Supporting data for "3D printed patient-specific aortic root models with internal sensors for minimally invasive applications"
2020-06-09
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2018-03-01
2019-11-30
2019-11-30
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2019-11-30
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Title
Supporting data for "3D printed patient-specific aortic root models with internal sensors for minimally invasive applications"
Published Date
2020-06-09
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Author Contact
McAlpine, Michael C
mcalpine@umn.edu
mcalpine@umn.edu
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Dataset
Experimental Data
Programming Software Code
Experimental Data
Programming Software Code
Abstract
Minimally invasive surgeries have numerous advantages, yet complications may arise from limited knowledge about the anatomical site targeted for the delivery of therapy. Transcatheter aortic valve replacement (TAVR) is a minimally invasive procedure for treating aortic stenosis. Here, we demonstrate multi-material 3D printing of patient-specific soft aortic root models with internally-integrated electronic sensor arrays that can augment testing for TAVR preprocedural planning. We evaluated the efficacies of the models by comparing their geometric fidelities with postoperative data from patients, as well as their in vitro hemodynamic performances in cases with and without leaflet calcifications. Furthermore, we uniquely demonstrated that internal sensor arrays can facilitate the optimization of bioprosthetic valve selections and in vitro placements via mapping of the pressures applied on the critical regions of the aortic anatomies. Such models may pave new avenues for mitigating the risks of postoperative complications, as well as facilitating the development of next-generation medical devices.
Description
The data set includes the experimental data and the code files for 3D printed patient-specific aortic root models with internal sensors for minimally invasive
applications.
Referenced by
Haghiashtiani, G., Qiu, K., Sanchez, J. Z., Fuenning, Z. J., Nair, P., Ahlberg, S. E., Iaizzo, P., A., McAlpine, M. C., “3D printed patient-specific aortic root models with internal sensors for minimally invasive applications”, Science Advances (2020).
https://doi.org/10.1126/sciadv.abb4641
https://doi.org/10.1126/sciadv.abb4641
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Funding information
National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health, Award Number DP2EB020537
Medtronic plc
MnDRIVE program at the University of Minnesota
The graduate school of the University of Minnesota, 2017–18 Interdisciplinary Doctoral Fellowship
The graduate school of the University of Minnesota, 2018–19 Doctoral Dissertation Fellowship
Medtronic plc
MnDRIVE program at the University of Minnesota
The graduate school of the University of Minnesota, 2017–18 Interdisciplinary Doctoral Fellowship
The graduate school of the University of Minnesota, 2018–19 Doctoral Dissertation Fellowship
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Suggested citation
Haghiashtiani, Ghazaleh; Qiu, Kaiyan; Sanchez, Jorge D Zhingre; Fuenning, Zachary J; Nair, Priya; Ahlberg, Sarah E; Iaizzo, Paul A; McAlpine, Michael C. (2020). Supporting data for "3D printed patient-specific aortic root models with internal sensors for minimally invasive applications". Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/xjv7-s394.
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| File View/Open | Description | Size |
|---|---|---|
| Figure 2.zip | Characterization of material properties | 983.42 KB |
| Figure 3.zip | Anatomical fidelity analyses of the 3D printed aortic root models and comparison to patient data | 55.85 KB |
| Figure 4.zip | In vitro hemodynamic studies with the 3D printed aortic root models | 586.9 KB |
| Figure 5 and S8.zip | Data files for the sensor array measurements for each case of valve implantation in the 3D printed model and the corresponding Matlab code for data processing | 2.22 MB |
| Figure S2.zip | Additional material characterization results | 561.02 KB |
| Figure S6B and Table S2.zip | Sensor array elements calibration data | 54.59 KB |
| Readme_3D printed aortic root models.txt | ReadMe | 5.68 KB |
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