"Super-resolution tracking of ultrasound microbubbles for mapping prostate cancer"
Supervisors: Dr Vassilis Sboros and Dr Weiping Lu (both Institute of Biological Chemistry, Biophysics and Bioengineering, Heriot Watt University), Dr Yoan Altmann (Institute of Sensors Signals and Systems, Heriot Watt University), Prof Alan McNeill (Department of Urology, Western General Hospital and University of Edinburgh).
Prostate cancer incidence and deaths from the disease have both significantly increased in recent years. Current diagnostic approaches are unpleasant, costly and invasive. Additionally, many men undergo radical treatments that can have long-term detriment on their quality of life because current methods to characterise these tumours are not always able to predict the patient’s likely disease progression with sufficient confidence.
We have proposed a new image analysis method based on diagnostic ultrasound in order to find prostate disease early. Since cancer has different blood flow compared to normal prostate the proposed technology aims to provide a 10-fold improvement in the detail in mapping the blood flow (i.e. from half a millimetre to a 20th of a mm). This unique method will help determine the state of a tumour. We will use images from volunteer prostate patients in order to develop our image analysis method and subsequently assess its potential for prostate cancer diagnosis.
Specifically, the project will investigate a method whereby images of microvessels are formed by the tracks of individual microbubbles that are available in the blood stream. Particle detection, segmentation and tracking will be developed to suit ultrasound imaging requirements. This will be based on robust integrated algorithms generated for optical microscopy (see references). Subsequently, information on the vascular architecture in microscopic detail will be generated. A comparison between normal (prostate cancer) and abnormal tissue (normal prostate) is the overall aim of the project. Note that no imaging modality can reach such resolution, and if successful the outcomes from this project will revolutionised medical imaging.
The candidate will work with physicists, engineers, life scientists and as part of an international collaborative network that also includes significant industrial partners (Philips Medical Imaging Ltd, BK Medical, and Lantheus Medical Imaging Inc) Desirable student background is sought in signal or image processing, Matlab programming, applied mathematics and statistics.
How to apply For informal enquiries and applications including a CV, please contact Vassilis Sboros ([Email Address Removed]). Tel: 0131 451 8015 Closing Date: 28th February 2019. The studentship should start in September or October 2019.
This 3yr PhD studentship is funded by the EPSRC DTP scheme. The annual stipend will be as set by the EPSRC for 3½ years. The studentship is only available to UK nationals or students from EU countries who have settled status and have been resident in the UK for 3 years prior to the start of the studentship. Consumables and contribution to travel expenses are included. Fees for non-EU applicants are not covered. Candidates must have obtained, or expect to obtain, a UK MSc degree or undergraduate honours degree (first class) in a related field, or an equivalent quality degree obtained outside the UK.
Yang et al. (2010) An adaptive non-local means filter for denoising live-cell images and improving particle detection. J Struct Biol 172: 233. Wilson et al. (2016) Automated single particle detection and tracking for large microscopy datasets. Royal Soc Open Sci 3: 160225.