M.Sc Thesis

M.Sc StudentPri-Or Roie
SubjectOn Chirp-Based Ultrasound Systems
DepartmentDepartment of Electrical and Computer Engineering
Supervisors ASSOCIATE PROF. Moshe Porat
DR. Zvi Friedman
Full Thesis text - in Hebrew Full thesis text - Hebrew Version


Two main challenges have so far prevented commercial adoption of chirp transmission in medical ultrasound despite its potential to achieve significantly enhanced imaging. The first is associated with the high sidelobe level resulting from signal compression using a matched filter. The second refers to the level of the sidelobes that is further increased since the pulse shape is distorted in the biological tissue in an unknown manner. This distortion is a result of the frequency dependent absorption in the living tissue.

In this research, we model the signal as a sum of a small number of 'strong reflectors'. Using this sparsity as a prior, we propose to use a method based on the Least Angle Regression (LARS) algorithm for solving the non-linear optimization problem involved in the estimation of the positions and the (generally complex) amplitudes of the strong reflectors, as well as the precise pulse shape anywhere in the tissue. Good estimation of the attenuation factor is achieved based on the correlation between the received and the transmitted signals. In addition, we have analyzed the attenuation influence on the decoded function and proposed a method to identify errors in the estimation.

In addition, we define quality criteria for the decoding. The criteria are used to set the threshold of the LARS algorithm. Since the threshold determines the number of entries in the decoded reflectivity function, while we deal with noisy environment, the tuning of the LARS' threshold has a major impact on the noise immunity, and on the rate of false detection. Another optional use of the criteria is to compare different decoding methods

The proposed decoding method has been tested with real and complex reflectivity functions to evaluate the effects of attenuation and noise on the results. Our conclusion is that the proposed approach is superior to presently available methods in terms of detection in a noisy environment, and allows using chirps signals with their advantages in such systems.