Adaptive flow detector and estimator for ultrasound high frame rate vector flow imaging

Abstract

Cardiovascular diseases is a leading cause of death worldwide and improvement of the

corresponding screening tool is the best way to deal with this clinical problem. In this thesis

we attempted to develop a framework of ultrasound high frame rate vector flow imaging

(VFI) by emphasizing on the design of corresponding flow detector and flow estimator. We

believe that the high temporal resolution and the complex blood flow visualization ability of

high frame rate VFI enables it to be further developed as a reliable flow imaging modality

for cardiological examination.

In order to achieve high temporal resolution, fast data acquisition algorithm was applied

in the framework. Doppler signals acquired using this acquisition algorithm have two

unique characteristics comparing with conventional data acquisition algorithm: (1) widen

spectral bandwidth and (2) greater clutter to blood signal ratio. These signal characteristics

give rise to unique signal processing. In addition, complex blood flow pattern, which

is common in cardiological examination, induces extra challenges in implementing high

frame rate VFI. In this thesis, flow detector which is adaptive to different flow scenarios

and high dynamic range 2D flow estimator were presented.

The proposed flow detector employes K-means++ clustering algorithm to classify clutter

components from acquired Doppler signals. As a performance analysis, Field II simulation

studies were performed by a parabolic flow phantom (flow velocity: 10mm/s to

200mm/s; tissue motion: 10mm/s; beam-flow angle: 60?). The post-filtered Doppler power

map and BCR were used as qualitative and quantitativemeasures of detectors performance.

Analyzed result has indicated that, as compared with clutter downmixing detector and

eigen-based detector, the proposed flow detector could classify and suppress clutter component

more effectively. Results also suggested that the proposed flow detector is more

adaptive to slow flow scenarios where existing flow detectors failed to distinguish between

blood and clutter components.

For the proposed flow estimator, it was characterized by the interpolation of speckle

tracking results in Lagrangian reference frame. The estimation bias and RMS error were

calculated for different flow scenarios (flow velocity: 100mm/s to 500mm/s; beam-flow

angle: 15? to 60?). It was found that the proposed flow estimator provides higher dynamic

range than conventional speckle tracking-based flow estimator. Nonetheless, it is also observed

that the estimation variances and errors increases in slow flow scenarios.

In order to demonstrate the medical potential of the proposed high frame rate VFI

framework. A carotid bifurcation simulation model with realistic blood flow pattern calculated

using computational fluid dynamic software was applied in the performance evaluation

study. In the VFI image obtained, complex blood flow pattern was readily visualized.

In contrast, conventional ultrasound flow imaging was only able to estimate axial velocity

map and thus lead to many ambiguities in analyzing the complex blood flow pattern. It

proved that ultrasound high frame rate VFI has the potential to be further developed into a

new cardiological examination technique.