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Improving medical radiation technologist training using virtual patient x-rays

University of British Columbia

In Canada, plain X-ray machines are operated by medical radiation technologists (MRTs). During their training, students learn how to produce radiographs on models rather than human subjects to avoid radiation exposure, and learn patient positioning with human subjects, but are not permitted to produce X-ray images of these subjects. In the latter case, instructors evaluate students’ patient positioning by only visual inspection since no images are produced for evaluation. Therefore, students do not receive visual feedback on the correlations between patient positioning and the resulting radiographs and are limited to learning to evaluate radiographs primarily through studying reference images in textbooks. Our goal was to develop a system during training that would generate estimated radiographs based on real-time measurements of joint positions of live human subjects to better prepare students for their initial clinical experiences. Our system combines real-time tracking of a live patient stand-in with CT modeling and virtual radiographs generated on-the-fly as the patient stand-ins are repositioned by MRTs. We performed a 16-participant user study to determine if this Virtual Patient X-ray (VPX) system would improve novice students’ ability to learn and perform proper patient positions. Participants were trained to learn how to position elbow and knee radiographic views using both VPX and conventional methods in which no visual feedback was provided. Patient positioning, expert evaluation scores, training time and survey results were measured to evaluate if training with VPX improved participants’ learning compared to conventional methods. We successfully designed a system generating near-real time virtual patient X-rays with a joint accuracy of 10-15°. Our user study showed a non-significant difference in evaluation scores for elbow imaging tasks and a lower significant difference in evaluation scores for knee imaging tasks using our VPX system. We also found significantly higher training times and higher confidence scores with VPX training. This leads us to believe there was a positive engagement and user learning interaction with the system. We conclude VPX shows promise for use in medical radiology classrooms for improving patient positioning skills. VPX could also serve as an effective visualization tool to complement the instructor’s feedback during in-class lessons.

Text

2019-10-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/72018

Biomedical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/