Although Doppler ultrasound may be used for the study of various types of motion within the body, its major uses are the detection and quantification of flow in the heart, arteries and veins. Doppler signals from these sources contain a great deal of information about flow, so developments in Doppler technology have led to a vast increase in the number of non-invasive blood velocity investigations carried out in all areas of medicine. There are now very many types of Doppler ultrasound devices available commercially for detecting, measuring and imaging blood flow and other movements within the body.
This thesis aims to implement a pulsed wave Doppler system by building the required blocks to get the Doppler information. This system based on demodulating the received signal with the transmitted signal, getting rid of the transmitted frequency using a low pass filter, integrating the required sample volume, applying a hardware wall motion filter, sampling the Doppler signal and finally reconstructing the real time spectrogram using the computer.
One of the major problems of Doppler systems is the wall motion signal which comes from the skeletal muscles that vibrate under sustained contraction, and generate strong low frequency side-band clutter in the Doppler signal. So, the thesis also aims to introduce a new nonparametric method for clutter rejection. We consider the Doppler data sampled using a sufficiently large dynamic range to allow for the clutter rejection to be implemented on the digital side. The Doppler signal is modeled as the summation of the true velocity signal, a clutter component, and a random noise component. To simplify the analysis, the first two components are assumed as deterministic yet unknown signals. The Doppler data are collected from the sample volume of interest as well as from several sample volumes in its neighborhood. Given that the shape of the clutter component will be similar in all these signals and given its relatively higher magnitude, it is possible to separate this component using blind source separation methods. We describe an efficient implementation methodology and demonstrate the efficiency of this new approach.
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