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. 2025 Mar;35(1):185-195.
doi: 10.1007/s00062-024-01471-7. Epub 2024 Nov 25.

Deep Learning Based Detection of Large Vessel Occlusions in Acute Ischemic Stroke Using High-Resolution Photon Counting Computed Tomography and Conventional Multidetector Computed Tomography

Jan Boriesosdick  1 , Iram Shahzadi  2   3 , Long Xie  4 , Bogdan Georgescu  4 , Eli Gibson  4 , Lynn Johann Frohwein  5 , Saher Saeed  1 , Nina P Haag  1 , Sebastian Horstmeier  1 , Christoph Moenninghoff  1 , Julius Henning Niehoff  1 , Alexey Surov  1 , Jan Borggrefe  1 , Jan Robert Kroeger  1
Affiliations

Deep Learning Based Detection of Large Vessel Occlusions in Acute Ischemic Stroke Using High-Resolution Photon Counting Computed Tomography and Conventional Multidetector Computed Tomography

Jan Boriesosdick et al. Clin Neuroradiol. 2025 Mar.

Abstract

Purpose: Deep learning (DL) methods for detecting large vessel occlusion (LVO) in acute ischemic stroke (AIS) show promise, but the effect of computed tomography angiography (CTA) image quality on DL performance is unclear. Our study investigates the impact of improved image quality from Photon Counting Computed Tomography (PCCT) on LVO detection in AIS using a DL-based software prototype developed by a commercial vendor, which incorporates a novel deep learning architecture.

Materials and methods: 443 cases that underwent stroke diagnostics with CTA were included. Positive cases featured vascular occlusions in the Internal Carotid Artery (ICA), M1, and M2 segments of the Middle Cerebral Artery (MCA). Negative cases showed no vessel occlusion on CTA. The performance of the DL-based LVO detection software prototype was assessed using Syngo.via version VB80.

Results: Our study included 267 non-occlusion cases and 176 cases. Among them, 150 cases were scanned via PCCT (no occlusion = 100, ICA and M1 = 41, M2 = 9), while 293 cases were scanned using conventional CT (no occlusion = 167, ICA and M1 = 89, M2 = 37). Independent of scanner type, the algorithm showed sensitivity and specificity of 70.5 and 98.9% for the detection of all occlusions. DL algorithm showed improved performance after excluding M2 occlusions (sensitivity 86.2%). After stratification by scanner type, the algorithm showed significantly a trend towards better performance (p = 0.013) on PCCT CTA images for the detection of all occlusions (sensitivity 84.0%, specificity 99%) compared to CTA images from conventional CT scanner (sensitivity 65.1%, specificity 98.8%). The detection of M2 occlusions was also better on PCCT CTA images (sensitivity 55.6%) compared to conventional scanner CTA images (sensitivity 18.9%), but the sample size for M2 occlusions was limited, and further research is needed to confirm these findings.

Conclusion: Our study suggests that PCCT CTA images may offer improved detection of large vessel occlusion, particularly for M2 occlusions. However further research is needed to confirm these findings. One of the limitations of our study is the inability to exclude the presence of a perfusion deficit, despite ruling out vascular occlusion, due to the lack of CT perfusion (CTP) imaging data. Future research may investigate CNNs by leveraging both CTA and CTP images from PCCT for improved performance.

Keywords: Computed tomography angiography; Deep learning; Large vessel occlusion; Photon counting computed tomography.

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Conflict of interest statement

Conflict of interest: J. Boriesosdick, S. Saeed, N.P. Haag, S. Horstmeier, C. Moenninghoff, J.H. Niehoff and A. Surov declare that they have no competing interests. I. Shahzadi, L. Xie, B. Georgescu, E. Gibson, L.J. Frohwein are employees of Siemens Healthineers. J. Borggrefe received honoraria for scientific lectures from Philips Healthcare and Siemens Healthineers. and J.R. Kroeger received honoraria for scientific lectures from GE Healthcare and for clinical advisory board membership from Siemens Healthineers.

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