EPFL | Biomedical Imaging Group | Doppler Vectorization

A Feasability Study of Color Flow Doppler Vectorization for Automated Blood Flow Monitoring

R. Schorer, A. Badoual, B. Bastide, A. Vandebrouck, M. Licker, D. Sage

Journal of Clinical Monitoring and Computing, vol. 31, no. 6, pp. 1167–1175, December 2017.

An ongoing issue in vascular medicine is the measure of the blood flow. Catheterization remains the gold standard measurement method, although non-invasive techniques are an area of intense research. We hereby present a computational method for real-time measurement of the blood flow from color flow Doppler data, with a focus on simplicity and monitoring instead of diagnostics. We then analyze the performance of a proof-of-principle software implementation. We imagined a geometrical model geared towards blood flow computation from a color flow Doppler signal, and we developed a software implementation requiring only a standard diagnostic ultrasound device. Detection performance was evaluated by computing flow and its determinants (flow speed, vessel area, and ultrasound beam angle of incidence) on purposely designed synthetic and phantom-based arterial flow simulations. Flow was appropriately detected in all cases. Errors on synthetic images ranged from nonexistent to substantial depending on experimental conditions. Mean errors on measurements from our phantom flow simulation ranged from 1.2 to 40.2% for angle estimation, and from 3.2 to 25.3% for real-time flow estimation. This study is a proof of concept showing that accurate measurement can be done from automated color flow Doppler signal extraction, providing the industry the opportunity for further optimization using raw ultrasound data.

@ARTICLE(http://bigwww.epfl.ch/publications/schorer1701.html,
AUTHOR="Schorer, R. and Badoual, A. and Bastide, B. and Vandebrouck, A.
	and Licker, M. and Sage, D.",
TITLE="A Feasability Study of Color Flow {D}oppler Vectorization for
	Automated Blood Flow Monitoring",
JOURNAL="Journal of Clinical Monitoring and Computing",
YEAR="2017",
volume="31",
number="6",
pages="1167--1175",
month="December",
note="")

© 2017 Springer. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from Springer. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.