Ultrasound imaging has been considered as the most powerful techniques for imaging the structures of organs and soft tissues in the human body. Obtaining the ultrasound image quality data often requires increasing the amount of transducer arrays. Consequently, larger amounts of data at Nyquist sample rate, twice of its maximum frequency, must be acquired and processed. The significant growth in the amounts of data enlarges both machinery size and power consumption of the hardware device. It has been recently shown by Yonina C. Eldar , that a significant sample-rate reduction may be obtained by treating ultrasound signals within the Finite Rate of Innovation (FRI) framework in each transducer elements. However, this technology has only been validated in theory. In our work, we study the theory of the Finite Rate of Innovation and make it toward a practical implementation. The model of practical implementation is approximated with a Matlab simulation. According to this Matlab simulation we analyze possible imperfections in this practical implementation. Finally, we use two groups of real ultrasound data to validate the simulated practical implementation. In the this Matlab simulation, we also achieve a theoretical sample-rate reduction.
|Date of Award||31 Jan 2015|
|Supervisor||Piet C.W. Sommen (Supervisor 1)|