Please use this identifier to cite or link to this item:
|Title:||Modern digital signal processing techniques applied to Doppler ultrasound|
|Authors:||Keeton, Paul Ivan John.|
|Presented at:||University of Leicester|
|Abstract:||Doppler ultrasound is used clinically to detect stenosis in the carotid artery. The presence of stenosis may be identified by disturbed flow patterns distal to the stenosis which cause spectral broadening in the spectrum of the Doppler signal around peak systole. This thesis investigates the ability of the short-time Fourier transform (STFT) and the autoregressive (AR) spectral estimators to perform time-frequency analysis of the non-stationary Doppler signal. Quantitative analysis of the degree of spectral broadening was measured using the spectral broadening index (SBI). A real-time system was developed using a modern DSP board combined with an IBM PC-compatible computer to analyse the Doppler signal in real-time using the STFT and AR algorithms.;The spectral estimators were compared using simulated Doppler spectra contaminated with noise over a range of signal-to-noise ratios (SNRs) and also real clinical Doppler signals recorded from both healthy subjects and patients with varying degrees of stenosis. The SBI was calculated using the mean and maximum frequency envelopes which were extracted from the STFT and AR sonograms using a threshold at -6 dB of the maximum component of each individual spectrum. The results of the analysis shows a strong correlation between the indices calculated using the FFT and AR algorithms. A qualitative improvement in both the appearance of the AR sonograms and the shape of the individual AR spectra was noticeable, however, the estimation of SBI for short data frames is not significantly improved using AR.;The final section of this thesis describes the wavelet transform (WT) and illustrates its application to Doppler ultrasound with two examples. Firstly, it is shown how wavelets can be used as an alternative to the STFT for the extraction of the time-frequency distribution of Doppler ultrasound signals. Secondly, wavelet-based adaptive filtering is implemented for the extraction of maximum blood velocity envelopes in the post processing of Doppler signals.|
|Rights:||Copyright © the author. All rights reserved.|
|Appears in Collections:||Theses, Dept. of Engineering|
Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.