Synthetic single microbubble model for ultrasound imaging

Currently contrast enhanced ultrasound imaging (CEUS) aims to assess blood perfusion macroscopically with millimetre resolution in the clinic. However, current research presents key limitations that are associated with ultrasound imaging in general, such as the variable point spread function (PSF) across the image and the non-constant velocities of the blood among the vascular tree.

Single microbubbles (MBs) are very efficient point scatterers of ultrasound and they lend themselves as ideal point targets in the imaging field. We utilize a translated particle tracking algorithm from optical microscopy to detect and link the moving MBs through the vascular space. However the evaluation of the software is not feasible without synthetic ultrasound data.

The image and the movement of the MB scatter were simulated in order to have a controlled environment to enable the development and evaluation of image analysis tools. The exact location and identity of each particle, that simulated point scatter, was known for every frame and used as the ground truth for testing the subsequent localisation and tracking.

A detection assessment algorithm was generated to calculate the efficiency of the software on the simulated data using 6 statistical measurements. In order to gain a thorough understanding of the synthetic ultrasound model, assessment of the tracking process in now required. After which, both the detection and the linking processes can be quantitatively evaluated and improved according to ultrasound limitations.