Digital quadrature demodulation of Doppler signals

Dissertação de mest., Engenharia Electrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2009Ultrasound has for many years been an important tool in the detection and quantification of various health problems. In vascular diseases, for example, the ultrasound can be applied with different techniques such as Transit-Time Flow Measurements (TTFM) [36], Doppler [28][40][41][42] and elastography [37] [38]. Research has been developed focusing the signal processing of Doppler ultrasound signals. In an ongoing project, named Desarrollo de Sistemas Ultras´onicos y Computacionales para Diagn´ostico Cardiovascular (SUCoDiC), Doppler ultrasound signals are processed by an analog signal processing unit, in order to obtain the inphase (I) and quadrature (Q) components of the Doppler ultrasound signals, to allow directional blood flow separation. Problems associated with unbalanced channels’ gain of the employed analog system have been detected, resulting in an inapropriate directional blood flow separation. This thesis reports the research performed to eliminate such problems by substituting the analog system’s demodulator by digital signal processing approaches aiming at the achievement of the same goals, i.e., obtaining the Doppler ultrasound signal’s inphase (I) and quadrature (Q) components, for efficient directional blood flow separation. Five digital quadrature techniques have been studied to achieve such goal. Also, given technical constraints imposed by the nature of the Doppler ultrasound signals to be used, and limitations of the sampling rate of the Analog-to-Digital Converter (ADC) used, two strategies to acquire the Doppler ultrasound signals were studied. Such strategies involved the sampling of a downconversion version of the Doppler ultrasound signals (by application of the heterodyne function) and direct sampling of the Doppler ultrasound signals using uniform bandpass sampling. From the results obtained, three approaches are selected and proposed for real time implementation. Comparison between both signal sampling strategies employed are also presente

Embedded system for real-time digital processing of medical Ultrasound Doppler signals

Ultrasound (US) Doppler systems are routinely used for the diagnosis of cardiovascular diseases. Depending on the application, either single tone bursts or more complex waveforms are periodically transmitted throughout a piezoelectric transducer towards the region of interest. Extraction of Doppler information from echoes backscattered from moving blood cells typically involves coherent demodulation and matched filtering of the received signal, followed by a suitable processing module. In this paper, we present an embedded Doppler US system which has been designed as open research platform, programmable according to a variety of strategies in both transmission and reception. By suitably sharing the processing tasks between a state-of-the-art FGPA and a DSP, the system can be used in several medical US applications. As reference examples, the detection of microemboli in cerebral circulation and the measurement of wall _distension_ in carotid arteries are finally presented

In-Suit Doppler Technology Assessment

The objective of this program was to perform a technology assessment survey of non-invasive air embolism detection utilizing Doppler ultrasound methodologies. The primary application of this technology will be a continuous monitor for astronauts while performing extravehicular activities (EVA's). The technology assessment was to include: (1) development of a full understanding of all relevant background research; and (2) a survey of the medical ultrasound marketplace for expertise, information, and technical capability relevant to this development. Upon completion of the assessment, LSR was to provide an overview of technological approaches and R&D/manufacturing organizations

Synthetic aperture radar demonstration kit for signal processing education

A Synthetic Aperture Radar scale model has been developed to improve signal processing teaching. Based on low frequency ultrasound transmission, it is a low cost demonstration kit. The overall software is directly running on Matlab® and allows easy and realtime modifications. This educational tool can be used to illuminate a scene using different waveforms, and then see the effects on the formed image. It can also be used in a more advanced way to test different signal processing in order to improve image focusing or to reduce computation burden

Value of superb microvascular imaging ultrasonography in the diagnosis of carpal tunnel syndrome: Compared with color Doppler and power Doppler.

The aim of this study was to compare the value of superb microvascular imaging (SMI) in carpal tunnel syndrome (CTS) with that of color Doppler ultrasonography (CDUS) and power Doppler ultrasonography (PDUS).Fifty patients with symptomatic CTS and 25 healthy volunteers were enrolled. The cross-sectional area (CSA), CDUS score, PDUS score, and SMI score of the median nerve (MN) at the carpal tunnel were recorded. The value of different ultrasonography (US) diagnostic strategies was calculated.The blood flow display ratio in the MN of the healthy volunteers had no statistical difference between CDUS, PDUS, and SMI (20%, 32%, and 48%, respectively, P \u3e.05). The blood flow display ratio for SMI in patients was significantly higher than that of CDUS and PDUS (90%, 52%, and 60%, respectively,

Regularized adaptive long autoregressive spectral analysis

This paper is devoted to adaptive long autoregressive spectral analysis when (i) very few data are available, (ii) information does exist beforehand concerning the spectral smoothness and time continuity of the analyzed signals. The contribution is founded on two papers by Kitagawa and Gersch. The first one deals with spectral smoothness, in the regularization framework, while the second one is devoted to time continuity, in the Kalman formalism. The present paper proposes an original synthesis of the two contributions: a new regularized criterion is introduced that takes both information into account. The criterion is efficiently optimized by a Kalman smoother. One of the major features of the method is that it is entirely unsupervised: the problem of automatically adjusting the hyperparameters that balance data-based versus prior-based information is solved by maximum likelihood. The improvement is quantified in the field of meteorological radar