: average density (HU); modified net water uptake (mNWU in %); total volume (mL); and hypodense (<26HU) volume (mL), and assessed agreement via Bland-Altman (mean difference [95%CI]) and concordance correlation coefficient (CCC) analysis.
Results:
218 training (n=104/89/25 male/female/unknown, mean age 68±14 years) and 762 test cases (n=243/206/313 male/female/unknown, mean age 70±15 years) were used. Spatial overlap was 0.30±0.30 between DLNCCT and manual segmentation, 0.22±0.25 between DLNCCT and DWI, 0.10±0.19/0.14±0.21 between DLNCCT and CTP (rCBF<30%/<38%), and 0.15±0.22/0.21±0.24 between CTP (rCBF<30%/<38%) and DWI. DLNCCT vs. DWI mean differences of ischemic ROI derived imaging markers were -1HU (95%CI:-7;6) for average density (CCC:0.71), 4.9% (95%CI:-7.0;16.8) for mNWU (CCC:0.35), -16mL (95%CI:-108;76) for total volume (CCC:0.57), and -4mL (95%CI:-31;23) for hypodense lesion volume (CCC: 0.75).
Conclusion:
Spatial overlap and agreement of imaging markers between DLNCCT and DWI ischemic ROIs were comparable to CTP and DWI.
Summary Statement:
Ischemic injury on NCCT is identified and quantified by a deep-learning model with accuracy similar to CTP and DWI in stroke patients with a large vessel occlusion.
Key Results:
Deep-learning models can segment ischemic brain tissue on NCCT.Ischemic regions identified by our model demonstrate comparable overlap with ischemic core segmentation on CTP (Dice: 0.21±0.24) and DWI (Dice: 0.22±0.25).Deep learning NCCT showed high agreement with follow-up DWI in determining the hypodense (<26 HU) lesion volume (mean difference -4mL [95%CI:-31;23], CCC: 0.75).
