Ultrasound Measurement of Vascular Distensibility Based on Edge Detection and Speckle Tracking Using Ultrasound DICOM Data

Enhancing flow-mediated dilation analysis by optimizing an open-source software with automated edge detection

Flow-mediated dilation (FMD) is a common measure of endothelial function and an indicator of vascular health. Automated software methods exist to improve the speed and accuracy of FMD analysis. Compared with commercial software, open-source software offers similar capabilities at a much lower cost while allowing for increased customization specific to users' needs. We introduced modifications to an existing open-source software, FloWave.us to better meet FMD analysis needs. The purpose of this study was to compare the repeatability and reliability of the modified FloWave.us software to the original software and to manual measurements. To assess these outcomes, duplex ultrasound imaging data from the popliteal artery in older adults were analyzed. The average percent FMD for the modified software was 6.98 ± 3.68% and 7.27 ± 3.81% for observer 1 and 2 respectively, compared with 9.17 ± 4.91% and 10.70 ± 4.47% with manual measurements and 5.07 ± 31.79% with the original software for observer 1. The modified software and manual methods demonstrated higher intraobserver intraclass correlation coefficients (ICCs) for repeated measures for baseline diameter, peak diameter, and percent FMD compared with the original software. For percent FMD, the interobserver ICC was 0.593 for manual measurements and 0.723 for the modified software. With the modified method, an average of 97.7 ± 2.4% of FMD videos frames were read, compared with only 17.9 ± 15.0% frames read with the original method when analyzed by the same observer. Overall, this work further establishes open-source software as a robust and viable tool for FMD analysis and demonstrates improved reliability compared with the original software.NEW & NOTEWORTHY This study improves edge detection capabilities and implements noise reduction strategies to optimize an existing open-source software's suitability for flow-mediated dilation (FMD) analysis. The modified software improves the precision and reliability of FMD analysis compared with the original software algorithm. We demonstrate that this modified open-source software is a robust tool for FMD analysis.

Autonomous Robotic Ultrasound Vascular Imaging System With Decoupled Control Strategy for External-Vision-Free Environments

OBJECTIVE

Ultrasound (US) probes scan over the surface of the human body to acquire US images in clinical vascular US diagnosis. However, due to the deformation and specificity of different human surfaces, the relationship between the scan trajectory of the skin and the internal tissues is not fully correlated, which poses a challenge for autonomous robotic US imaging in a dynamic and external-vision-free environment. Here, we propose a decoupled control strategy for autonomous robotic vascular US imaging in an environment without external vision.

METHODS

The proposed system is divided into outer-loop posture control and inner-loop orientation control, which are separately determined by a deep learning (DL) agent and a reinforcement learning (RL) agent. First, we use a weakly supervised US vessel segmentation network to estimate the probe orientation. In the outer loop control, we use a force-guided reinforcement learning agent to maintain a specific angle between the US probe and the skin in the dynamic imaging processes. Finally, the orientation and the posture are integrated to complete the imaging process.

RESULTS

Evaluation experiments on several volunteers showed that our RUS could autonomously perform vascular imaging in arms with different stiffness, curvature, and size without additional system adjustments. Furthermore, our system achieved reproducible imaging and reconstruction of dynamic targets without relying on vision-based surface information.

CONCLUSION AND SIGNIFICANCE

Our system and control strategy provides a novel framework for the application of US robots in complex and external-vision-free environments.

Semiautomated Software to Improve Stability and Reduce Operator-Induced Variation in Vascular Ultrasound Speckle Tracking

OBJECTIVES

Ultrasound is useful in predicting arteriovenous fistula (AVF) maturation, which is essential for hemodialysis in end-stage renal disease patients. We developed ultrasound software that measures circumferential vessel wall strain (distensibility) using conventional ultrasound Digital Imaging and Communications in Medicine (DICOM) data. We evaluated user-induced variability in measurement of arterial wall distensibility and upon finding considerable variation we developed and tested 2 methods for semiautomated measurement.

METHODS

Ultrasound scanning of arteries of 10 subjects scheduled for AVF surgery were performed. The top and bottom of the vessel wall were tracked using the Kanade-Lucas-Tomasi (KLT) feature-tracking algorithm over the stack of images in the DICOM cine loops. The wall distensibility was calculated from the change of vessel diameter over time. Two semiautomated methods were used for comparison.

RESULTS

The location of points selected by users for the cine loops varied significantly, with a maximum spread of up to 120 pixels (7.8 mm) for the top and up to 140 pixels (9.1 mm) for the bottom of the vessel wall. This variation in users' point selection contributed to the variation in distensibility measurements (ranging from 5.63 to 41.04%). Both semiautomated methods substantially reduced variation and were highly correlated with the median distensibility values obtained by the 10 users.

CONCLUSIONS

Minimizing user-induced variation by standardizing point selection will increase reproducibility and reliability of distensibility measurements. Our recent semiautomated software may help expand use in clinical studies to better understand the role of vascular wall compliance in predicting the maturation of fistulas.