UNIGE document Scientific Article
previous document  unige:100659  next document
add to browser collection

A feasability study of color flow doppler vectorization for automated blood flow monitoring

Badoual, A
Bastide, B
Vandebrouck, A
Sage, D
Published in Journal of Clinical Monitoring and Computing. 2017, vol. 31, no. 6, p. 1167-1175
Abstract 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.
PMID: 27838880
Full text
Article (Published version) (862 Kb) - document accessible for UNIGE members only Limited access to UNIGE
Research group Dysfonctions cardio-pulmonaires et cérébrales (278)
(ISO format)
SCHORER, Raoul et al. A feasability study of color flow doppler vectorization for automated blood flow monitoring. In: Journal of Clinical Monitoring and Computing, 2017, vol. 31, n° 6, p. 1167-1175. doi: 10.1007/s10877-016-9953-2 https://archive-ouverte.unige.ch/unige:100659

309 hits

0 download


Deposited on : 2017-12-20

Export document
Format :
Citation style :