This study aims to accurately differentiate penumbra regions from areas of cerebral ischemia in hyperacute stroke using computed tomography perfusion (CTP) imaging. A dedicated image processing pipeline was developed to generate and compare perfusion maps derived from dynamic contrast-enhanced CT data. The tissue-at-risk was identified through integrated visual interpretation of cerebral blood flow (CBF), cerebral blood volume (CBV), and time to peak (TTP) maps, based on hemodynamic criteria reported in the literature. The approach demonstrated high reproducibility and effectively distinguished hypoperfused but salvageable tissue (penumbra) from irreversibly damaged infarct core. These findings establish a foundation for the development of automated segmentation tools to support rapid clinical decision-making in acute stroke settings.

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Differentiation of Penumbra Zones from Ischemia in Hyperacute Stroke on CT Using Artificial Intelligence-Based Perfusion Analysis

  • M. Martin Martina,
  • N. Quiroga,
  • I. Boroni,
  • P. Irusta,
  • V. Di Cesare,
  • V. Herrera,
  • F. Gonzales,
  • R. Isoardi,
  • J. Cortez,
  • D. Fino

摘要

This study aims to accurately differentiate penumbra regions from areas of cerebral ischemia in hyperacute stroke using computed tomography perfusion (CTP) imaging. A dedicated image processing pipeline was developed to generate and compare perfusion maps derived from dynamic contrast-enhanced CT data. The tissue-at-risk was identified through integrated visual interpretation of cerebral blood flow (CBF), cerebral blood volume (CBV), and time to peak (TTP) maps, based on hemodynamic criteria reported in the literature. The approach demonstrated high reproducibility and effectively distinguished hypoperfused but salvageable tissue (penumbra) from irreversibly damaged infarct core. These findings establish a foundation for the development of automated segmentation tools to support rapid clinical decision-making in acute stroke settings.