Data underlying the PhD thesis: Deformation prediction and autonomous path planning for robot-assisted endovascular interventions

DOI:10.4121/c1e87614-0e77-4c61-ad6d-0f1609725d17.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/c1e87614-0e77-4c61-ad6d-0f1609725d17

Datacite citation style

Li, Zhen (2023): Data underlying the PhD thesis: Deformation prediction and autonomous path planning for robot-assisted endovascular interventions. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/c1e87614-0e77-4c61-ad6d-0f1609725d17.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This dataset contains research data supporting the findings described in the thesis of the author. The research objective is to develop a robust and efficient path-planning algorithm for endovascular interventions. The dataset includes some examples of models (.obj format) for testing path planning algorithms, building the deformable environment, and comparing the accuracy of deformation prediction. The dataset also includes some quantitative results (.xlsx) obtained from the user study with the intervention simulator and from the user study with different path-planning guidance.

Note: The patient-related dataset is not publicly accessible due to the agreement with the hospital.

History

  • 2023-07-19 first online, published, posted

Publisher

4TU.ResearchData

Format

model/obj and excel/.xlsx

Funding

  • AuTonomous intraLuminAl Surgery (grant code 813782) [more info...] European Commission

Organizations

TU Delft, Faculty of Mechanical, Maritime and Materials Engineering (3ME), Department of Biomechanical Engineering

DATA

Files (6)