Predicting Spinal Column Profile from Surface Topography via 3D Non-Contact Surface Scanning - Participant data
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This dataset contains participant data for the 50 participants involved in a study of spinal column profiles from surface topography via 3D non-contact surface scanning.
Data includes Date of Birth, Age, BMI, BMI classification, and gender. Coordinates of the spinous processes (from MRI), Vitamin D capsules overlaying the spinous processes (from MRI), and 3D surface scan markers overlaying the spinous processes are provided for each participant in lateral lying on a rigid substrate.
Geographical area of data collection
kmlPolyCoords
153.025220,-27.483706 153.028267,-27.483706 153.028267,-27.485343 153.025220,-27.485343 153.025220,-27.483706
Publications
Little, Paige, Rayward, Lionel, Pearcy, Mark, Izatt, Maree, Green, Daniel, Labrom, Robert, & Askin, Geoffrey (2019) Predicting spinal profile using 3D non-contact surface scanning: Changes in surface topography as a predictor of internal spinal alignment. PLoS One, 14(9), Article number: e02224531-15.
https://eprints.qut.edu.au/133771/
Research areas
Spinal column
3D surface scanning
Magnetic Resonance Imaging
Cite this collection
Rayward, Lionel; Little, J. Paige; (2022): Predicting Spinal Column Profile from Surface Topography via 3D Non-Contact Surface Scanning - Participant data. Queensland University of Technology. (Dataset) https://doi.org/10.25912/RDF_1665115151418
Data file types
Data files are in Microsoft Excel format (.xlsx).
Licence
Copyright
© Queensland University of Technology, 2022.
Dates of data collection
From 2017-09-27 to 2019-11-02
Connections
Has chief investigator
Contacts
Other
Date record created:
2022-09-09T09:44:39
Date record modified:
2022-11-15T14:36:30
Record status:
Published - Open Access