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Published on: February 12, 2018
Using the Microsoft Kinect for patient size estimation and radiation dose normalization: proof of concept and initial
Tessa S Cook1, Gregory Couch, Timothy J Couch
1Department of Radiology, Hospital of the University of Pennsylvania, 1 Silverstein, 3400 Spruce Street, Philadelphia, PA 19104, USA. tessa@alumni.upenn.edu
Journal of Digital Imaging
|January 25, 2013
Summary
Accurately estimating patient radiation dose requires considering patient size. This study introduces a novel method using Microsoft Kinect™ to measure 3D patient volume, improving dose estimation and CT protocols.
Area of Science:
- Medical Physics
- Radiological Imaging
- Biomedical Engineering
Background:
- Accurate patient-specific radiation dose estimation is crucial but challenging.
- Current methods often overlook patient size, using inadequate surrogates like weight or BMI.
- Existing dose metrics lack precision in reflecting individual patient anatomy.
Purpose of the Study:
- To develop and evaluate a novel method for estimating patient volume using Microsoft Kinect™.
- To improve the accuracy of patient-specific radiation dose estimation in medical imaging.
- To enable more precise normalization of dose estimates and patient-specific CT protocols.
Main Methods:
- Utilized Microsoft Kinect™, a combination RGB camera-infrared depth sensor, for 3D patient scanning.
- Developed an approach to estimate patient volume from Kinect™ depth data.
- Conducted preliminary testing and validation of the volume estimation system.
Main Results:
- The Kinect™ system demonstrated good correlation for estimating patient volume in standard upright positions.
- Variations in results were observed with different arm positions.
- Further validation is needed across diverse patient positions and demographics.
Conclusions:
- 3D patient volume estimation using Kinect™ shows promise for enhancing radiation dose assessment.
- This technique could lead to more accurate, patient-specific CT imaging protocols.
- Additional research is required to refine the method and integrate it with existing size metrics.
