Signal processing in color flow imaging for blood flow measurement

• Main Author: Chin, Khai Sing.
• Other Authors: Zhang Cishen
• Format: Final Year Project
• Language: English
• Published: 2009
• Subjects: DRNTU::Engineering::Chemical engineering::Biotechnology
• Online Access: http://hdl.handle.net/10356/16385
• Institution: Nanyang Technological University

Color Flow Imaging is a popular non-invasive imaging method of blood flow in medicine. This project investigated the signal processing method to produce the Color Flow Image from raw color RF data and its’ application in the analysis of blood flow. The signal processing was implemented in Matlab™. In order to extract blood velocity information from the raw color RF signal, a method called Autocorrelation was used. This method was first developed by Kasai et al in1987. It has the advantage of fast and easy implementation compared to other more complex methods, and this allows for real-time image processing. A code was developed to re-produce the Color Flow Image using the Autocorrelation method. The implementation of this code took approximately 1 minute. The main issue in Color Flow Image processing addressed in this project is the designing of an efficient clutter filter to filter out unwanted tissue signal and extract only pure blood signal. The filter design in this project was based on the method developed by Hans Torp et al in 2002. In this project, the author used an IIR high pass filter to filter out low frequency tissue signals. An initialization was applied to the filter in order to minimize the transient of output signal. Three types of filter initialization was investigated which are; Zero Initialization, Step Initialization, and Projection Initialization. A comparison was done and Projection initialized filter was found to produce the best frequency response. In the second part of this project, when a Color Flow Image was formed, the author used the blood velocity information obtained from the Autocorrelation method to analyze blood velocity profile in cardiac cycle and characteristics of turbulent blood flow. Both analyses were done on the aorta of a healthy male subject with no previous record on heart diseases and blood vessel related diseases. The analyses of blood velocity profile could be used to detect abnormal blood flow due to diseased valve or blood vessels. The quantification of turbulent blood flow was done based on two main characteristics; bi-directionality of turbulent flow and high variance of velocity values in turbulent flow. These are the characteristics of blood flow found in stenosed blood vessel and tumor blood vessel. In this project, the potential use of Color Flow Imaging as a detection tool for tumor and stenoses is investigated.