A methodology for breast density measurement using the HEXITEC pixellated spectroscopic technology
Academic supervisor: Dr Silvia Pani, University of Surrey
Academic supervisor: Prof Philip Evans, University of Surrey
Partner supervisor: Dr Emma Harris, Institute of Cancer Research
Partner supervisor: Mr Matthew Wilson, STFC UKRI Rutherford Appleton Laboratory
The project will develop a safer, more precise breast density measurement technique than mammography. We will use novel X-ray detector technology allowing simultaneous acquisitions of multiple images at different X-ray energies, giving better discrimination of tissues at a minimal dose. We will determine how this detector can be best used to obtain precise measures of breast density, and develop computer algorithms to analyse the information provided by multiple X-ray energies to provide a map of the thicknesses of glandular and fatty tissue across the breast. The algorithms will be developed using computer simulations, and tested in the laboratory on custom-developed test objects.
Portable hybrid gamma-optical camera for quantitative 3D precision imaging in cancer diagnosis
Academic supervisor: Dr Sarah Bugby, Loughborough University
Academic supervisor: Dr Georgina Cosma, Loughborough University
Partner supervisor: Dr Paul Cload, Serac Imaging Systems Ltd. (SIS)
Prior to surgery, SPECT or PET cameras are used for surgical planning. However, these images can’t take into account the changes in patient position or surrounding tissue during surgery, and the imaging systems are far too large to be used during surgery. Instead, radioguidance is provided by non-imaging gamma probes via an audible signal. However, these probes can’t provide precise localisation of sources, cover the entire surgical field of view, or provide information on the depth of a source within tissue.
This project will build on STFC-developed technology – a portable high resolution hybrid gamma-optical camera - now being developed by SIS. We will extend this concept using stereoscopic imaging techniques and new image analysis processes, so that the system can provide depth information in real time.
This technique for intraoperative gamma imaging would be applicable to a range of cancer diagnosis and staging or other procedures, decreasing time in surgery and improving patient outcomes.
Scoping Study Awards
Development of radiation detectors for medical imaging using opaque scintillators
PI: Prof Jeffrey Hartnell, University of Sussex
Co-I: Patrick Begley, Royal Sussex County Hospital
We have recently developed a new concept for a scintillator detector. Many radiation detectors use scintillators, which are materials that give off light when a neutrino hits them. Traditional scintillator detectors have required transparent scintillators to allow detection of the light, while our new concept requires an opaque scintillator. The opacity causes the light to bounce around close to where it is produced and then a dense lattice of fibre optic cables is used to extract the light. Such a configuration enables fast and high-resolution imaging capabilities. This novel detector technology is also particularly well suited to covering large areas such as those needed in a total-body PET scanner.
In this project we will quantify the spatial and timing resolution of a simulated prototype opaque scintillator detector element of a PET scanner. Simulation and reconstruction software will be developed to achieve this, based on algorithms developed for neutrino detectors such as MINOS and NOvA. In particular, we will investigate how the performance depends on variables such as fibre pitch, scattering length and scintillator/fibre decay times. This will inform future designs of prototypes that we aim to construct.
Proof of Concept Awards
Modelling of pulse pile-up and deadtime effects on quantitative imaging potential of x-CSI systems at medically relevant fluxes
PI: Dr Dimitra G. Darambara, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust (ICR/RMH)
Co-I: Prof Val O’Shea, University of Glasgow
Early work has already shown that x-CSI systems can readily distinguish between muscle and fat, as well as water and bone, tempting many to speculate that cysts, polyps and tumours could be differentiated without further analysis. This would greatly reduce the number of follow up procedures patients would need, allowing them to receive reassurance or treatment quicker than currently possible. A range of questions remain about how best to use the data x-CSI produces however, and in order to answer these questions soon accurate computer models that can simulate patient scans are required. Unfortunately, current computer models are unable to account for the electronics complications that are introduced by the high speeds used in medical imaging applications. The aim of the current work is to develop a complete computer model of the electronics inside an x-CSI system which can be incorporated into existing computer models of the rest of the system. It is hoped that doing so will allow existing questions about x-CSI system design and data use to be rapidly answered, giving patients access to this cutting-edge technique in their clinic.
Machine Learning System for Decision Support and Computational Automation of Early Cancer Detection and Categorisation in Colonoscopy
PI: Prof Bogdan Matuszewski, University Of Central Lancashire
Co-I: Prof Victor Debattista, University of Central Lancashire
Co-I: Mr Adnan A. Sheikh, East Lancashire Hospitals NHS Trust (ELHT)
With application of new advanced machine learning methodologies (the so-called deep learning) and advanced visualisation, it is conceivable to increase significantly robustness and effectiveness of colorectal cancer screening, improving lesion detectability and the accuracy of their histological characterisation, as well as reducing cost, risk and discomfort to patients. The proposed research will investigate possible ways to leverage this novel technology to develop better early CRC detection within colonoscopy procedures.The proposed methodology is very computationally intensive hence the need for collaboration between engineers, clinicians and experts in high-performance computing who are typically focused on high-end computationally demanding physical simulations. This project brings all these expertise together to assist clinicians in improving the outcome of the colonoscopy screening procedure.