Funded Projects

Scoping Study Awards

NEW PROJECT - Low activity Imaging for Better Oncology, LIMBO: How low can you go in Positron Emission Tomography?

ECR Investigator: Dr Ben Wynne, University of Edinburgh

Positron Emission Tomography (PET) is a medical imaging technique used to diagnose many cancers. The prevalence of PET is largely attributed to the discovery of FDG-18F, a glucose analogue with the positron-emitting isotope Fluorine-18 attached, which highlights active cancer tumours. Cancer of the lungs, lymphoma, rectum, oesophagus, and thyroid can all be diagnosed this way. Despite this success there are clear limitations. For example, cancer diagnosis of the uterus, prostate, and neuroendocrine system, are either not achievable or have had mixed success using FDG-18F. There is a strong need to develop alternative radiotracers to fill this gap. Radiotracers labelled with Zirconium-89, such as DFO-89Zr, are promising candidates because they target the immune system. One of the main downsides is the smaller activity due to 89Zr’s longer half-life. However, recently developed Total Body PET scanners may provide an ideal system for its use. The reduced activity of 89Zr (about 1/43^rd of 18F) can be compensated for by the increased sensitivity (up to 40 times) of Total Body PET. In this study, relevant existing work in this field will be reviewed. Furthermore, a computer simulations framework will be set up to assess the radiotracer activity lower limit to help inform (pre)clinical studies.

NEW PROJECT - Suitability of sub-voxel position resolution algorithms in CZT for medical imaging applications.

ECR Investigator: Dr Ellis Rintoul, University of Liverpool

Cadmium Zinc Telluride (CZT) detectors have become well established in recent years for gamma-ray detection applications and, because of their room-temperature operation and excellent energy resolution, their use in gamma-cameras for cancer diagnosis is being explored. The imaging quality of such a camera requires the accurate measurement of the gamma-ray interaction positions within the detector. Improvement of the accuracy to which this can be determined results in improved image quality.
The accuracy with which interaction position can be determined may be improved through the application of three-dimensional Pulse Shape Analysis (PSA). These techniques are commonly applied to germanium detectors for gamma-tracking and imaging in the STFC Nuclear Physics programme, though recently the application to CZT detectors for use in molecular breast imaging with 141 keV gamma-rays has been performed. This project aims to further this work by assessing the potential of these techniques for medical radionuclides that emit higher energy gamma-rays, where CZT detectors could find broader application in cancer diagnosis. This project will use detector characterisation facilities at the University of Liverpool to experimentally quantify the achievable position resolution improvement. Knowledge exchange with Kromek, facilitated through placement, will allow assessment of the commercial applicability of these techniques.

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

Positron Emission Tomography (PET) is crucial to diagnosis and staging of many cancers, but availability is currently limited. In 2019 the first total-body PET scanner was built in California, offering 40x greater sensitivity for scans of the whole body although with a price tag of about £10 million. A large fraction of the cost of these scanners is the transparent scintillator crystals that detect the radiation.
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

NEW PROJECT - Biosensing and Near Infrared Imaging of New Biomarkers for the Detection of Prostate Cancer

PI: Prof Sofia Pascu, University of Bath

Co-I: Professor Stanley W. Botchway, STFC Central Laser Facility

For more than a century, the visual inspection of the low-resolution images of dye-stained tissue sections remains the gold standard for pathologists to examine the biopsy of suspected caner. As a subjective andqualitative assessment, the practice may produce some divided opinions from various pathologists, as well as inaccurate cancer diagnosis. In the era of precision medicine, cancer tissue diagnostics is urgently due for the revolution of precision imaging and quantitative assessment. To fulfil the goal, we aim at incorporating super-resolution radial fluctuations (SRRF) imaging scheme and algorithm in a novel tissue imagingtechnique-microscopy with ultraviolet surface excitation (MUSE). SRRF takes advantage of intrinsic timedependent intensity variation of fluorescent molecules, and calculates the true position of each moleculewith high precision. MUSE illuminates tissue specimens using deep ultraviolet lights that are strongly absorbed by proteins, hence a shallow imaging depth enables high-contrast imaging. The marriage of SRRFand MUSE, i.e. SR-MUSE, promises the tissue imaging of millimetre-scale specimens with sub-cellular resolution (~ 200 nm) within a few seconds, enabling quantitative identification and mapping of key morphological and functional cellular and sub-cellular features contributing to cancer diagnosis. The technique will promote accurate intraoperative and postoperative pathological diagnosis for surgeons and pathologists to reach the optimal cancer treatment decisions.

NEW PROJECT - Low-cost, rapid super-resolution microscopy with ultraviolet surface excitation for quantitative cancer histopathology

PI: Prof. Lin Wang, STFC Central Laser Facility

Co-I: Dr. Sara Wells, Mary Lyon Centre at MRC Harwell

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

Traditional x-ray systems produce black and white images which are good for differentiating dense tissues like bone from softer tissues, but not very good at distinguishing between these softer tissues (e.g. fat vs muscle). Tumours tend to be slightly denser than healthy tissue, however so are a range of other, less dangerous growths such as cysts and polyps. Currently, to classify suspicious x-ray masses as cancerous or not patients have to undergo follow up testing such as a biopsy, which can be more distressing for the patient. X-ray photon Counting Spectral Imaging (x-CSI) is a new technique, which can produce colour x-ray images that provide a great deal more information to the radiologist at a fraction of the radiation dose to the patient.
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)

Colorectal cancer (CRC) is one of the leading causes of cancer deaths worldwide, e.g. in the United States, it is the third largest cause of cancer deaths. In Europe, it is the second largest cause of cancer deaths, with 243,000 deaths reported in 2018. Colon cancer survival rate depends strongly on an early detection; decreasing from 95%, when detected early, to only 35% when detected in the later stages; hence the importance of colon screening. It is commonly accepted that most colorectal cancers evolve from polyps. Typically, a colonoscopy screening is used to detect polyps before any malignant transformation or at an early cancer stage. Optical colonoscopy is the gold standard for colon screening; however, colonoscopy has some significant limitations. Various recent studies have reported that between 17%-28% of colon polyps are missed during routine colonoscopy screening procedures, with about 39% of patients having at least one polyp missed. It has also been estimated that improvement of polyp detection rate by 1% reduces the risk of CRC by 3%.
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.

Compact beta detectors for radio-guided glioblastoma biopsy and recision

PI: Dr Bjoern Seitz, University of Glasgow

Co-I: Prof Anthony Chalmers, University of glasgow and Beatson West of Scotalnd Cancer Centre

Co-I: Dr David Lewis, Cancer Research Beaston UK Beatson Institute and University of Glasgow

Co-I: Prof Andrew Biankin, Wolfson Wohl Cancer Research Centre and University of Glasgow

This project aims to provide the proof-of concept of a new diagnostic tool for the advanced detection of cancers via radio-guided biopsy and the surgical removal of cancerous tissue. It can be deployed during surgery in the operating theatre and does not require specially trained personnel. The use of radioactive markers is required. This is common and very beneficial form any procedures and shown by one of the proponents to provide better delineation than a standard fluorescent marker. While some devices performing similar measurements exist (mostly for gamma radiation in breast cancer surgery) or are under development, the device proposed here will provide better sensitivity and accuracy.

The proposed beta radiation detector is based around next-generation ultrabright scintillator materials coupled to low-power silicon photomultipliers. These advanced technologies allow radiation detectors to be miniaturised to unprecedented dimensions while maintaining high performance standards. A miniature beta radiation detector prototype of dimensions comparable to a biopsy needle and not larger than devices employed in key-hole surgery, will be designed and prototyped during this proof-of-concept project.

The proposed technology will be tested with a challenging cancer type (glioblastoma) ex- vivo using FET radio tracers in comparison with 5-ALA fluorescent marker. It will be developed in to a tool to guide biopsy and enhance its precision and success rate in early diagnosis as well as increasing the success rate of surgical interventions. With Cancer Diagnosis Network+ funding, it is hoped that this innovation will, in the future, deliver substantial economic and resource benefits to the healthcare sector and NHS by improving patient outcomes.

sCMOS as an alternative to EMCCDs for high performance gamma imaging

PI: Dr Sarah Bugby, Loughborough University

Co-I: Dr Philip Marsden, Unitive Design & Analysis Ltd

This project investigates the use of advanced scientific CMOS sensors for gamma imaging in nuclear medicine. This is an early-stage project, although there is longer term potential for broadimpact in diagnostic imaging, we will focus on two specific scenarios where impact may arise in the shorter term.

Radioguided surgery is widely used for cancer diagnoses. Radioguided sentinel lymph node biopsy for melanoma, squamous cell carcinoma, breast, head and neck, gastric, and gynaecological cancers among others is well established and carried out over 2 million times annually. Radioguided occult lesion localization also shows promise for the detection of non-palpable breast tumours (Lovrics et al. Eur J Surg Onco, 2011) and has also been expanded to renal cell carcinoma, papillary carcinoma and sarcomas (Bitencourt et al. Clinics 2009). For relatively simple procedures, such as in breast SLNB, the performance of non-imaging gamma probes is good. However, in more complex regions such as head and neck or gynaecological cancers, surgery can be extremely challenging. In these cases, there can be limited separation between the injection site, lymphatic basin, and structures requiring preservation and the use of intraoperative imaging (with a portable gamma camera) can improve patient outcomes (Vidal-Sicart et al. Clin Transl Imaging, 2016).

Clinical gamma cameras are large devices, housed in dedicated rooms in specialist departments. Most gamma cameras are designed for whole body imaging (with some exceptions such as dedicated cardiac scanners). For small organ imaging, a portable camera could provide the necessary information in a more compact system. In the UK, a portable camera would allow greater flexibility, the ability to image at the patient’s bedside and free up time on large systems. However, in developing nations this impact could be far larger. In Uruguay, for example, three nuclear medicine departments serve the entire population and patients unable to travel long distances (in some cases >500km) do not have access to nuclear medicine or the diagnosis it can provide. A portable system opens the possibility of bringing imaging to the patient. Colleagues estimate this would impact the diagnosis of over 1000 Uruguayan women per annum in breast cancer SLNB alone.

To meet these challenges, this project will explore a new detector design for an existing portable gamma camera – the Hybrid Gamma Camera (HGC) developed at the Universities of Leicester and Nottingham (Lees at al. Sensors 2017) – to improve its performance and the quality of diagnosis it can provide.

Comparing the feasibility of emission and attenuation spectral x-ray imaging for detecting elemental composition changes associated with breast cancer diagnosis

PI: Dr Dimitra G. Darambara, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust (ICR/RMH)

Co-I: Matthew Veale, Rutherford Appleton Laboratory, UKRI STFC

Co-I: Matt Wilson, Rutherford Appleton Laboratory, UKRI STFC

The aim of this project is to see whether changes in the atomic makeup of the breast associated with cancer can be detected using new x-ray technologies.

Currently the changes that are screened for in women (e.g. physical lumps, changes in x-ray breast density) are not specific to cancer and have to be followed up with other tests. Whilst some tests are non-invasive (e.g. MRI), others involve a biopsy (a minor surgery to take a small sample) of the suspicious lump and analysing it in a laboratory to conclude if it is cancerous or benign. In all cases, there is usually a wait for the follow up tests which can be distressing for women as they wait.

This project will assess two different ways of performing a quantitative virtual biopsy, with the same x-rays used at screening, to help:

• detect cancer at an earlier stage

• provide an answer to the patient sooner (reducing stressful uncertain waits)

• avoid unnecessary biopsies (suspected cancers may still need a biopsy for other reasons)

• reduce the burden on NHS resources.

As a result of this study, we expect the feasibility of a future virtual biopsy system to be shown, and a preliminary design outlined.

PhD Awards

A methodology for breast density measurement using the HEXITEC pixellated spectroscopic technology

Student: Oakley Clark

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

Breast density describes the proportion of glandular and fatty tissues in a breast and is known to be an indicator of breast cancer risk. Women with dense breasts can be up to 6 times more likely to develop breast cancer in their lifetime. A reliable method to measure breast density would allow each woman to be allocated a personalised screening schedule, with highrisk women being screened more often than low-risk women. This would optimise breast screening, its associated cost, and ensure that women are not unnecessarily exposed to potentially harmful X-ray doses.
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

Student: Jiang Yangfan

Academic supervisor: Dr Sarah Bugby, Loughborough University

Academic supervisor: Dr Georgina Cosma, Loughborough University

Partner supervisor: Dr Paul Cload, Serac Imaging Systems Ltd. (SIS)

Medical gamma cameras are used routinely, and the resulting images are of great value to the clinician in the staging and treatment of a variety of conditions. Some aspects of cancer diagnosis, such as staging to investigate whether cancer has spread to other areas of the body, require surgical intervention.
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.

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