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Summary
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This study introduces an automated method for analyzing cerebral blood flow using digital subtraction angiography (DSA). The technique accurately measures circulation time, improving efficiency in diagnosing conditions like carotid stenosis.

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Area of Science:

  • Neuroradiology
  • Medical Imaging Analysis
  • Cerebrovascular Physiology

Background:

  • Digital subtraction angiography (DSA) provides critical blood flow data.
  • Manual region-of-interest (ROI) selection in DSA is labor-intensive and time-consuming.
  • Automated analysis methods are needed to enhance efficiency and accuracy.

Purpose of the Study:

  • To develop and validate an automated technique for segmenting arterial, capillary, and venous vasculatures in DSA.
  • To assess the utility of time-density curve parameters from automated segmentation for evaluating cerebral circulation time.
  • To investigate the correlation between automated capillary time-density curve features and cerebral circulation time in patients with carotid stenosis.

Main Methods:

  • Retrospective analysis of DSA data from 36 patients with unilateral carotid stenosis.
  • Development of an automated algorithm for segmenting vascular networks and generating time-density curves.
  • Calculation of time-density curve parameters, including the full width at half maximum (FWHM).
  • Comparison of automated results with established measures of cerebral circulation time.

Main Results:

  • The automated technique successfully segmented arterial, capillary, and venous phases.
  • Time-density curves were generated for each vascular compartment.
  • The full width at half maximum (FWHM) of the time-density curve for the automatically segmented capillary vasculature showed a strong correlation with cerebral circulation time.
  • The automated method significantly reduces the time required for analysis compared to manual ROI selection.

Conclusions:

  • Automated time-density curve analysis of DSA is a feasible and efficient method for assessing cerebral blood flow.
  • The FWHM of the capillary time-density curve derived from automated segmentation serves as a reliable indicator of cerebral circulation time.
  • This technique holds promise for improving the diagnostic workflow in cerebrovascular diseases like carotid stenosis.