2023
Parametric solid models of the at-term uterus from magnetic resonance images: online resources
This file contains documents on the creation of parametric solid models of the at-term uterus. These parametric solid models are based on measurements collected from MRI and MRI segmented solid models of five pregnant patients at 38-39 weeks gestation. The measurements were then used to create three parametric models for each patient: elliptical, averaged, and multi-measure. Each parametric model was compared to the corresponding MRI segmented model visually and through finite element analysis.
The Online Resources folder contains four documents detailing the specifics of this work. Online Resource 1 contains sets of dimension measurements from the five patients. Online Resource 2 provides a detailed description of the build process to generate the averaged parametric model. Online Resource 3 provides a detailed description of the build process for the multi-measure parametric model. Online Resource 4 contains heat maps of first, second, and third principal Lagrange stain magnitude and direction for all patients and modeling methods as found through finite element analysis. Online Resource 5 contains boxplots of first, second, and third principal strain for all patients and modeling methods by anatomical section.
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LouwagieEtAl_JBiomech2024_OnlineResources.zip
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More About This Work
- Academic Units
- Mechanical Engineering
- Published Here
- October 13, 2023
Notes
This data was made publicly available on October 13, 2023. An update was made to Online Resource 5 on January 29, 2024.
The data in this file correspond to the manuscript entitled “Parametric solid models of the at-term uterus from magnetic resonance images,” to be published in the Journal of Biomechanical Engineering. The manuscript is co-authored by Erin Louwagie (Mechanical Engineering, Columbia University), Divya Rajasekharan (Mechanical Engineering, Columbia University), Arielle Feder (Mechanical Engineering, Columbia University), Shuyang Fang (Mechanical Engineering, Columbia University), Chia-Ling Nhan-Chang (Obstetrics and Gynecology, Columbia University Irving Medical Center), Mirella Mourad (Obstetrics and Gynecology, Columbia University Irving Medical Center), and Kristin Myers (Mechanical Engineering, Columbia University).
