Computational cardiac atlases: from patient to population and back

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.

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

“…The fibre orientation atlas has the potential to be be adapted to a patient image to enable cardiac electromechanical modelling (Marchesseau et al, 2013). A review on the applications of the computational cardiac atlas can be referred to at (Young and Frangi, 2009). …”

Antenatal architecture and activity of the human heart

“…Detailed models of human cardiomyocytes have been constructed for pacemaking [5], atrial [6] and ventricular cells [4,7], and been embedded into tissue architecture and organ models based on post-mortem anatomy and architecture [8,9], or atlases of cardiac anatomy [10]. Patient-specific modelling is becoming technically possible.…”

Precision Imaging: more descriptive, predictive and integrative imaging

“…On the one side, there is a bottom-up, datadriven direction which we like to refer to as "imagebased modelling" or more broadly, "phenomenological modelling". Perhaps starting with the success of statistical shape modelling (Young and Frangi, 2009;Castro-Mateos et al, 2014), and successive developments leading to computational atlasing, computational anatomy (Miller et al, 2015) and disease state fingerprinting (Kumar et al, 2012;Mattila et al, 2011), these and other developments accelerated by machine learning emphasize learning and inference of knowledge directly from vast amounts of imaging data (Kansagra et al, 2016;Medrano-Gracia et al, 2015;Margolies et al, 2016). This confluence of image-based computational modelling with developments on population imaging (Volzke et al, 2012) will increasingly underpin computational models and phenotypes of health and disease.…”