Robert Merrifield

Technologies for global health

Institute for Global Health Innovation (L Conteh PhD, Prof A Darzi FRCS, P Howitt MA, K Kerr PhD, Prof S Matlin DSc, R Merrifi eld PhD, Prof G-Z Yang PhD), Centre for Environmental Policy (E Wilson MSc), Centre for Health Policy (D King MRCS, M Kulendran MRCS, Prof P C Smith BA), Department of Bioengineering (Prof A M J Bull PhD, Prof R A Malkin PhD, Prof M M Stevens PhD), Department of Civil and Environmental Engineering (M R Templeton PhD), Department of Infectious Diseases (G S Cooke PhD, N Ford PhD, S D Reid PhD), Department of Infectious Disease Epidemiology (S A J Gregson PhD), Department of Materials (Prof M M Stevens), Department of Medicine (A Blakemore PhD), Department of Primary Care & Public Health (Prof A Majeed MD), Department of Surgery and Cancer (H Ashrafi an MRCS, Prof C Vincent PhD), Faculty of Medicine (Prof R Atun FRCP), Global eHealth Unit (J Car PhD), Imperial College Business School (Prof R Atun FRCP, Prof J Barlow PhD), and Imperial Innovations (HA Penfold PhD), Imperial College London, London, UK Technologies for global health

Big Data for Health

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

Progress towards patient-specific computational flow modeling of the left heart via combination of magnetic resonance imaging with computational fluid dynamics.

A combined computational fluid dynamics (CFD) and magnetic resonance imaging (MRI) methodology has been developed to simulate blood flow in a subject-specific left heart. The research continues from earlier experience in modeling the human left ventricle using time-varying anatomical MR scans. Breathing artifacts are reduced by means of a MR navigator echo sequence with feedback to the subject, allowing a near constant breath-hold diaphragm position. An improved interactive segmentation technique for the long- and short-axis anatomical slices is used. The computational domain is extended to include the proximal left atrium and ascending aorta as well as the left ventricle, and the mitral and aortic valve orifices are approximately represented. The CFD results show remarkable correspondence with the MR velocity data acquired for comparison purposes, as well as with previously published in vivo experiments (velocity and pressure). Coherent vortex formation is observed below the mitral valve, with a larger anterior vortex dominating the late-diastolic phases. Some quantitative discrepancies exist between the CFD and MRI flow velocities, owing to the limitations of the MR dataset in the valve region, heart rate differences in the anatomical and velocity acquisitions, and to certain phenomena that were not simulated. The CFD results compare well with measured ranges in literature.© 2003 Biomedical Engineering Society.

Outlier Detection and Handling for Robust 3-D Active Shape Models Search

This paper presents a new outlier handling method for volumetric segmentation with three-dimensional (3-D) active shape models. The method is based on a shape metric that is invariant to scaling, rotation and translation by using the ratio of interlandmark distances as a local shape dissimilarity measure. Tolerance intervals for the descriptors are calculated from the training samples and used as a statistical tolerance model to infer the validity of the feature points. A replacement point is then suggested for each outlier based on the tolerance model and the position of the valid points. A geometrically weighted fitness measure is introduced for feature point detection, which limits the presence of outliers and improves the convergence of the proposed segmentation framework. The algorithm is immune to the extremity of the outliers and can handle a highly significant presence of erroneous feature points. The practical value of the technique is validated with 3-D magnetic resonance (MR) segmentation tasks of the carotid artery and myocardial borders of the left ventricle

The Influence of Inflow Boundary Conditions on Intra Left Ventricle Flow Predictions

The combination of computational fluid dynamics (CFD) and magnetic resonance imaging (MRI) offers a promising tool that enables the prediction of blood flow patterns in subject-specific cardiovascular models. The influence of the model geometry on the accuracy of the simulation is well recognized. This paper addresses the impact of different boundary conditions on subject-specific simulations of left ventricular (LV) flow. A novel hybrid method for prescribing effective inflow boundary conditions in the mitral valve plane has been developed. The detailed quantitative results highlight the strengths as well as the potential pitfalls of the approach.

Subject-specific computational simulation of left ventricular flow based on magnetic resonance imaging

A detailed investigation of left ventricle (LV) flow patterns could improve our understanding of the function of the heart and provide further insight into the mechanisms of heart failure. This study presents patient-specific modelling with magnetic resonance imaging (MRI) to investigate LV blood flow patterns in normal subjects. In the study, the prescribed LV wall movements based on the MRI measurements drove the blood flow in and out of the LV in computational fluid dynamics simulation. For the six subjects studied, the simulated LV flow swirls towards the aortic valve and is ejected into the ascending aorta with a vertical flow pattern that follows the left-hand rule. In diastole, the inflow adopts a reasonably straight route (with no significant secondary flow) towards the apex in the rapid filling phase with slight variations in the jet direction between different cases. When the jet reaches about two thirds of the distance from the inflow plane to the apex, the blood flow starts to change direction and swirls towards the apex. In the more slowly filling phase, a centrally located jet is evident with vortices located on both sides of the jet on an anterior—posterior plane that passes through the mitral and aortic valves. In the inferior—superior plane, a main vortex appears for most of the cases in which an anticlockwise vortex appears for three cases and a clockwise vortex occurs for one case. The simulated flow patterns agree well qualitatively with MRI-measured flow fields.

Flow and myocardial interaction: an imaging perspective

Heart failure due to coronary artery disease has considerable morbidity and poor prognosis. An understanding of the underlying mechanics governing myocardial contraction is a prerequisite for interpreting and predicting changes induced by heart disease. Gross changes in contractile behaviour of the myocardium are readily detected with existing techniques. For more subtle changes during early stages of cardiac dysfunction, however, a sensitive method for measuring, as well as a precise criterion for quantifying, normal and impaired myocardial function is required. The purpose of this paper is to outline the role of imaging, particularly cardiovascular magnetic resonance (CMR), for investigating the fundamental relationships between cardiac morphology, function and flow. CMR is emerging as an important clinical tool owing to its safety, versatility and the high-quality images it produces that allow accurate and reproducible quantification of cardiac structure and function. We demonstrate how morphological and functional assessment of the heart can be achieved by CMR and illustrate how blood flow imaging can be used to study flow and structure interaction, particularly for elucidating the underlying haemodynamic significance of directional changes and asymmetries of the cardiac looping. Future outlook on combining imaging with engineering approaches in subject-specific biomechanical simulation is also provided.

The grand challenges of Science Robotics

These 10 grand challenges may have major breakthroughs, research, and/or socioeconomic impacts in the next 5 to 10 years. One of the ambitions of Science Robotics is to deeply root robotics research in science while developing novel robotic platforms that will enable new scientific discoveries. Of our 10 grand challenges, the first 7 represent underpinning technologies that have a wider impact on all application areas of robotics. For the next two challenges, we have included social robotics and medical robotics as application-specific areas of development to highlight the substantial societal and health impacts that they will bring. Finally, the last challenge is related to responsible innovation and how ethics and security should be carefully considered as we develop the technology further.

Combined CFD/MRI Analysis of Left Ventricular Flow

The relationship between the morphology and blood flow of the Left Ventricle (LV) during myocardial remodelling is complex and not yet fully understood. Cardiovascular MR (CMR) velocity imaging is a versatile tool for the observation of general flow patterns in-vivo. More detailed understanding of the coupled relationship between blood flow patterns and myocardial wall motion can be further enhanced by the combined use of Computational Fluid Dynamics (CFD) and CMR. This permits the generation of comprehensive high-resolution velocity fields and the assessment of dynamic indices, such as mass transport and wall shear stress, that are important but cannot be measured directly by using imaging alone. One of the key drawbacks of ventricular flow simulation using CFD is that it is sensitive to the prescribed inflow boundary conditions. Current research in this area is limited and the extent to which this affects in-vivo flow simulation is unknown. In this work, we measure this sensitivity as a function of the inflow direction and determine the limit that is required for accurate ventricular flow simulation. This represents an important step towards the development of a combined MR/CFD technique for detailed LV flow analysis.

Evaluation of carotid artery wall volume measurement using novel semiautomated analysis software

To evaluate semiautomated analysis software for measuring the total carotid arterial wall volume (TWV) as a measure of atheroma burden.