Modelling and visualising the heart

Christie, G. Richard; Bullivant, David; Blackett, Shane; Hunter, Peter

doi:10.1007/s00791-002-0079-3

Cited by 34 publications

(20 citation statements)

References 14 publications

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“…Similarly, code B, which used tetrahedral finite elements, was closest to code I, which used a regular grid FDM. To foster dissemination and interaction across the research community, the results from each simulation were stored on and are available from the AMDB website (formally called euHeartDB [46]) in the form of meshes representing the cuboid in the CMGUI [47] format. Each result is a mesh representing the cuboid, with each node recording the time of activation at that point.…”

Section: Benchmark Simulationsmentioning

confidence: 99%

Verification of cardiac tissue electrophysiology simulators using an N -version benchmark

Niederer

Kerfoot

Benson

et al. 2011

Phil. Trans. R. Soc. A.

246

311

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One contribution of 11 to a Theme Issue 'Towards the virtual physiological human: mathematical and computational case studies'. Ongoing developments in cardiac modelling have resulted, in particular, in the development of advanced and increasingly complex computational frameworks for simulating cardiac tissue electrophysiology. The goal of these simulations is often to represent the detailed physiology and pathologies of the heart using codes that exploit the computational potential of high-performance computing architectures. These developments have rapidly progressed the simulation capacity of cardiac virtual physiological human style models; however, they have also made it increasingly challenging to verify that a given code provides a faithful representation of the purported governing equations and corresponding solution techniques. This study provides the first cardiac tissue electrophysiology simulation benchmark to allow these codes to be verified. The benchmark was successfully evaluated on 11 simulation platforms to generate a consensus gold-standard converged solution. The benchmark definition in combination with the gold-standard solution can now be used to verify new simulation codes and numerical methods in the future.

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Section: Benchmark Simulationsmentioning

confidence: 99%

Verification of cardiac tissue electrophysiology simulators using an N -version benchmark

Niederer

Kerfoot

Benson

et al. 2011

Phil. Trans. R. Soc. A.

246

311

View full text Add to dashboard Cite

show abstract

“…The main academic goal of CMISS is to support the IUPS Physiome Project. See reference [2] for a detailed review of the abilities of CMISS in relation to the heart.…”

Section: (D) Cellmentioning

confidence: 99%

Using CellML in Computational Models of Multiscale Physiology

Nickerson

Hunter

2005

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference

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A computational modeling framework is presented which enables the integration of multiple physics and spatial scales in models of physiological systems. A novel aspect of the framework is the use of CellML to specify all model and simulation specific mathematical equations including cellular models and material constitutive relationships. Models of cardiac electromechanics at cellular, tissue, and organ spatial scales are used to illustrate the developed and implemented framework and other applications are discussed.

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“…For comparison, if linear quadrilateral patches were used, about 100 linear patches would be required to represent the same surface with comparable accuracy. For this reason, Hermite polynomials are widely used in cardiac biomechanics studies for surface representation [25][26][27]. A simple 8x8 finite element model (FEM) with intrinsic C 1 continuity can sufficiently represent the geometry of the endocardium [26,27].…”

Section: Geometric Function Setupmentioning

confidence: 99%

Real-time segmentation of 4D ultrasound by Active Geometric Functions

Duan

Angelini

Homma

et al. 2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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Four-dimensional ultrasound based on matrix phased array transducers can capture the complex 4D cardiac motion in a complete and real-time fashion. However, the large amount of information residing in 4D ultrasound scans and novel applications under interventional settings pose a big challenge in efficiency for workflow and computer-aided diagnostic algorithms such as segmentation. In this context, a novel formulation framework of the minimal surface problem, called Active Geometric Functions (AGF), is proposed to reach truly real-time performance in segmenting 4D ultrasound data. A specific instance of AGF based on finite element modeling and Hermite surface descriptors was implemented and evaluated on 35 4D ultrasound data sets with a total of 425 time frames. Quantitative comparison to manual tracing showed that the proposed method provides LV contours close to manual segmentation and that the discrepancy was comparable to inter-observer tracing variability. The ability of such realtime segmentation will not only facilitate the diagnoses and workflow, but also enables novel applications such as interventional guidance and interactive image acquisition with online segmentation.

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Modelling and visualising the heart

Cited by 34 publications

References 14 publications

Verification of cardiac tissue electrophysiology simulators using an N -version benchmark

Verification of cardiac tissue electrophysiology simulators using an N -version benchmark

Using CellML in Computational Models of Multiscale Physiology

Real-time segmentation of 4D ultrasound by Active Geometric Functions

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