A fast convolution-based methodology to simulate 2-D/3-D cardiac ultrasound images.

This paper describes a fast convolution-based methodology for simulating ultrasound images in a 2-D/3-D sector format as typically used in cardiac ultrasound. The conventional convolution model is based on the assumption of a space-invariant point spread function (PSF) and typically results in linear images. These characteristics are not representative for cardiac data sets. The spatial impulse response method (IRM) has excellent accuracy in the linear domain; however, calculation time can become an issue when scatterer numbers become significant and when 3-D volumetric data sets need to be computed. As a solution to these problems, the current manuscript proposes a new convolution-based methodology in which the data sets are produced by reducing the conventional 2-D/3-D convolution model to multiple 1-D convolutions (one for each image line). As an example, simulated 2-D/3-D phantom images are presented along with their gray scale histogram statistics. In addition, the computation time is recorded and contrasted to a commonly used implementation of IRM (Field II). It is shown that COLE can produce anatomically plausible images with local Rayleigh statistics but at improved calculation time (1200 times faster than the reference method).