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Proceedings Paper

Feasibility of pulse wave velocity estimation from low frame rate US sequences in vivo
Author(s): Maria Zontak; Matthew Bruce; Michelle Hippke; Alan Schwartz; Matthew O'Donnell
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Paper Abstract

The pulse wave velocity (PWV) is considered one of the most important clinical parameters to evaluate CV risk, vascular adaptation, etc. There has been substantial work attempting to measure the PWV in peripheral vessels using ultrasound (US). This paper presents a fully automatic algorithm for PWV estimation from the human carotid using US sequences acquired with a Logic E9 scanner (modified for RF data capture) and a 9L probe. Our algorithm samples the pressure wave in time by tracking wall displacements over the sequence, and estimates the PWV by calculating the temporal shift between two sampled waves at two distinct locations. Several recent studies have utilized similar ideas along with speckle tracking tools and high frame rate (above 1 KHz) sequences to estimate the PWV. To explore PWV estimation in a more typical clinical setting, we used focused-beam scanning, which yields relatively low frame rates and small fields of view (e.g., 200 Hz for 16.7 mm filed of view). For our application, a 200 Hz frame rate is low. In particular, the sub-frame temporal accuracy required for PWV estimation between locations 16.7 mm apart, ranges from 0.82 of a frame for 4m/s, to 0.33 for 10m/s. When the distance is further reduced (to 0.28 mm between two beams), the sub-frame precision is in parts per thousand (ppt) of the frame (5 ppt for 10m/s). As such, the contributions of our algorithm and this paper are: 1. Ability to work with low frame-rate ( 200Hz) and decreased lateral field of view. 2. Fully automatic segmentation of the wall intima (using raw RF images). 3. Collaborative Speckle Tracking of 2D axial and lateral carotid wall motion. 4. Outlier robust PWV calculation from multiple votes using RANSAC. 5. Algorithm evaluation on volunteers of different ages and health conditions.

Paper Details

Date Published: 13 March 2017
PDF: 8 pages
Proc. SPIE 10139, Medical Imaging 2017: Ultrasonic Imaging and Tomography, 1013906 (13 March 2017); doi: 10.1117/12.2255535
Show Author Affiliations
Maria Zontak, Univ. of Washington (United States)
Matthew Bruce, Univ. of Washington (United States)
Michelle Hippke, Alliance HealthCare Services (United States)
Alan Schwartz, Alliance HealthCare Services (United States)
Matthew O'Donnell, Univ. of Washington (United States)

Published in SPIE Proceedings Vol. 10139:
Medical Imaging 2017: Ultrasonic Imaging and Tomography
Neb Duric; Brecht Heyde, Editor(s)

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