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Review

Understanding CT Perfusion in Acute Ischemic Stroke: How Algorithms Shape Perfusion Maps

by
Nicola Morelli
1,*,
Marco Spallazzi
2,
Marina Biondi
1,
Eugenia Rota
3 and
Davide Colombi
4
1
Neuroradiology Unit, Guglielmo da Saliceto Hospital, 29121 Piacenza, Italy
2
Neurology Unit, University of Parma, 43121 Parma, Italy
3
Neurology Unit, San Giacomo Hospital, 15067 Novi Ligure, Italy
4
Department of Diagnostic Imaging, Centro Diagnostico Rocca, 29121 Piacenza, Italy
*
Author to whom correspondence should be addressed.
Diagnostics 2026, 16(12), 1831; https://doi.org/10.3390/diagnostics16121831 (registering DOI)
Submission received: 2 May 2026 / Revised: 2 June 2026 / Accepted: 10 June 2026 / Published: 12 June 2026
(This article belongs to the Special Issue Clinical Advances and Applications in Neuroradiology: 2nd Edition)

Abstract

CT perfusion (CTP) is widely used in acute ischemic stroke imaging, particularly for treatment selection beyond conventional time windows. However, automated perfusion maps are not direct measurements of irreversible tissue injury, but estimates shaped by deconvolution strategy, temporal correction, dispersion handling, and software-specific thresholds. This review provides a clinically oriented explanation of how CTP algorithms influence the estimation of ischemic core and hypoperfused tissue. Particular attention is given to singular value decomposition (SVD) methods, Bayesian approaches, and timing parameters, including time to maximum (Tmax), Delay, time to peak (TTP), and mean transit time (MTT). Differences in residue function estimation and threshold definition may generate variable outputs across software platforms, even from the same source dataset. Perfusion thresholds should therefore not be treated as universally interchangeable. CTP findings should be integrated with clinical status, non-contrast CT, CT angiography (CTA), collateral status, occlusion site, and imaging-to-treatment context, serving as decision-support tools rather than isolated measures of tissue viability.
Keywords: CT perfusion; acute ischemic stroke; deconvolution; perfusion imaging; Bayesian algorithm; Tmax; ischemic core; stroke imaging CT perfusion; acute ischemic stroke; deconvolution; perfusion imaging; Bayesian algorithm; Tmax; ischemic core; stroke imaging

Share and Cite

MDPI and ACS Style

Morelli, N.; Spallazzi, M.; Biondi, M.; Rota, E.; Colombi, D. Understanding CT Perfusion in Acute Ischemic Stroke: How Algorithms Shape Perfusion Maps. Diagnostics 2026, 16, 1831. https://doi.org/10.3390/diagnostics16121831

AMA Style

Morelli N, Spallazzi M, Biondi M, Rota E, Colombi D. Understanding CT Perfusion in Acute Ischemic Stroke: How Algorithms Shape Perfusion Maps. Diagnostics. 2026; 16(12):1831. https://doi.org/10.3390/diagnostics16121831

Chicago/Turabian Style

Morelli, Nicola, Marco Spallazzi, Marina Biondi, Eugenia Rota, and Davide Colombi. 2026. "Understanding CT Perfusion in Acute Ischemic Stroke: How Algorithms Shape Perfusion Maps" Diagnostics 16, no. 12: 1831. https://doi.org/10.3390/diagnostics16121831

APA Style

Morelli, N., Spallazzi, M., Biondi, M., Rota, E., & Colombi, D. (2026). Understanding CT Perfusion in Acute Ischemic Stroke: How Algorithms Shape Perfusion Maps. Diagnostics, 16(12), 1831. https://doi.org/10.3390/diagnostics16121831

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