The Taub Faculty of Computer Science Events and Talks

Ultrasound images are often contaminated with acoustic clutter, which obscures image details of interest, thus leading to potentially inaccurate medical diagnosis. In order to address this problem, we are proposing a model-based image reconstruction approach using the individually stored channel data of the ultrasound transducer elements, in a grid of image points. Analysis of the data allows the description of the image as consisting of coherent strong reflectors, speckled tissue and clutter noise, which can be mostly rejected according to its inter-element and inter-transmit second-order statistics. It is accordingly shown that this de-noised reconstruction becomes a spectral estimation task. We have further shown that element signals from strong (point or distributed) reflectors follow a first order auto-regressive model, based on which they can be accurately located in the image beyond the basic resolution of the system. The reconstruction results of the proposed approach provide improved image quality in the sense of resolution, speckled tissue texture and signal-to-noise ratio compared to state-of-the-art methods.

*M.Sc. research under the supervision of Prof. Moshe Porat (EE, Technion) and Dr. Zvi Friedman (GE Healthcare).