Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates.
Authors
Affiliations (9)
Affiliations (9)
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Kasteelpark Arenberg 10, Leuven, 3001, Belgium. Electronic address: [email protected].
- Division of Medical Physics and Biophysics, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Graz, Austria.
- Division of Medical Physics and Biophysics, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria.
- Division of Medical Physics and Biophysics, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria; Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.
- Department of Cardiology, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium; Department of Cardiovascular Sciences, KU Leuven, Herestraat 49, Leuven, 3000, Belgium.
- Division of Radiology, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium.
- Division of Cardiology, Hartcentrum, Jessa Ziekenhuis, Stadsomvaart 11, Hasselt, 3500, Belgium; Department of Medicine and Life Sciences, University of Hasselt, Stadsomvaart 11, Hasselt, 3500, Belgium.
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Graz, Austria.
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Kasteelpark Arenberg 10, Leuven, 3001, Belgium.
Abstract
A cardiac digital twin is a virtual replica of a patient-specific heart, mimicking its anatomy and physiology. A crucial step of building a cardiac digital twin is anatomical twinning, where the computational mesh of the digital twin is tailored to the patient-specific cardiac anatomy. In a number of studies, the effect of anatomical variation on clinically relevant functional measurements like electrocardiograms (ECGs) is investigated, using computational simulations. While such a simulation environment provides researchers with a carefully controlled ground truth, the impact of anatomical differences on functional measurements in real-world patients remains understudied. In this study, we develop a biventricular statistical shape model and use it to quantify the effect of biventricular anatomy on ECG-derived and demographic features, providing novel insights for the development of digital twins of cardiac electrophysiology. To this end, a dataset comprising high-resolution cardiac CT scans from 271 healthy individuals, including athletes, is utilized. Furthermore, a novel, universal, ventricular coordinate-based method is developed to establish lightweight shape correspondence. The performance of the shape model is rigorously established, focusing on its dimensionality reduction capabilities and the training data requirements. The most important variability in healthy ventricles captured by the model is their size, followed by their elongation. These anatomical factors are found to significantly correlate with ECG-derived and demographic features. Additionally, a comprehensive synthetic cohort is made available, featuring ready-to-use biventricular meshes with fiber structures and anatomical region annotations. These meshes are well-suited for electrophysiological simulations.