Rod Hose

A vision and strategy for the virtual physiological human in 2010 and beyond

European funding under framework 7 (FP7) for the virtual physiological human (VPH) project has been in place now for nearly 2 years. The VPH network of excellence (NoE) is helping in the development of common standards, open-source software, freely accessible data and model repositories, and various training and dissemination activities for the project. It is also helping to coordinate the many clinically targeted projects that have been funded under the FP7 calls. An initial vision for the VPH was defined by framework 6 strategy for a European physiome (STEP) project in 2006. It is now time to assess the accomplishments of the last 2 years and update the STEP vision for the VPH. We consider the biomedical science, healthcare and information and communications technology challenges facing the project and we propose the VPH Institute as a means of sustaining the vision of VPH beyond the time frame of the NoE.

Big Data, Big Knowledge: Big Data for Personalized Healthcare

The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including nontextual information; distributed data management under security and performance constraints; specialized analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the “physiological envelope” during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority.

A vision and strategy for the virtual physiological human: 2012 update.

European funding under Framework 7 (FP7) for the virtual physiological human (VPH) project has been in place now for 5 years. The VPH Network of Excellence (NoE) has been set up to help develop common standards, open source software, freely accessible data and model repositories, and various training and dissemination activities for the project. It is also working to coordinate the many clinically targeted projects that have been funded under the FP7 calls. An initial vision for the VPH was defined by the FP6 STEP project in 2006. In 2010, we wrote an assessment of the accomplishments of the first two years of the VPH in which we considered the biomedical science, healthcare and information and communications technology challenges facing the project (Hunter et al. 2010 Phil. Trans. R. Soc. A 368, 2595–2614 (doi:10.1098/rsta.2010.0048)). We proposed that a not-for-profit professional umbrella organization, the VPH Institute, should be established as a means of sustaining the VPH vision beyond the time-frame of the NoE. Here, we update and extend this assessment and in particular address the following issues raised in response to Hunter et al.: (i) a vision for the VPH updated in the light of progress made so far, (ii) biomedical science and healthcare challenges that the VPH initiative can address while also providing innovation opportunities for the European industry, and (iii) external changes needed in regulatory policy and business models to realize the full potential that the VPH has to offer to industry, clinics and society generally.

Simulations of time harmonic blood flow in the Mesenteric artery: comparing finite element and lattice Boltzmann methods.

BackgroundSystolic blood flow has been simulated in the abdominal aorta and the superior mesenteric artery. The simulations were carried out using two different computational hemodynamic methods: the finite element method to solve the Navier Stokes equations and the lattice Boltzmann method.ResultsWe have validated the lattice Boltzmann method for systolic flows by comparing the velocity and pressure profiles of simulated blood flow between methods. We have also analyzed flow-specific characteristics such as the formation of a vortex at curvatures and traces of flow.ConclusionThe lattice Boltzmann Method is as accurate as a Navier Stokes solver for computing complex blood flows. As such it is a good alternative for computational hemodynamics, certainly in situation where coupling to other models is required.

A patient-specific study of type-B aortic dissection: evaluation of true-false lumen blood exchange

BackgroundAortic dissection is a severe pathological condition in which blood penetrates between layers of the aortic wall and creates a duplicate channel – the false lumen. This considerable change on the aortic morphology alters hemodynamic features dramatically and, in the case of rupture, induces markedly high rates of morbidity and mortality.MethodsIn this study, we establish a patient-specific computational model and simulate the pulsatile blood flow within the dissected aorta. The k-ω SST turbulence model is employed to represent the flow and finite volume method is applied for numerical solutions. Our emphasis is on flow exchange between true and false lumen during the cardiac cycle and on quantifying the flow across specific passages. Loading distributions including pressure and wall shear stress have also been investigated and results of direct simulations are compared with solutions employing appropriate turbulence models.ResultsOur results indicate that (i) high velocities occur at the periphery of the entries; (ii) for the case studied, approximately 40% of the blood flow passes the false lumen during a heartbeat cycle; (iii) higher pressures are found at the outer wall of the dissection, which may induce further dilation of the pseudo-lumen; (iv) highest wall shear stresses occur around the entries, perhaps indicating the vulnerability of this region to further splitting; and (v) laminar simulations with adequately fine mesh resolutions, especially refined near the walls, can capture similar flow patterns to the (coarser mesh) turbulent results, although the absolute magnitudes computed are in general smaller.ConclusionsThe patient-specific model of aortic dissection provides detailed flow information of blood transport within the true and false lumen and quantifies the loading distributions over the aorta and dissection walls. This contributes to evaluating potential thrombotic behavior in the false lumen and is pivotal in guiding endovascular intervention. Moreover, as a computational study, mesh requirements to successfully evaluate the hemodynamic parameters have been proposed.

@neurIST: Infrastructure for Advanced Disease Management Through Integration of Heterogeneous Data, Computing, and Complex Processing Services

The increasing volume of data describing human disease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the @neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the systems architecture is generic enough that it could be adapted to the treatment of other diseases. Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers clinicians the tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medical researchers gain access to a critical mass of aneurysm related data due to the systems ability to federate distributed information sources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access and work on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand for performing computationally intensive simulations for treatment planning and research.

@neurIST - Towards a System Architecture for Advanced Disease Management through Integration of Heterogeneous Data, Computing, and Complex Processing Services

This paper presents the system architecture of the @neurIST project, which aims at supporting the research and treatment of cerebral aneurysms by bringing together heterogeneous data, computing and complex processing services. The architecture is generic enough to adapt it to the treatment of other diseases beyond cerebral aneurysms. The paper describes the generic requirements of the system and presents the architecture, applications and middleware technologies used to realise the system and highlights the innovations in @neurIST.

Share and enjoy: anatomical models database—generating and sharing cardiovascular model data using web services

Sharing data between scientists and with clinicians in cardiac research has been facilitated significantly by the use of web technologies. The potential of this technology has meant that information sharing has been routinely promoted through databases that have encouraged stakeholder participation in communities around these services. In this paper we discuss the Anatomical Model Database (AMDB) (Gianni et al. Functional imaging and modeling of the heart. Springer, Heidelberg, 2009; Gianni et al. Phil Trans Ser A Math Phys Eng Sci 368:3039–3056, 2010) which both facilitate a database-centric approach to collaboration, and also extends this framework with new capabilities for creating new mesh data. AMDB currently stores cardiac geometric models described in Gianni et al. (Functional imaging and modelling of the heart. Springer, Heidelberg, 2009), a number of additional cardiac models describing geometry and functional properties, and most recently models generated using a web service. The functional models represent data from simulations in geometric form, such as electrophysiology or mechanics, many of which are present in AMDB as part of a benchmark study. Finally, the heartgen service has been added for producing left or bi-ventricle models derived from binary image data using the methods described in Lamata et al. (Med Image Anal 15:801–813, 2011). The results can optionally be hosted on AMDB alongside other community-provided anatomical models. AMDB is, therefore, a unique database storing geometric data (rather than abstract models or image data) combined with a powerful web service for generating new geometric models.

Acquisition of 3-D Arterial Geometries and Integration with Computational Fluid Dynamics

A system for acquisition of 3-D arterial ultrasound geometries and integration with computational fluid dynamics (CFD) is described. The 3-D ultrasound is based on freehand B-mode imaging with positional information obtained using an optical tracking system. A processing chain was established, allowing acquisition of cardiac-gated 3-D data and segmentation of arterial geometries using a manual method and a semi-automated method, 3D meshing and CFD. The use of CFD allowed visualization of flow streamlines, 2-D velocity contours and 3-D wall shear stress. Three-dimensional positional accuracy was 0.17-1.8mm, precision was 0.06-0.47mm and volume accuracy was 4.4-15%. Patients with disease and volunteers were scanned, with data collection from one or more of the carotid bifurcation, femoral bifurcation and abdominal aorta. An initial comparison between a manual segmentation method and a semi-automated method suggested some advantages to the semi-automated method, including reduced operator time and the production of smooth surfaces suitable for CFD, but at the expense of over-smoothing in the diseased region. There were considerable difficulties with artefacts and poor image quality, resulting in 3-D geometry data that was unsuitable for CFD. These artefacts were exacerbated in disease, which may mean that future effort, in the integration of 3-D arterial geometry and CFD for clinical use, may best be served using alternative 3-D imaging modalities such as magnetic resonance imaging and computed tomography.

Personalization of cubic hermite meshes for efficient biomechanical simulations

Cubic Hermite meshes provide an efficient representation of anatomy, and are useful for simulating soft tissue mechanics. However, their personalization can be a complex, time consuming and labour-intensive process. This paper presents a method based on image registration and using an existing template for deriving a patient-specific cubic Hermite mesh. Its key contribution is a solution to customise a Hermite continuous description of a shape with the use of a discrete warping field. Fitting accuracy is first tested and quantified against an analytical ground truth solution. To then demonstrate its clinical utility, a generic cubic Hermite heart ventricular model is personalized to the anatomy of a patient, and its mechanical stability is successfully tested. The method achieves an easy, fast and accurate personalization of cubic Hermite meshes, constituting a crucial step for the clinical adoption of physiological simulations.