Ultrasound is one of the non-invasive techniques that are used in clinical diagnostics of carotid artery disease.
OBJECTIVE:
This paper presents software methodology that can be used in combination with this imaging technique to provide additional information about the state of patient-specific artery.
METHODS:
Overall three modules are combined within the proposed methodology. A clinical dataset is used within the deep learning module to extract the contours of the carotid artery. This data is then used within the second module to perform the three-dimensional reconstruction of the geometry of the carotid bifurcation and ultimately this geometry is used within the third module, where the hemodynamic analysis is performed. The obtained distributions of hemodynamic quantities enable a more detailed analysis of the blood flow and state of the arterial wall and could be useful to predict further progress of present abnormalities in the carotid bifurcation.
RESULTS:
The performance of the deep learning module was demonstrated through the high values of relevant common classification metric parameters. Also, the accuracy of the proposed methodology was shown through the validation of results for the reconstructed parameters against the clinically measured values.
CONCLUSION:
The presented methodology could be used in combination with standard clinical ultrasound examination to quickly provide additional quantitative and qualitative information about the state of the patient’s carotid bifurcation and thus ensure a treatment that is more adapted to the specific patient.
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