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This case study describes how research in bio-imaging using Electrical Impedance Tomography (EIT) at Middlesex University stimulates collaborative development in EIT applications particularly in imaging brain function, lung function and tumour detection, and the development of Optical Tomography of brain function in neonates. The researchers have contributed a range of public domain, open source resources to the international industrial and research communities such as novel reconstruction algorithms, geometric models for generating accurate finite element and boundary element forward models and methods to generate subject-specific forward models in the belief that collaborative use of such resources is the most effective route to impact.
UCL's research has led to changes in patient care for men with prostate cancer, through the implementation of less invasive, image-directed treatment and diagnostic strategies, and clinical trials that use these techniques. The use of medical image registration software to deliver high- intensity ultrasound therapy in a targeted manner has been shown to change the treatment plan in half of the patients participating in a clinical study. New biopsy criteria are now used routinely to classify patient risk at University College Hospital, where, since 2009, clinicians have determined the treatment options for more than 741 prostate cancer patients. The scheme has been adopted, by 15 other hospitals in the UK and internationally, where it has become the recommended standard of care, and has been used to treat more than 1,200 patients.
A biomarker is a measurement or physical sign used as a substitute for a clinically meaningful endpoint that measures directly how a patient feels, functions, or survives. Biomarkers can be used to assess changes induced by a therapy or intervention on a clinically meaningful endpoint.
New quantitative image analysis techniques developed at Imperial College have enabled the computation of imaging biomarkers that are now widely used in clinical trials as well as for healthcare diagnostics. This case study illustrates the resulting key impacts including:
Key advances in the earlier diagnosis of cancer, leading to better treatment and higher survival rates, have resulted from the commercialisation of unique imaging software that exploits research from the Department of Engineering Science. The software products that came from this research, Volpara™, XD and XRT are now used at major cancer centres worldwide (with approximately 1100 software installations), aiding treatment of tens of thousands of patients every year. Between 2009 and July 2013, Volpara™ scanned over 1.2 million mammograms, enabling the early detection of around 1800 cancers. The products' success has catalysed significant improvements in cancer care, and generated an estimated £9M in sales over the past two years for the spinout companies established to develop them (Matakina, based in New Zealand, and Mirada Medical, based in the UK).
Phase unwrapping is an essential algorithmic step in any measurement system or sensor that seeks to determine continuous phase. Instances of such devices are widespread: e.g. image reconstruction in magnetic resonance imaging (MRI), synthetic aperture radar (SAR) by satellite systems, analysis of seismic data in geophysics and optical instrumentation, to name but a few. Without successfully solving the phase unwrapping problem these instruments cannot function.
The topic is well developed and competition among algorithms is fierce. In 2012 alone, some 235 papers, most of which were describing potential new algorithms, were published in the area. But the continuing need for high-speed, automated and robust unwrapping algorithms poses a major limitation on the employability of phase measuring systems.
Working originally within the context of structured light 3D measurement systems, our research has developed new phase image unwrapping algorithms that constitute significance advances in speed, automation and robustness. The work has led to adoption by industry, as well as use in commercial and government research centres around the globe. Our approach since 2010 has been to make these algorithms freely available to end users. Third parties have gone on to translate our algorithms into other languages, widely used numerical software libraries have incorporated the algorithms and there are high profile industrial users.
A unique aspect of the signal and image processing research at the University of Central Lancashire (UCLan) lies in exploitation of the synergies between non-destructive evaluation (NDE) of aerostructures in the aerospace manufacturing sector and non-invasive diagnosis (NID) of patients in the medical sector. For the former, through collaborative research with world leading aerospace companies, data processing technologies used in medical NID have been exploited to ensure structural safety of aircraft at reduced time and cost. For the latter, through collaborative research with the UCLan led Europe-wide network which includes top medical research centres and hospitals, sensing technologies used in aerospace NDE have been exploited to create new measurement modalities for quantitative medical diagnosis of major diseases. Furthermore, arising out the cross-sectoral and interdisciplinary research the Tele-immersive Digital Manufacturing facility (TiM) emerges as our vision for the factory of the future which has attracted investments from the world leading digital technology providers and made impacts on one of the most important manufacturing regions in the world.
Professor Alexander's work on diffusion magnetic resonance imaging (MRI) modelling and processing has had significant and lasting impact on medical practice. In particular, neurosurgical support systems rely on his work to map the major connection pathways in the brain, helping the surgeons avoid damaging them during intervention. Specific examples are in epilepsy, where, since 2010, surgeons perform about one operation per week using these systems, and brain tumour resection, where surgeons in Milan have since early 2013 been using a similar system based on UCL's latest microstructure imaging techniques. The key impact is on patients, whose likelihood of permanent post-operative deficits in, for example, visual, verbal or motor skills, is significantly reduced.
This Keele University research in multiscale object recognition has led to two key breakthroughs: (a) the automated identification of tissue boundaries in computer tomographic (CT) scans, enabling the latest radiotherapy equipment to more accurately target diseased tissue thus avoiding neighbouring healthy organs. Such improvements are essential to the successful roll-out of new more precise linear accelerators in the treatment of cancer; (b) new fractal algorithms to characterise the quality of transplanted cell growth from post-operative biopsies. By automating the selection of the healthiest cells this has assisted the generation of patient-specific cartilage and is essential for the development of a medical capability for large-scale patient-specific generation of cartilage growth for the treatment of arthritis. It has indirectly led to software improvements in cell-tracking and to achieving reliable auto-focussing in high throughput non-invasive microscopy.