Computational cardiac atlases: from patient to population and back
Integrative models of cardiac physiology are important for understanding disease and planning intervention. Multimodal cardiovascular imaging plays an important role in defining the computational domain, the boundary/initial conditions, and tissue function and properties. Computational models can th...
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| Published in | Experimental physiology Vol. 94; no. 5; pp. 578 - 596 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford, UK
The Physiological Society
01.05.2009
Blackwell Publishing Ltd John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Integrative models of cardiac physiology are important for understanding disease and planning intervention. Multimodal cardiovascular
imaging plays an important role in defining the computational domain, the boundary/initial conditions, and tissue function
and properties. Computational models can then be personalized through information derived from in vivo and, when possible, non-invasive images. Efforts are now established to provide Web-accessible structural and functional
atlases of the normal and pathological heart for clinical, research and educational purposes. Efficient and robust statistical
representations of cardiac morphology and morphodynamics can thereby be obtained, enabling quantitative analysis of images
based on such representations. Statistical models of shape and appearance can be built automatically from large populations
of image datasets by minimizing manual intervention and data collection. These methods facilitate statistical analysis of
regional heart shape and wall motion characteristics across population groups, via the application of parametric mathematical
modelling tools. These parametric modelling tools and associated ontological schema also facilitate data fusion between different
imaging protocols and modalities as well as other data sources. Statistical priors can also be used to support cardiac image
analysis with applications to advanced quantification and subject-specific simulations of computational physiology. |
|---|---|
| AbstractList | Integrative models of cardiac physiology are important for understanding disease and planning intervention. Multimodal cardiovascular imaging plays an important role in defining the computational domain, the boundary/initial conditions, and tissue function and properties. Computational models can then be personalized through information derived from in vivo and, when possible, non-invasive images. Efforts are now established to provide Web-accessible structural and functional atlases of the normal and pathological heart for clinical, research and educational purposes. Efficient and robust statistical representations of cardiac morphology and morphodynamics can thereby be obtained, enabling quantitative analysis of images based on such representations. Statistical models of shape and appearance can be built automatically from large populations of image datasets by minimizing manual intervention and data collection. These methods facilitate statistical analysis of regional heart shape and wall motion characteristics across population groups, via the application of parametric mathematical modelling tools. These parametric modelling tools and associated ontological schema also facilitate data fusion between different imaging protocols and modalities as well as other data sources. Statistical priors can also be used to support cardiac image analysis with applications to advanced quantification and subject-specific simulations of computational physiology. Integrative models of cardiac physiology are important for understanding disease and planning intervention. Multimodal cardiovascular imaging plays an important role in defining the computational domain, the boundary/initial conditions, and tissue function and properties. Computational models can then be personalized through information derived from in vivo and, when possible, non‐invasive images. Efforts are now established to provide Web‐accessible structural and functional atlases of the normal and pathological heart for clinical, research and educational purposes. Efficient and robust statistical representations of cardiac morphology and morphodynamics can thereby be obtained, enabling quantitative analysis of images based on such representations. Statistical models of shape and appearance can be built automatically from large populations of image datasets by minimizing manual intervention and data collection. These methods facilitate statistical analysis of regional heart shape and wall motion characteristics across population groups, via the application of parametric mathematical modelling tools. These parametric modelling tools and associated ontological schema also facilitate data fusion between different imaging protocols and modalities as well as other data sources. Statistical priors can also be used to support cardiac image analysis with applications to advanced quantification and subject‐specific simulations of computational physiology. Integrative models of cardiac physiology are important for understanding disease and planning intervention. Multimodal cardiovascular imaging plays an important role in defining the computational domain, the boundary/initial conditions, and tissue function and properties. Computational models can then be personalized through information derived from in vivo and, when possible, non-invasive images. Efforts are now established to provide Web-accessible structural and functional atlases of the normal and pathological heart for clinical, research and educational purposes. Efficient and robust statistical representations of cardiac morphology and morphodynamics can thereby be obtained, enabling quantitative analysis of images based on such representations. Statistical models of shape and appearance can be built automatically from large populations of image datasets by minimizing manual intervention and data collection. These methods facilitate statistical analysis of regional heart shape and wall motion characteristics across population groups, via the application of parametric mathematical modelling tools. These parametric modelling tools and associated ontological schema also facilitate data fusion between different imaging protocols and modalities as well as other data sources. Statistical priors can also be used to support cardiac image analysis with applications to advanced quantification and subject-specific simulations of computational physiology. |
| Author | Alejandro F. Frangi Alistair A. Young |
| Author_xml | – sequence: 1 givenname: Alistair A. surname: Young fullname: Young, Alistair A. – sequence: 2 givenname: Alejandro F. surname: Frangi fullname: Frangi, Alejandro F. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19098087$$D View this record in MEDLINE/PubMed |
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| Title | Computational cardiac atlases: from patient to population and back |
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