An efficient algorithm for mapping imaging data to 3D unstructured grids in computational biomechanics

Abstract

SUMMARY Geometries for organ scale and multiscale simulations of organ function are now routinely derived from imaging data. However, medical images may also contain spatially heterogeneous information other than geometry that are relevant to such simulations either as initial conditions or in the form of model parameters. In this manuscript, we present an algorithm for the efficient and robust mapping of such data to imaging-based unstructured polyhedral grids in parallel. We then illustrate the application of our mapping algorithm to three different mapping problems: (i) the mapping of MRI diffusion tensor data to an unstructured ventricular grid; (ii) the mapping of serial cyrosection histology data to an unstructured mouse brain grid; and (iii) the mapping of computed tomography-derived volumetric strain data to an unstructured multiscale lung grid. Execution times and parallel performance are reported for each case.

Построение Четырехкамерного Геометрического Образа Сердца Человека На Основе Рентгеновской Томографии

“…Следует отметить, что в большинстве работ исследователи выполняют построение только левого желудочка или двух желудочков совместно [8,16,17]. На таких образах можно выполнять моделирование электродинамических процессов, поскольку желудочки отделены от предсердий атриовентрикулярной перегородкой, которая препятствует прохождению потенциала действия между ними.…”

Velo and REXAN — Integrated data management and high speed analysis for experimental facilities

“…Knowledge associated with each method as described above will eventually complement existing domain knowledge in the area of chemical image and together will constitute a comprehensive knowledge base in support of CII data analysis. Based on this constantly growing knowledge based we will develop tools to automate whenever possible the reuse of data analysis services and associated parameters [30], [31].…”

“…Grids from several disparate datasets and modalities can be mapped [40], [41], [31] to a common framework for storage and/or analysis. Careful design of the objective function can ensure that the information content of the image is preserved.…”