Development of validation techniques for super-resolution ultrasound imaging with microbubble contrast agents

Student thesis: Doctoral ThesisDoctor of Philosophy


Non-invasive, longitudinal imaging of microvasculature at depth is not possible with existing medical imaging techniques. Previous work has demonstrated the potential of super-resolution ultrasound imaging (SRUS) with microbubbles (MBs) to visualise microvasculature. However, to ensure reliable representation of the vasculature for useful clinical translation, the factors which affect the super resolution image must be better understood. In this work, simulation and phantom tools to validate acoustic SRUS imaging have been developed and applied.

MBs are ultrasound (US) contrast agents that scatter strongly such that individual MBs can be detected when low concentrations of the contrast agent are imaged. The MB positions can be determined by localising their isolated point spread functions. By building up a map of localisations over time a representation of the vasculature can be generated. Both simulated and in vitro ground truth investigation are required to validate and improve upon super-resolution (SR) protocols.

A simulation environment combining the Marmottant model to simulate MB dynamics and k-Wave to incorporate tissue and model wave propagation has been developed. This has allowed the comparison and investigation of methods of extracting the MB signal from the surrounding tissue signal. A simultaneous optical and acoustic test rig was constructed to provide an experimental ground truth. To reduce artefacts produced using current vessel phantoms, a novel acquisition and signal processing method was invented to counter the limited dynamic range of the ultrasound transducer. Finally, microvascular phantoms have been developed to provide targets that better allow the capabilities of SRUS to be investigated compared to previous phantoms used in the literature.

In short, this work involved the development of simulation and experimental tools to begin to address the challenges surrounding clinical translation of SRUS.
Date of Award1 Jul 2020
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorRobert Eckersley (Supervisor), Meng-Xing Tang (Supervisor) & Chris Dunsby (Supervisor)

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