About the Project
This project will provide an exciting opportunity to work in a unique collaboration between The Centre for Vision Speech and Signal Processing (CVSSP) at The University of Surrey and The National Physical Laboratory (NPL), to develop test methodologies to support the development of new image interpretation methods to aid the next generation of radiotherapy treatment.
Medical imaging is key to modern radiotherapy (RT) treatment of cancer. In RT treatment planning, x-ray CT scans are used to define the tumour volumes to be treated, with the aid of various modalities according to the specific cancer site. In high risk cancer sites such as head & neck, lung, and cervix, one important modality used to guide the treatment planning is Positron Emission Tomography (PET) imaging as it provides functional information on tumour metabolism. Traditionally the identified tumour is prescribed a radiotherapy dose based on a whole-population. There is emerging evidence that intra-tumour heterogeneity may be related to the disease outcome and that the CT and PET images of tumours may contain this information (known as image “texture”). By analysing this texture it may be possible to have a priori knowledge of the response of the tumour, thereby allowing for personalised radiotherapy treatment planning tailored to each patient.
However, the robustness of image texture analysis to variations in methodology and the associated uncertainties are not well understood. In particular, image spatial resolution varies with scanner settings, and between manufacturers and clinics, which heavily influences the texture analysis. The relatively low spatial resolution of PET images causes Partial Volume Effects (PVE), which confounds our ability to study texture in this modality. There are a variety of algorithms that can compensate for PVE in PET, but the impact of these algorithms on PET texture and its correlation with CT texture has not been extensively studied. There is therefore an unmet need for appropriate reference standards and standardised methodologies for multi-modality image texture analysis. This project aims to address this, with two main objectives. The first is to develop an experimental framework for standardised texture measurements and the second is to study the impact of PVE correction algorithms in texture analysis of PET data.
CVSSP is a leading UK research centre in audio-visual signal processing, computer vision and machine learning. Our Centre is one of the largest in Europe with over 120 researchers and a grant portfolio in excess of £18.5million, bringing together a unique combination of cutting-edge sound and vision expertise. Our aim is to advance the state of the art in multimedia signal processing and computer vision, with a focus on image, video and audio applications. Our Centre has a robust track-record of innovative research leading to technology transfer and exploitation in biometrics, creative industries (film, TV, games, VR), mobile communication, healthcare, robotics and consumer electronics.
NPL is an internationally recognized standards laboratory. The successful applicant will be expected to spend time at each partner organization.
Please send a cover letter explaining your interest in and qualifications for the project, a CV, and the names and contact details of two referees. Informal enquiries should be sent via e-mail to Prof Phil Evans ([Email Address Removed]) or Dr Mohammad Hussein ([Email Address Removed]). Formal applications should be made via our programme page https://www.surrey.ac.uk/postgraduate/vision-speech-and-signal-processing-phd
Non-native speakers of English will normally be required to have IELTS 6.5 or above (or equivalent). Shortlisted applicants will be contacted directly to arrange a suitable time for an interview.
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