Improving patient CT positioning with a 3D camera system
A three-dimensional (3D) camera system with automated patient body contour detection can help improve the positioning of patients in computed tomography (CT) gantries, according to radiologists from Erasmus Medical Center in Rotterdam, The Netherlands. In fact, such a camera system outperformed manual positioning by radiologic technologists with respect to accuracy, according to findings of a study published online October 10th in European Radiology.
Proper patient positioning is important to optimize the quality of a CT examination and the appropriate radiation dose. Because patient positioning affects a patient’s shape and size on a CT localizer radiograph, it directly affects automated tube current modulation (ATCM) and the efficacy of bowtie filters. If a patient is positioned too high or too low from the isocenter, the localizer radiograph is either magnified or reduced in width and the radiation dose applied by the ATCM increases or decreases. Radiologic technologists can use laser beams to visually check the central positioning of the patient, but this is labor-intensive and user dependent.
Radiologists and staff of the Department of Radiology and Nuclear Medicine assessed the performance and accuracy of a prototype 3D camera for body contour detection (Siemens Healthineers) and compared it to routine positioning by technologists. They conducted a study of patient positioning of 423 patients who had been manually positioned for abdomen, head, and/or thorax CT scans and of 254 patients who were positioned with use of the 3D camera system.
The positioning accuracy of each patient was expressed as a single value in millimeter representing the difference between the table height suggested by the camera or selected by the technologist and the ideal table height. The authors defined ideal table height as the height at which the scanner isocenter coincides with the patient isocenter. They computed the patient isocenter by automatic skin contour extraction in each axial image and averaged over all images in a scan.
Lead author Ronald Booij, coordinator of the CT research and innovation unit and a doctoral candidate in radiology, and colleagues determined that 31% of patients had been positioned higher than the scanner isocenter and 69% had been positioned lower by the technologists. By comparison, 79% had been positioned higher and 21% had been positioned lower when the 3D camera system was used.
The median absolute table height deviation with manual positioning was 12.0 mm for abdomen, 12.2 mm for head, 13.4 mm for thorax-abdomen, and 14.7 mm for thorax CT scans, with the median table height deviation highest for thorax CT scans. By comparison, deviation with 3D camera positioning was 6.3 mm for abdomen, 9.5 mm for head, 6.0 for thorax-abdomen, and 5.4 mm for thorax CT scans, with the median table height deviation highest for head CT scans.
The researchers did not evaluate the causes of the deviations in patients positioned by the camera. They suggested that possible causes were “inaccurate 3D depth data from the camera or inaccurate fitting of the avatar model to this depth data.” They believe that both the avatar model and its registration algorithm could be improved by training and learning from clinical data, and that additional research is needed. The open access article contains a detailed description and photos of the camera system and how its positioning system works,
The team does not dismiss the capabilities and role of technologists in proper patient positioning. “In clinical practice, we observed a more accurate positioning with the aid of the fast analysis of the 3D camera , and in our opinion, the radiographers are supported in patient positioning with the aid of the camera, rather than visually checking only. Thereby, they might be faster in determining of the ideal table height.” They strongly support the “symbiosis of the human and the smart technology” of the 3D camera system.
REFERENCE
- Booij R, Budde, RPJ, Dijkshoorn ML, et al. Accuracy of automated patient positioning in CT using a 3D camera for body contour detection. Eur Radiol. Published online October 10, 2018. doi: 10.1007/s00330-018-5745-z.