BACKGROUND AND OBJECTIVE: Cardiac electrophysiology is a medical specialty with a long and rich tradition of computational modeling. Nevertheless, no community standard for cardiac electrophysiology simulation software has evolved yet. Here, we present the openCARP simulation environment as one solution that could foster the needs of large parts of this community. METHODS AND RESULTS: openCARP and the Python-based carputils framework allow developing and sharing simulation pipelines which automate in silico experiments including all modeling and simulation steps to increase reproducibility and productivity. The continuously expanding openCARP user community is supported by tailored infrastructure. Documentation and training material facilitate access to this complementary research tool for new users. After a brief historic review, this paper summarizes requirements for a high-usability electrophysiology simulator and describes how openCARP fulfills them. We introduce the openCARP modeling workflow in a multi-scale example of atrial fibrillation simulations on single cell, tissue, organ and body level and finally outline future development potential. CONCLUSION: As an open simulator, openCARP can advance the computational cardiac electrophysiology field by making state-of-the-art simulations accessible. In combination with the carputils framework, it offers a tailored software solution for the scientific community and contributes towards increasing use, transparency, standardization and reproducibility of in silico experiments.
Models of cardiac mechanics are increasingly used to investigate cardiac physiology. These models are characterized by a high level of complexity, including the particular anisotropic material properties of biological tissue and the actively contracting material. A large number of independent simulation codes have been developed, but a consistent way of verifying the accuracy and replicability of simulations is lacking. To aid in the verification of current and future cardiac mechanics solvers, this study provides three benchmark problems for cardiac mechanics. These benchmark problems test the ability to accurately simulate pressure-type forces that depend on the deformed objects geometry, anisotropic and spatially varying material properties similar to those seen in the left ventricle and active contractile forces. The benchmark was solved by 11 different groups to generate consensus solutions, with typical differences in higher-resolution solutions at approximately 0.5%, and consistent results between linear, quadratic and cubic finite elements as well as different approaches to simulating incompressible materials. Online tools and solutions are made available to allow these tests to be effectively used in verification of future cardiac mechanics software.
Electrical Impedance Tomography (EIT) is a clini- cally used tool for bed-side monitoring of ventilation. Previous work also showed a high potential for lung perfusion moni- toring with indicator-enhanced EIT. However, many research questions have yet to be answered before it can be broadly ap- plied in clinical everyday life. The goal of this work is to eval- uate a new method to improve EIT perfusion measurements. Pulmonary hemodynamic transfer functions were estimated using regularized deconvolution with Tikhonov regularization to estimate spatial perfusion parameters. The final comparison between EIT images and PET scans showed a median corre- lation of 0.897 for the images which were reconstructed using the regularized deconvolution. In comparison the previously used maximum slope method led to a median correlation of 0.868.
Student Theses (1)
X. Augustin. Optimierung der Separation des Indikatorsignals in EIT-Messspannungen und der anschließenden regularisierten Entfaltung für die pulmonale Perfusionsschätzung. Institut für Biomedizinische Technik, Karlsruher Institut für Technologie (KIT). Bachelorarbeit. 2019
Abstract:
Electrical Impedance Tomography (EIT) is already a well evaluated tool for monitoring ventilation. Previous work also showed a high potential for lung perfusion monitoring with Electrical Impedance Tomography. The simultaneous measurement of ventilation and perfusion would enable the opportunity of monitoring ventilation-perfusion ratios. This would improve the possibility of monitoring and guiding respiration of patients with lung dieseases, especially critical ill patients and potentially reducing ventilator induced lung injury. However, the lack of stability and spatial resolution of the currently used methods do not allow clinical application. Therefore the goal of this thesis is to evaluate two new methods to improve EIT perfusion measurements. First of all the voltage signals were analysed. Afterwards, a separation of the indicator signal was evaluated regarding its potential to improve the image reconstruction. The results showed a decrease of correlation between EIT and PET images when the voltages were Gamma fitted or sparsing was performed. However the analysis of the voltage measurements showed that they contain valuable information regarding the perfusion state of the lung. It is proposed that this information could be used to work on improvements of the reconstruction algorithm. In the second part of this thesis the estimation of a transfer function was evaluated, to calculate perfusion parameters. For estimating the transfer function the TSVD and Tikhonov regularization were used. Two different stable algorithms for the transfer function estimation were created. The final comparison showed a median correlation of 0,897 for the images which were reconstructed using the Tikhonov regularization. In comparison the TSVD led to a median of 0,865 and the previously used maximum-slope method led to a median correlation of 0,868. However none of the evaluated methods showed a measurable improvement of the perfusion estimation, which was the goal of this thesis. But a visual comparison of the reconstructed EIT images showed noticeable differences. This result indicated that a transfer function estmiation might have the potential to improve the spatial resolution. All investigated methods strongly depend on other necessary steps of the image reconstruction. The transfer function estimation depends on the heart region detection for estimating the Arterial Input Function (AIF). Another step, which affects the results is the comparison between the PET and EIT images. The image comparison again depends on the heart region detection, as the heart region is eliminated from the images. Therefore, it is proposed to work on a better heart region detection which might have the potential to improve the overall outcome of the image reconstruction process.