P64 Carotid Artery Tracking with Automated Wall Position Resets Yields Robust Distension Waveforms in Long-term Ultrasonic Recordings

Abstract

Background

Carotid artery tracking has high clinical relevance for the investigation of arterial stiffness indicators like Pulse Wave Velocity (PWV). However, current tracking systems are unreliable and/or not fully automated [1,2]. In this work we propose a novel wall tracking algorithm for long-term ultrasonic recordings, featuring automated beat-to-beat end-diastolic wall position resets.

Methods

Carotid artery ultrasound (Vantage64, Verasonics, USA) and simultaneous ECG (ECG100C, BIOPAC, USA) were acquired from 10 subjects (38 ± 10 years) in 6 repeated measurements, each involving a resting, breathing and handgrip intervention. The ECG triggers an automated algorithm, whose heuristics utilize the hypoechogenic lumen to detect the end-diastolic wall positions in the ultrasound data. Subsequently, wall motion is tracked throughout the cardiac cycle by complex cross-correlation [3]. Further processing yields carotid distension waveforms and local PWV via spatiotemporal fitting of waveform fiducials. The novel per-beat algorithm was benchmarked against a manually initialized per-intervention algorithm, while ground truth wall positions were manually annotated. Performance was assessed for temporal efficiency, spatial accuracy and feature consistency.

Results

Average results show a ~4000% higher temporal efficiency, 20% increased spatial accuracy (µ error: 0.66 to 0.53 [mm]) and 14% improved feature consistency (ơPWV: 2.2 to 1.9 [m/s]) for the per-beat algorithm. Results of exceptional cases reveal even more significant performance, e.g. 60% increased spatial accuracy (µ error: 1.57 to 0.64 [mm]) for gradual drift and 58% improved feature consistency (ơPWV: 1.9 to 0.8 [m/s]) for instant vessel loss (see Figure 1).

Conclusion

The proposed algorithm demonstrates significant temporal efficiency, spatial accuracy and feature consistency, particularly during perturbations. Such robustness is essential for long-term monitoring, making the algorithm a powerful tool in ambulatory vascular research.