Biomedical and life sciences.
Bench Fees / Research Training & Support Grant.
The Engineering and Physical Sciences Research Council (EPSRC).
Fully funded 3,5 years full-time PhD studentship (including home tuition fees, annual stipend and consumables) starting on 1st October 2022.
Cancer is among the leading causes of death worldwide. Detection, diagnosis and clinical intervention can significantly increase chances of patient survival. However, current non-invasive imaging methods are unable to visualise the microvasculature associated with cancerous tumours deep in the body.
Recently developed super-resolution ultrasound (SR-US) imaging1-4 is gaining world-wide recognition due to its ability to achieve a resolution far beyond the conventional limit for ultrasound (US), allowing visualisation of micro-vascular structure and flow. Alongside highly detailed micro-vascular images, SR-US also has potential to provide access to multiple new quantitative functional and morphological parameters that could be crucial for tumour characterisation.
Nevertheless, when imaging deep in the human body, significant challenges can exist for SR-US, including significant tissue motion, and the presence of overlaying tissues can cause significant sound aberration. Furthermore, SR-US in 2D has been hampered by the lack of information in the third dimension, resulting in the overlap of information (~mm) in clinical systems.
Recent advances in biomedical ultrasound including ultrafast data acquisition and 2D array probe technology allows the acquisition of up to tens of thousands of imaging frames per second. These have made it possible to non-invasively image the microvascular morphology and flow dynamics with a resolution of tens of microns at a higher frame rate, and acquire volumetric data in deep tissue.
In this project, we propose to develop advanced SR-US for tumour characterisation, which will involve the following.
- Initially, the student will implement existing SR-US localisation and tracking algorithms on existing clinical datasets with valuable histological ground truth data. These have minimal motion and shallow depths providing an important opportunity to examine how 2D SR-US can help discriminate benign from malignant tumours in a clinical setting.
- The student can propose new, and refine existing, functional and morphological quantitative measures aimed at extracting microvascular details not possible with existing diffraction-limited techniques over large clinical datasets. By developing an understanding of the challenges of SR-US in a clinical environment, the student will have acquired the knowledge and comprehension to aid in its development.
- the student will implement and evaluate the most up-to-date SR-US post-processing developments, e.g. robust motion correction and advanced tracking algorithms.
- A framework will be developed to validate methodological advancements in a controlled set-up using ultrasound simulations with realistic motion and aberration and experiments.
- The student will develop and evaluate signal processing algorithms for robust motion and aberration correction and explore novel signal and image processing methods for more efficient and automated SR-US implementation on a research system. Experimental and in vivo data acquisition of microbubble based imaging data will be acquired. The student will evaluate and optimise imaging and correction algorithms using the validation framework.
- The student will propose and define strategies for extending the imaging strategies with a research system and programmable 2D matrix-array probe (ULA-OP, Vermon) to allow 3D SR-US imaging.
- Evaluating and optimising both 2D and 3D methodologies in vivo.
- Use the proposed advancements to measure tumour characteristics in clinical setting with mainly rigid motion and shallow depths (focal testicular tumours).
- Use the proposed technology to measure tumour characteristics in clinical setting in more challenging application (kidney, liver).
1 Viessmann, O. M. et al. Phys. Med. Biol. 58, 6447–58 (2013)
2 Christensen-Jeffries, K. et al. IEEE Trans. Med. Imaging 34, 433–440 (2015)
3 Christensen-Jeffries, K. et al. Ultrasound Med. Biol. 46, 865–891 (2020)
4 Errico, C. et al. Nature 527, 499–502 (2015)
Informal email enquiries from interested students to the supervisor are encouraged (contact details below).
Kirsten Christensen-Jeffries - email@example.com
Each studentship is fully funded for 3 years 4 months. This includes home tuition fees, stipend and generous project consumables.
Stipend: Students will receive a tax-free stipend at the UKRI rate of £18,062 per year as a living allowance.
Research Training Support Grant (RTSG): A generous project allowance will be provided for research consumables and for attending UK and international conferences.
Candidates who meet the eligibility requirements for Home Fee status will be eligible to apply for this project. Home students will be eligible for a full UKRI award, including fees and stipend, if they satisfy the UKRI criteria below, including residency requirements. To be classed as a Home student, candidates must meet the following criteria:
- be a UK National (meeting residency requirements), or
- have settled status, or
- have pre-settled status (meeting residency requirements), or
- have indefinite leave to remain or enter.
Prospective candidates should have a 1st or 2:1 M-level qualification in Biomedical Engineering, Physics, Engineering, Computer Science, Mathematics, or a related programme.
Preference will be given to candidates with a background conducive to multidisciplinary research and preferably programming skills.
We welcome eligible applicants from any personal background, who are pleased to join diverse and friendly research groups.
Closing date is 30th June 2022 (please note that applications can be closed early if a suitable candidate is found).
Please submit an application for the Biomedical Engineering and Imaging Science Research MPhil/PhD (Full-time) programme using the King’s Apply system. Please include the following with your application:
- A PDF copy of your CV should be uploaded to the Employment History section.
- A 500-word personal statement outlining your motivation for undertaking postgraduate research should be uploaded to the Supporting statement section.
Funding information: Please choose Option 5 “I am applying for a funding award or scholarship administered by King’s College London” and under “Award Scheme Code or Name” enter BMEIS_DTP_KCJ. Failing to include this code might result in you not being considered for this funding.