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Validating the Automatic Independent Component Analysis of DSA
J-S Hong1, Y-H Kao2, F-C Chang3,4
1From the Department of Biomedical Imaging and Radiological Sciences (J.-S.H., Y.-H.K.).
AJNR. American Journal of Neuroradiology
|January 26, 2019
Summary
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.
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.
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