Automated estimation of ischemic core volume on noncontrast-enhanced CT via machine learning

Iris E. Chen, Brian Tsui, Haoyue Zhang, Joe X. Qiao, William Hsu, May Nour, Noriko Salamon, Luke Ledbetter, Jennifer Polson, Corey Arnold, Mersedeh BahrHossieni, Reza Jahan, Gary Duckwiler, Jeffrey Saver, David Liebeskind, Kambiz Nael

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Background: Accurate estimation of ischemic core on baseline imaging has treatment implications in patients with acute ischemic stroke (AIS). Machine learning (ML) algorithms have shown promising results in estimating ischemic core using routine noncontrast computed tomography (NCCT). Objective: We used an ML-trained algorithm to quantify ischemic core volume on NCCT in a comparative analysis to pretreatment magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) in patients with AIS. Methods: Patients with AIS who had both pretreatment NCCT and MRI were enrolled. An automatic segmentation ML approach was applied using Brainomix software (Oxford, UK) to segment the ischemic voxels and calculate ischemic core volume on NCCT. Ischemic core volume was also calculated on baseline MRI DWI. Comparative analysis was performed using Bland–Altman plots and Pearson correlation. Results: A total of 72 patients were included. The time-to-stroke onset time was 134.2/89.5 minutes (mean/median). The time difference between NCCT and MRI was 64.8/44.5 minutes (mean/median). In patients who presented within 1 hour from stroke onset, the ischemic core volumes were significantly (p = 0.005) underestimated by ML-NCCT. In patients presented beyond 1 hour, the ML-NCCT estimated ischemic core volumes approximated those obtained by MRI-DWI and with significant correlation (r = 0.56, p < 0.001). Conclusion: The ischemic core volumes calculated by the described ML approach on NCCT approximate those obtained by MRI in patients with AIS who present beyond 1 hour from stroke onset.

Original languageEnglish
JournalInterventional Neuroradiology
StateAccepted/In press - 2022
Externally publishedYes


  • Acute ischemic stroke
  • machine learning
  • noncontrast CT


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