CONCEPTUAL DESIGN AND PRELIMINARY RESULTS OF A VR-BASED RADIATION SAFETY TRAINING SYSTEM FOR INTERVENTIONAL RADIOLOGISTS

Student perceptions of the use of three-dimensional (3-D) virtual reality (VR) simulation in the delivery of radiation protection training for radiography and medical students

BACKGROUND

VR simulation-based learning is increasingly used in healthcare education to prepare students for clinical practice. This study investigates healthcare students' experience of learning radiation safety in a simulated interventional radiology (IR) suite.

METHOD

Radiography students (n = 35) and medical students (n = 100) were introduced to 3D VR radiation dosimetry software designed to improve the learners' understanding of radiation safety in IR. Radiography students underwent formal VR training and assessment, which was complemented with clinical placement. Medical students practiced similar 3D VR activities informally without assessment. An online questionnaire containing Likert questions and open-ended questions was used to gather student feedback on the perceived value of VR-based radiation safety education. Descriptive statistics and Mann-Whitney U tests were used to analyse Likert-questions. Open-ended question responses were thematically analysed.

RESULTS

A survey response rate of 49% (n = 49) and 77% (n = 27) was obtained from radiography and medical students respectively. Most respondents (80%) enjoyed their 3D VR learning experience, favouring the in-person VR experience to online VR. 73% felt that VR learning enhanced their confidence across all relevant learning outcomes. Whilst confidence was enhanced across both cohorts, VR learning had a greater impact on confidence levels amongst medical students with respect to their understanding of radiation safety matters (U = 375.5, p < 0.01). 3D VR was deemed a valuable assessment tool.

CONCLUSION

Radiation dosimetry simulation-based learning in the 3D VR IR suite is perceived to be a valuable pedagogical tool by radiography and medical students and enhances curricula content.

Extended Reality in Diagnostic Imaging-A Literature Review

The utilization of extended reality (ER) has been increasingly explored in the medical field over the past ten years. A comprehensive analysis of scientific publications was conducted to assess the applications of ER in the field of diagnostic imaging, including ultrasound, interventional radiology, and computed tomography. The study also evaluated the use of ER in patient positioning and medical education. Additionally, we explored the potential of ER as a replacement for anesthesia and sedation during examinations. The use of ER technologies in medical education has received increased attention in recent years. This technology allows for a more interactive and engaging educational experience, particularly in anatomy and patient positioning, although the question may be asked: is the technology and maintenance cost worth the investment? The results of the analyzed studies suggest that implementing augmented reality in clinical practice is a positive phenomenon that expands the diagnostic capabilities of imaging studies, education, and positioning. The results suggest that ER has significant potential to improve diagnostic imaging procedures' accuracy and efficiency and enhance the patient experience through increased visualization and understanding of medical conditions. Despite these promising advancements, further research is needed to fully realize the potential of ER in the medical field and to address the challenges and limitations associated with its integration into clinical practice.

Development and evaluation of the effectiveness of educational material for radiological protection that uses augmented reality and virtual reality to visualise the behaviour of scattered radiation

Understanding the behaviour of scattered radiation is important for learning appropriate radiation protection methods, but many existing visualisation systems for radiation require special devices, making it difficult to use them in education. The purpose of this study was to develop teaching material for radiation protection that can help visualise the scattered radiation with augmented reality (AR) and virtual reality (VR) on a web browser, develop a method for using it in education and examine its effectiveness. The distribution of radiation during radiography was calculated using Monte Carlo simulation, and teaching material was created. The material was used in a class for department of radiological technology students and its influence on motivation was evaluated using a questionnaire based on the evaluation model for teaching materials. In addition, text mining was used to evaluate impressions objectively. Educational material was developed that can be used in AR and VR for studying the behaviour of scattered radiation. The results of the questionnaire showed that the average value of each item was more than four on a five-point scale, indicating that the teaching material attracted the interest of users. Through text mining, it could be concluded that there was improved understanding of, and confidence in, radiation protection.

Estimating Compton scatter distributions with a regressional neural network for use in a real-time staff dose management system for fluoroscopic procedures

Staff-dose management in fluoroscopic procedures is a continuing concern due to insufficient awareness of radiation dose levels. To maintain dose as low as reasonably achievable (ALARA), we have developed a software system capable of monitoring the procedure room scattered radiation and the dose to staff members in real-time during fluoroscopic procedures. The scattered-radiation display system (SDS) acquires imaging-system signal inputs to update technique and geometric parameters used to provide a color-coded mapping of room scatter. We have calculated a discrete look-up-table (LUT) of scatter distributions using Monte-Carlo (MC) software and developed an interpolation technique for the multiple parameters known to alter the spatial shape of the distribution. However, the file size for the LUT's can be large (~2GB), leading to long SDS installation times in the clinic. Instead, this work investigated the speed and accuracy of a regressional neural network (RNN) that we developed for predicting the scatter distribution from imaging-system inputs without the need for the LUT and interpolation. This method greatly reduces installation time while maintaining real-time performance. Results using error maps derived from the structural similarity index indicate high visual accuracy of predicted matrices when compared to the MC-calculated distributions. Dose error is also acceptable with a matrix element-averaged percent error of 31%. This dose-monitoring system for staff members can lead to improved radiation safety due to immediate visual feedback of high-dose regions in the room during the procedure as well as enhanced reporting of individual doses post-procedure.

Using a convolutional neural network for human recognition in a staff dose management software for fluoroscopic interventional procedures

Staff dose management is a continuing concern in fluoroscopically-guided interventional (FGI) procedures. Being unaware of radiation scatter levels can lead to unnecessarily high stochastic and deterministic risks due to the effects of absorbed dose by staff members. Our group has developed a scattered-radiation display system (SDS) capable of monitoring system parameters in real-time using a controller-area network (CAN) bus interface and displaying a color-coded mapping of the Compton-scatter distribution. This system additionally uses a time-of-flight depth sensing camera to track staff member positional information for dose rate updates. The current work capitalizes on our body tracking methodology to facilitate individualized dose recording via human recognition using 16-bit grayscale depth maps acquired using a Microsoft Kinect V2. Background features are removed from the images using a depth threshold technique and connected component analysis, which results in a body silhouette binary mask. The masks are then fed into a convolutional neural network (CNN) for identification of unique body shape features. The CNN was trained using 144 binary masks for each of four individuals (total of 576 images). Initial results indicate high-fidelity prediction (97.3% testing accuracy) from the CNN irrespective of obstructing objects (face masks and lead aprons). Body tracking is still maintained when protective attire is introduced, albeit with a slight increase in positional data error. Dose reports are then able to be produced which contain cumulative dose to each staff member at the eye lens level, waist level, and collar level. Individualized cumulative dose reporting through the use of a CNN in addition to real-time feedback in the clinic will lead to improved radiation dose management.