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A Methodological Approach to Non-invasive Assessments of Vascular Function and Morphology
Published on: February 7, 2015
Evaluation of the algorithm for automatic identification of the common carotid artery in ARTSENS
Ashish Kumar Sahani1, Jayaraj Joseph, Mohanasankar Sivaprakasam
1Department of Electrical Engineering, Indian Institute of Technology Madras, India.
Insights
A new algorithm automatically identifies the common carotid artery (CCA) walls in ultrasound data for the ARTSENS™ device, improving cardiovascular disease risk screening in underserved regions. This image-free tool aids minimal-training operators in accurate arterial compliance measurements.
Area of Science:
- Biomedical Engineering
- Cardiovascular Imaging
- Medical Device Development
Background:
- Arterial compliance (AC) is a key cardiovascular disease (CVD) risk indicator, typically assessed via B-mode ultrasound.
- Low sonologist availability in low- and middle-income countries limits CVD screening.
- The ARTSENS™ device aims to provide an image-free, minimally trained solution for AC measurement.
Purpose of the Study:
- To develop and evaluate a novel algorithm for automatic identification of common carotid artery (CCA) walls from A-mode radio-frequency (RF) ultrasound frames.
- To address the unique challenges of automated CCA wall detection in an image-free ultrasound system (ARTSENS™).
- To improve the accuracy and usability of non-invasive CVD risk screening tools.
Main Methods:
- Development of an algorithm utilizing relative positions and temporal motion of CCA walls for identification.
- Extensive evaluation using simulated datasets with varying signal-to-noise ratios (SNR).
- Clinical validation using RF data from the CCA of 30 human volunteers.
Main Results:
- The algorithm achieved over 90% accuracy in identifying wall positions in simulated data with SNR > 3 dB.
- Successful arterial wall localization in over 70% of clinical measurements from human volunteers.
- Demonstrated real-time performance with minimal trade-offs affecting accuracy and ability to reject irrelevant frames.
Conclusions:
- The developed algorithm effectively identifies CCA walls in image-free ultrasound RF frames, crucial for the ARTSENS™ system.
- This automated approach enhances the feasibility of widespread, accessible CVD risk screening.
- The algorithm's robustness and real-time capability support its integration into practical clinical tools.
Abstract:
Arterial compliance (AC) is an indicator of the risk of cardiovascular diseases (CVDs) and it is generally estimated by B-mode ultrasound investigation. The number of sonologists in low- and middle-income countries is very disproportionate to the extent of CVD. To bridge this gap we are developing an image-free CVD risk screening tool-arterial stiffness evaluation for non-invasive screening (ARTSENS™) which can be operated with minimal training. ARTSENS uses a single element ultrasound transducer to investigate the wall dynamics of the common carotid artery (CCA) and subsequently measure the AC. Identification of the proximal and distal walls of the CCA, in the ultrasound frames, is an important step in the process of the measurement of AC. The image-free nature of ARTSENS creates some unique issues which necessitate the development of a new algorithm that can automatically identify the CCA from a sequence of A-mode radio-frequency (RF) frames. We have earlier presented the concept and preliminary results for an algorithm that employed clues from the relative positions and temporal motion of CCA walls, for identifying the CCA and finding the approximate wall positions. In this paper, we present the detailed algorithm and its extensive evaluation based on simulation and clinical studies. The algorithm identified the wall position correctly in more than 90% of all simulated datasets where the signal-to-noise ratio was greater than 3 dB. The algorithm was then tested extensively on RF data obtained from the CCA of 30 human volunteers, where it successfully located the arterial walls in more than 70% of all measurements. The algorithm could successfully reject frames where the CCA was not present thus assisting the operator to place the probe correctly in the image-free system, ARTSENS. It was demonstrated that the algorithm can be used in real-time with few trade-offs which do not affect the accuracy of CCA identification. A new method for depth range selection that leads to significant performance improvements has also been demonstrated.
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