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Arterial input function segmentation based on a contour geodesic model for tissue at risk identification in ischemic

Sukhdeep Singh Bal1,2,3, Ke Chen1, Fan-Pei Gloria Yang2,4,5,6

  • 1Department of Mathematical Sciences, University of Liverpool, Liverpool, UK.

Medical Physics
|January 31, 2022
PubMed
Summary

A new method for selecting the arterial input function (AIF) improves accuracy in estimating perfusion parameters like cerebral blood flow (CBF) for ischemic stroke diagnosis. This technique enhances the reliability of perfusion imaging analysis.

Keywords:
arterial input function measurementscerebral blood flowcerebral perfusion imagingdynamic susceptibility contrastvariation model

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Area of Science:

  • Medical Imaging
  • Neuroscience
  • Biomedical Engineering

Background:

  • Perfusion parameters, including cerebral blood flow (CBF) and Tmax, are crucial for diagnosing and predicting outcomes in ischemic stroke.
  • Accurate estimation of these parameters relies on the selection of the arterial input function (AIF), a critical step in clinical practice.

Purpose of the Study:

  • To develop and validate a novel technique for arterial input function (AIF) selection in perfusion imaging.
  • To improve the accuracy and reliability of perfusion parameter estimation for ischemic stroke assessment.

Main Methods:

  • A variational segmentation model incorporating a distance function with geometric constraints was employed.
  • Discrete total variation and energy minimization were used to identify arterial regions.
  • Matrix analysis was applied to select the AIF with the highest peak within the segmented region.

Main Results:

  • The proposed AIF selection method demonstrated superior arterial features, including higher peak position and faster attenuation, compared to existing methods.
  • Perfusion parameters (mean CBF and Tmax) estimated using the novel AIF were higher than those from traditional methods.
  • Ischemic regions were accurately delineated on perfusion maps derived from the proposed method.

Conclusions:

  • The developed AIF segmentation framework outperforms current clinical standards for perfusion imaging.
  • Perfusion parameters derived using the proposed AIF selection are more accurate and reliable.
  • This method holds potential for integration into routine perfusion imaging calculations.