Vector-velocity estimation in swept-scan using a k-space approach

Geng Shi Jeng*, Pai Chi Li

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

The swept-scan technique (i.e., continuously moving a single-crystal transducer during pulse-echo data acquisition) is used in high-frequency, ultrasonic flow imaging. Relative to the conventional step-scan technique, swept scanning improves the rate of data acquisition and enables near-real-time, high-frequency color flow mapping. However, the continuous transducer movement may have nonnegligible effects on accuracy of velocity estimation. This paper introduces a spatial frequency domain (i.e., k-space) approach that quantifies the effects of both lateral and axial motions in a swept scan. It is shown that the k-space representation is equivalent to a Doppler-radio frequency (RF) frequency domain representation, and that transducer movement in the swept-scan technique results in a change in Doppler bandwidth. In addition, a vector velocity estimator is developed based on the proposed k-space approach. Both simulations and flow-phantom experiments were performed to evaluate the performance of the proposed vector velocity estimator. A 45-MHz transducer was scanned at 20 mm/s. The Doppler angle ranged from 29° to 90°, and the flow velocities ranged from 15 to 30 mm/s. The results show that the proposed k-space vector velocity estimator exhibited a mean error of 2.6° for flow-direction estimation, with the standard deviation ranging from 2.2° to 8.2°. In comparison, for the conventional spectral-broadening-based vector velocity estimator ignoring the swept-scan effect, the mean error became 15° and the standard deviations were from 2.7° to 6.6°.

Original languageEnglish
Pages (from-to)947-958
Number of pages12
JournalIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Volume53
Issue number5
DOIs
StatePublished - May 2006

Fingerprint Dive into the research topics of 'Vector-velocity estimation in swept-scan using a k-space approach'. Together they form a unique fingerprint.

  • Cite this