1st Supervisor: Mengxing Tang, Imperial College London
2nd Supervisor: Kirsten Christensen Jeffries, King’s College London
3rd Supervisor: Ying Luan, GE Healthcare
Project Description / Background:
This is an industrial PhD project aimed at developing novel imaging techniques for contrast-enhanced liver imaging using the ultrasound contrast agent (UCA) SonazoidTM. UCAs are micrometer size bubbles (with the size range of 3-10 µm diameters) that consist of an inert gas core encapsulated by a stabilizing shell (protein, lipid or polymer). They are blood pool agents which scatter ultrasound signals very efficiently, thus can be used for imaging both macro- and micro- blood circulation . SonazoidTM microspheres (GE Healthcare) have been shown to have very stable physicochemical characteristics and have been approved in several countries/regions for clinical liver imaging and diagnosis .
SonazoidTM has a unique imaging feature comparing to other commercially available UCA – it has an extended late phase (termed “Kupffer phase”) in which it persists for several hours in the liver and spleen due to the uptake of the agent by Kupffer cells. The very stable Kupffer phase imaging is suitable for repeated scanning from 10 to 60 min after contrast injection . Different patterns of vascular phase (0-2 min after injection), late phase (2-10 min) and Kupffer phase (>10 min) contrast enhancement can be used to distinguish between different types of focal liver lesions effectively. Figure 1 shows an example of characterisation of liver metastasis using contrast-enhanced ultrasound imaging with SonazoidTM .
Aim of the PhD Project:
The objective of this project is to design and implement adaptive imaging algorithms to optimise the imaging strategy using SonazoidTM, and to exploit the features of Sonazoid at different imaging phases. We envision a two-stage imaging algorithm: Firstly, during the vascular phase, high-speed imaging techniques could be applied, and images could be reconstructed and processed on a pixel-by-pixel level to visualize the blood perfusion in large and small vessels (e.g., angiogenesis) in liver tumours [5-6]. Secondly, during the late phase and Kupffer phase, image processing techniques can be developed and applied to optimise image quality. These algorithms can be developed using a tissue-mimicking phantom on a programmable imaging platform; the methodology can also be tested and potentially implemented on a GE LOGIQ E9 ultrasound system.
In addition, the individual is also highly encouraged to devise and explore other ideas, with the ultimate goal of improving the capability and efficacy of liver lesion imaging and diagnosis using SonazoidTM.
This is an interdisciplinary project oriented at clinical application with a clear industrial interest. You will be working closely with industrial partners at GE Healthcare Imaging R&D. This will be a great opportunity for the individual to develop knowledge and skills in medical physics, image processing/reconstruction, laboratory-based phantom development and measurements and medical devices.
Figure 1. An example of Sonazoid-enhanced ultrasound images featuring Metastasis in liver during different image phases (A) Peripheral hypervascular pattern was clearly demonstrated in the vascular phase. (B) A clear defect is evident in the Kupffer phase .
 Cosgrove and Lassau, Imaging of perfusion using ultrasound, Eur J Nucl Med Mol Imaging 27, 2010
 Sontum, Physicochemical characteristics of SonazoidTM, a new contrast agent for ultrasound imaging. Ultrasound in Med. & Biol., 2008
 Kudo et al., Contrast enhanced ultrasonography for diagnosis of hepatic malignancies: comparison with contrast-enhanced CT. Oncology 75, 2008
 Kudo et al., Sonazoid-enhanced ultrasound in the diagnosis and treatment of hepatic tumours, J Med Ultrasound, 2008
 Stanziola et al., Temporal and spatial processing of high frame-rate contrast enhanced ultrasound data, Conference paper 21st European symposium on ultrasound contrast imaging, Rotterdam, 2016
 Errico et al., Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging, Vol 527, Nature, 2015”‹