This action aims to optimally prepare three young researchers for the evolving medical imaging world by offering a unique set of targeted interdisciplinary training and research assignments in the areas of anatomy, pathology, imaging techniques, quantitative image analysis and segmentation, Magnetic Resonance (MR) physics and MR image simulation. MR imaging is the major imaging modality for brain and spine anatomy and pathology. A clear trend can be observed from visual to computer-assisted diagnosis by quantification of disease-specific biomarkers, derived from the MR images. The major components in image quantification applications are tissue and organ segmentation and classification. Manual segmentation is too tedious and cumbersome for daily clinical practice and would lead to large inter-user variability. Much research is therefore performed on automatic segmentation techniques. Training, validation and benchmarking of these techniques is currently impeded by the lack of MR image databases with exact reference segmentations. The research will follow an innovative approach to overcome the current barriers for wide uptake of automatic segmentation. By combining mathematical organ models with physical and biological tissue properties and image simulation methods, substantial public image databases will be established providing ample MR images with ground truth (exact) segmentations, by which fast and accurate optimization and validation of image segmentation algorithms will be enabled. Based on sound career development plans, and coached by experienced supervisors a training is offered by leading image analysis research groups from Philips (global leader in medical imaging) and the Eindhoven University of Technology (world-wide recognized authority in education and research on image analysis, esp. on MRI) and supported by researchers from leading clinical centers as UMC Utrecht, TU Munich, Kings College London and the German Center for Neurodegenerative Diseases.

Heart Failure (HF) is defined by the heart's reduced ability to pump blood due to a drop in cellular contractility, enlarged anatomy and increased coronary micro-vascular resistance. This loss of pump function accounts for a significant increase in both mortality and morbidity in western society. With the U.K.'s elderly population expanding, HF is rapidly becoming an epidemic. There is currently a 1 in 5 life-time risk of HF and costs associated with acute and long term hospital treatments are accelerating. The significance of the disease has motivated the application of state of the art clinical imaging techniques to aid diagnosis and clinical planning. Measurements of cardiac wall motion, chamber flow patterns and coronary perfusion currently provide high resolution data sets for characterising HF patients. However, the clinical practice of using population-based metrics derived from separate image sets often indicates contradictory treatments plans due to inter-individual variability in pathophysiology. Thus, despite imaging advances, determining optimal treatment strategies for HF patients remains problematic. To exploit the full value of imaging technologies, and the combined information content they produce, requires the ability to integrate multiple types of functional data into a consistent framework. This in turn will support a paradigm shift away from predefined clinical indices determining treatment options and a move towards true personalisation of care based on an individual's physiology.An exciting and highly promising strategy for underpinning this shift is the assimilation of multiple image sets into personalised and biophysically consistent mathematical models. The development of such models provides the ability to capture the multi-factorial cause and effect relationships which link the underlying pathophysiological mechanisms. Furthermore, using a biophysical basis presents unique opportunities to assist with treatment decisions through the derivation of quantities that cannot be imaged but are likely to play a key mechanistic role in HF e.g. tissue stress and pump efficiency.In parallel with imaging advances the approach is also underpinned by the ongoing development of complementary technologies, including improved numerical methods and increased performance per unit cost of computing. This computational progress has accelerated the addition of multi-physics functionality to a range of organ models which have recently been organized into international initiatives such as the IUPS sponsored Physiome and VPH projects. Within these programmes the heart is arguably the most advanced current exemplar of an integrated organ model. As such it represents a promising first candidate with which to focus on an important human disease.My goal during this fellowship will be to focus on personalising and applying these models in clinical and industrial settings for treating HF patients. Model simulations will be focused on quantifying diagnosis, aiding patient selection and guiding interventional planning for specific treatments carried out by leading clinicians based in the cardio-vascular imaging group at Kings College London (KCL). In addition to this direct clinical application of the model, the research will also be focused on the tuning of Left Ventricular Assist Devices (LVADs) which are often connected to the heart in HF to reduce mechanical load by pumping blood from the left ventricle directly into the aorta. Through these applications my aim is to both improve our understanding of this significant cardiovascular disease and demonstrate the potential of biophysical models for improving human healthcare.