Distributed Data Management Service for VPH Applications

Koulouzis, Spiros; Belloum, Adam; Bubak, Marian; Lamata, Pablo; Nolte, Daniel; Vasyunin, Dmitry; Laat, Cees de

doi:10.1109/mic.2015.71

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Representing variation in anatomy across a cohort of individuals is, however, a well-known challenge in medical imaging analysis. In the last decade, cardiac atlases were increasingly used to study variability in shape [19][20][21][22][23] and motion [24][25][26][27][28] in healthy and diseased states. As with simulation studies, most of these atlases are focused on one cardiac chamber.…”

Section: Introductionmentioning

confidence: 99%

A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations

et al. 2020

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Computational models of the heart are increasingly being used in the development of devices, patient diagnosis and therapy guidance. While software techniques have been developed for simulating single hearts, there remain significant challenges in simulating cohorts of virtual hearts from multiple patients. To facilitate the development of new simulation and model analysis techniques by groups without direct access to medical data, image analysis techniques and meshing tools, we have created the first publicly available virtual cohort of twenty-four four-chamber hearts. Our cohort was built from heart failure patients, age 67±14 years. We segmented four-chamber heart geometries from end-diastolic (ED) CT images and generated linear tetrahedral meshes with an average edge length of 1.1 ±0.2mm. Ventricular fibres were added in the ventricles with a rule-based method with an orientation of-60˚and 80˚at the epicardium and endocardium, respectively. We additionally refined the meshes to an average edge length of 0.39±0.10mm to show that all given meshes can be resampled to achieve an arbitrary desired resolution. We ran simulations for ventricular electrical activation and free mechanical contraction on all 1.1mm-resolution meshes to ensure that our meshes are suitable for electro-mechanical simulations. Simulations for electrical activation resulted in a total activation time of 149±16ms. Free mechanical contractions gave an average left ventricular (LV) and right ventricular (RV) ejection fraction (EF) of 35±1% and 30±2%, respectively, and a LV and RV stroke volume (SV) of 95±28mL and 65±11mL, respectively. By making the cohort publicly available, we hope to facilitate large cohort computational studies and to promote the development of cardiac computational electro-mechanics for clinical applications.

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Section: Introductionmentioning

confidence: 99%

A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations

et al. 2020

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“…The infrastructure planner uses the TOSCA specification 3 to get descriptions of applications and their constraints. The infrastructure provisioner uses OCCI as its default provisioning interface, and currently supports the Amazon EC2, EGI FedCloud and ExoGen 4 Clouds. Since DRIP relies on multiple Cloud providers it offers a best-effort approach for the provision, stability and performance of the underlying virtual infrastructure.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…To deliver acceptable performance, time-critical applications thus rely not only on the infrastructure for parallel computing or fast communication between components but also on optimisation of system-level application behaviour [2,3]. The customisation of the infrastructure must consider performance constraints on applications at run-time as well as the utilisation and cost of the underlying resources across applications [4,5]. In this chapter, we present a smart infrastructure optimisation engine, called Dynamic Real-time Infrastructure Planner (DRIP), that has been developed to bridge the gap between application requirements and service delivery on the part of research support environments, to optimise the quality of service at all levels.…”

Section: Introductionmentioning

confidence: 99%

Virtual Infrastructure Optimisation

Koulouzis

Martin

Zhao

2020

Lecture Notes in Computer Science

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The increasing volumes of data being produced, curated and made available by research infrastructures in the environmental science domain require services able to optimise the delivery staging and process of data on behalf of researchers. Specialised data services for managing the data lifecycle, for creating and delivering data products, and for customised data processing and analysis, all play a crucial role in how these research infrastructures serve their communities, and many of these activities are time-critical needing to be carried out frequently within specific time windows. We describe our experiences identifying the time-critical requirements of environmental scientists making use of computational research support environments. We also present a microservice-based infrastructure optimisation suite, the Dynamic Real-time Infrastructure Planner, used for constructing virtual infrastructures for research applications on demand. This chapter is partially based on a recent paper presented in [1].

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Time‐critical data management in clouds: Challenges and a Dynamic Real‐Time Infrastructure Planner (DRIP) solution

Koulouzis

Martin

Zhou

et al. 2019

Concurrency and Computation

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SummaryThe increasing volume of data being produced, curated, and made available by research infrastructures in the environmental science domain require services that are able to optimize the delivery and staging of data for researchers and other users of scientific data. Specialized data services for managing data life cycle, for creating and delivering data products, and for customized data processing and analysis all play a crucial role in how these research infrastructures serve their communities, and many of these activities are time‐critical—needing to be carried out frequently within specific time windows. We describe our experiences identifying the time‐critical requirements of environmental scientists making use of computational research support environments. We present a microservice‐based infrastructure optimization suite, the Dynamic Real‐Time Infrastructure Planner, used for constructing virtual infrastructures for research applications on demand. We provide a case study whereby our suite is used to optimize runtime service quality for a data subscription service provided by the Euro‐Argo using EGI Federated Cloud and EUDAT's B2SAFE services, and to consider how such a case study relates to other application scenarios.

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Distributed Data Management Service for VPH Applications

Cited by 6 publications

References 14 publications

A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations

A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations

Virtual Infrastructure Optimisation

Time‐critical data management in clouds: Challenges and a Dynamic Real‐Time Infrastructure Planner (DRIP) solution

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