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

Abstract

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.

Appendix: Reaction diffusion benchmark using the ten Tusscher model

AMDB stores a total of 96 models as part of the simulation benchmark study undertaken in [13] which represent the results of the electrophysiology simulations as activation times over a 20 × 7 × 3 mm volume of myocardial tissue. These electrophysiological simulations used the Ten Tusscher 2006 [18] model to simulate electricity propagation over the volume, such that each node’s data represent activation time in milliseconds (the time at which the upstroke passed through 0 mV).

The study aimed to establish a benchmark physiology problem with a simple setup which can be reproduced easily by various research groups. To perform a benchmark using this simulation, a number of groups were asked to develop code to perform the simulation using the three spacings (0.5, 0.2, 0.1 mm) and three timestep (0.005, 0.01, 0.05 ms) setups. The results from these groups are then compared to demonstrate that the problem is coherent and consistent, and therefore the results are useful in testing the correctness of these simulation implementations.

Eleven groups were invited to perform the simulation and submit their results. This produces 99 data sets to upload to the site, excepting four instances where the simulations failed for 0.1 mm resolution and 0.05 ms time steps, and one which produced un-physiological results. The table given in Table 1 lists the participants, their institutions, the numerical method used and type of mesh or grid used.

Table 1 List of study participants

Table 2 gives the L ² norm of the difference in activation times between simulations with the highest temporal and spatial resolutions. These values represent the quantitative difference between results, which would be expected to produce similar values when comparing implementations using the same underlying method, either finite element or finite difference. However one result shown here is that in some cases, such as A and G, there is a similarity of results despite differing methods being used.

Table 2 L ² norm differences between simulations of highest temporal and spatial resolution

To generate this table as well as other figures, the AMDB website was expanded to include functionality for analysing and processing data stored as models. The section of the site dedicated to functional models allows users to plot the activation times for one or more EP benchmark models and produce a graph in SVG format like those presented here. A second analysis samples the values at all eight corners of selected result sets, as well as the center point. A third analysis performs the L ² norm calculations between selected models, which was used to generate the data for Table 2.