This programme aims to change the way medical imaging is currently used in applications where quantitative assessment of disease progression or guidance of treatment is required. Imaging technology traditionally sees the reconstructed image as the end goal, but in reality it is a stepping stone to evaluate some aspect of the state of the patient, which we term the target, e.g. the presence, location, extent and characteristics of a particular disease, function of the heart, response to treatment etc. The image is merely an intermediate visualization, for subsequent interpretation and processing either by the human expert or computer based analysis. Our objectives are to extract information which can be used to inform diagnosis and guide therapy directly from the measurements of the imaging device. We propose a new paradigm whereby the extraction of clinically-relevant information drives the entire imaging process. All medical imaging devices measure some physical attribute of the patient's body, such as the X-ray attenuation in CT, changes acoustic impedance in ultrasound, or the mobility of protons in MRI. These physical attributes may be modulated by changes in structure or metabolic function. Medical images from devices such as MR and CT scanners often take 10s of seconds to many minutes to acquire. The unborn child, the very young, the very old or very ill cannot stay still for this time and methods of addressing motion are inefficient and cannot be applied to all types of imaging. Usually triggering and gating strategies are applied, which result in a low acquisition efficiency (since most of the data is rejected) and often fail due to irregular motion. As a result the images are corrupted by significant motion artifact or blurring.Accurate computational modeling of physiology and pathological processes at different spatial scales has shown how careful measurements from imaging devices might allow the clinician or the medical scientist to infer what is happening in health, in specific diseases and during therapy. Unfortunately, making these accurate measurements is very difficult due to the movement artifacts described above. Imaging systems can provide the therapist, interventionist or surgeon with a 3D navigational map showing where therapy should be delivered and measuring how effective it is. Unfortunately image guided interventions in the moving and deforming tissues of the chest and abdomen is very difficult as the images are often corrupted by motion and as the procedure progresses the images generally diverge from the local anatomy that the interventionist or surgeon is treating.Our programme brings together three different groups of people: computer scientists who construct computer models of anatomy, physiology, pharmacological processes and the dynamics of tissue motion; imaging scientists who develop new ways to reconstruct images of the human body; and clinicians working to provide better treatment for their patients. With these three groups working together we will devise new ways to correct for motion artifact, to produce images of optimal quality that are related directly to clinically relevant measures of tissue composition, microscopic structure and metabolism. We will apply these methods to improve understanding of disease progression; guide therapies and assess response to treatment in cancer arising in the lung and liver; to ischaemic heart disease; to the clinical management of the foetus while still in the womb; and to caring for premature babies and young children.

In redeveloping the EngD VEIV centre, we will be focussing on three themes in the area: - Vision & Imaging, covering the areas of computer-based interpretation of images. For example, object tracking in real-time video, or face detection and surface appearance capture. UCL now has a broad expertise in medical imaging (see description of CMIC), and also in tracking and interpretation of images (e.g. expertise of Julier and Prince who are on the management team). Previously we have supported several EngD projects in this area: e.g. Philips (structure from MRI), Sortex (object detection), Bodymetrics (body measurement from scanning data), where the innovation has been in higher-levels of interpretation of imaging data and derivation of measurements automatically. Two other projects highlight the rapidly developing imaging technology, with high-density sensors and high dynamic range imagery (e.g. BBC and Framestore). We have outline support from several companies for continuing in this area. - Media & Interfaces, covering real-time graphics and interactive interfaces. For example, the use of spatially immersive interfaces, or computer games technology. We have a growing relationship with a number of key games companies (EA, Sony, Eidos, Rebellion), where their concern or interest lies in the management of large sets of assets for complex games software. There is interest in tools for developing imagery (r.g. Arthropics, Geomerics). We also have interest in the online 3D social spaces from IBM and BT. A relatively recent development that we plan to exploit is the combination of real-time tracking, real-time graphics and ubiquitous sensing to create augmented reality systems. Interest has been expressed in this area from Selex and BAe. There is also a growing use of these technologies in the digital heritage area, which we have expertise in and want to expand. - Visualisation & Design, covering the generation and visualisation of computer models in support of decision-making processes. For example, the use of visualisation of geographic models, or generative modelling for architectural design. Great advances have been made in this area recently, with the popularity of online GIS tools such as Google Earth tied in to web services and the acceptance of the role of IT in complex design processes. We would highlight the areas of parameterised geometry (e.g. with Fosters and the ComplexMatters spin-out), studying pedestrian movements (with Buro Happold, Node Architects), visualisation of GIS data (e.g. ThinkLondon, Arup Geotechnical), and medical visualisation.These themes will be supported by broadening the engagement with other centres around UCL, including: the UCL Interaction Centre, the Centre for Medical Image Computing, the Chorley Institute and the Centre for Computational Science.The main value of the centre is that visual engineering requires cross-disciplinary training. This is possible with a normal PhD, but within the centre model inter-disciplinary training can embed the students' focussed research into a larger context. The centre model provides a programme structure and forums to ensure that opportunities and mechanisms for cross-disciplinary working are available. The centre also provides an essential role in providing some core training; though by its nature the programme must incorporate modules of teaching from a wide variety of departments that would otherwise be difficult to justify.