Simulation of nonstationary spectral analysis of turbulence in the aorta using a modified autoregressive or maximum entropy (AR/ME) method

Flow Velocity Mapping Using Contrast Enhanced High-Frame-Rate Plane Wave Ultrasound and Image Tracking: Methods and Initial in Vitro and in Vivo Evaluation

Ultrasound imaging is the most widely used method for visualising and quantifying blood flow in medical practice, but existing techniques have various limitations in terms of imaging sensitivity, field of view, flow angle dependence, and imaging depth. In this study, we developed an ultrasound imaging velocimetry approach capable of visualising and quantifying dynamic flow, by combining high-frame-rate plane wave ultrasound imaging, microbubble contrast agents, pulse inversion contrast imaging and speckle image tracking algorithms. The system was initially evaluated in vitro on both straight and carotid-mimicking vessels with steady and pulsatile flows and in vivo in the rabbit aorta. Colour and spectral Doppler measurements were also made. Initial flow mapping results were compared with theoretical prediction and reference Doppler measurements and indicate the potential of the new system as a highly sensitive, accurate, angle-independent and full field-of-view velocity mapping tool capable of tracking and quantifying fast and dynamic flows.

Cardiac Doppler blood-flow signal analysis. Part 2. Time/frequency representation based on autoregressive modelling

Doppler spectrograms obtained by using autoregressive (AR) modelling based on the Yule-Walker equations were investigated. A complex AR model using the in-phase and the quadrature components of the Doppler signal was used to provide blood-flow directions. The effect of model orders on the spectrogram estimation was studied using cardiac Doppler blood flow signals taken from 20 patients. The 'final prediction error' (FPE) and the 'Akaike's information criterion' (AIC) provided almost identical results in model-order selection. An index, the spectral envelope area (SEA), was used to evaluate the effect of window duration and sampling frequency on AR Doppler spectrogram estimation. The statistical analysis revealed that the SEA obtained from AR modelling was not sensitive to window duration and sampling frequency. This result verified the consistency of the AR Doppler spectrogram. The white-noise characteristics of the AR modelling error signal indicated that the Doppler blood-flow signal can be adequately modelled as a complex AR process. With appropriate model orders, AR modelling provided better Doppler spectrogram estimates than the periodogram.