An analysis of the effectiveness of reflective learning through watching videos recorded with smart glasses-With multiple views (student, patient, and overall) in radiography education.

The Objective Structured Clinical Examination (OSCE) is designed to assess medical students' skills and attitude competencies before clinical practice. However, no method of reflective learning using video-based content has been used in OSCE education. This study aimed to confirm whether using smart glasses-based educational content is effective for OSCE reflective learning using multiple views (patient, student, and overall). This educational intervention study included a control group exposed to the traditional learning method and an intervention group exposed to a learning method incorporating smart glasses. Participants were 117 (72 in the control group and 45 in the intervention group) third-year radiological technology students scheduled to take the OSCE and 70 (37 in the control group and 33 in the intervention group) who met the eligibility criteria. Mock OSCEs were administered before and after the educational intervention (traditional and smart glasses-based education) to investigate changes in scores. After the educational intervention, a self-reported comprehension survey and a questionnaire were administered on the effectiveness of the video-based content from different views for student reflective learning. Unexpectedly, the OSCE evaluation score after the preliminary investigation significantly increased for the smart glasses control group (0.36±0.1) compared to the intervention group (0.06±0.1) setting up the radiographic conditions (x-ray center and detector center; p = 0.042). The intervention group's lower score in the mock OSCEs may have been due to the discomfort of wearing the smart glasses to perform the radiography procedure and their unfamiliarity with the smart glasses, which may have affected their concentration. The findings suggest that smart glasses-based education for OSCEs can be improved (e.g., being easy to handle and use and trouble-free).

Estimation of patient's angle from skull radiographs using deep learning.

BACKGROUND: Skull radiography, an assessment method for initial diagnosis and post-operative follow-up, requires substantial retaking of various types of radiographs. During retaking, a radiologic technologist estimates a patient's rotation angle from the radiograph by comprehending the relationship between the radiograph and the patient's angle for adequate assessment, which requires extensive experience. OBJECTIVE: To develop and test a new deep learning model or method to automatically estimate patient's angle from radiographs. METHODS: The patient's position is assessed using deep learning to estimate their angle from skull radiographs. Skull radiographs are simulated using two-dimensional projections from head computed tomography images and used as input data to estimate the patient's angle, using deep learning under supervised training. A residual neural network model is used where the rectified linear unit is changed to a parametric rectified linear unit, and dropout is added. The patient's angle is estimated in the lateral and superior-inferior directions. RESULTS: Applying this new deep learning model, the estimation errors are 0.56±0.36° and 0.72±0.52° in the lateral and superior-inferior angles, respectively. CONCLUSIONS: These findings suggest that a patient's angle can be accurately estimated from a radiograph using a deep learning model leading to reduce retaking time, and then used to facilitate skull radiography.

[Two-dimensional Projection and Image Reconstruction].

Medical images are being recently reconstructed using several data sets. In x-ray and computed tomography (CT), 3-dimensional (3D) images are reconstructed using 2-dimensional (2D) projection data. Research regarding the image reconstruction method has been actively conducted. Further, 3D images include patient information and are used as diagnostic tools. Therefore, it is necessary to acquire a projection technique. There are several algorithms of the projection method. In this paper, we reviewed three methods. The first method is based on mathematical formulae, the second integrates pixel values at regular intervals along the x-ray track, and the third integrates pixel values multiplied by distance. We confirmed the usefulness of the projection method for reconstructing images.

[Development of the image registration program for portal and DRR images in radiation therapy].

In this article, the authors propose an image registration program of portal images and digitally reconstructed radiography (DRR) images used as simulation images for external beam radiation therapy planning. First, the center of the radiation field in a portal image taken using a computed radiograhy cassette is matched to the center of the portal image. Then scale points projected on a DRR image and the portal image are deleted, and the portal image with the radiation field is extracted. Registration of the DRR and portal images is performed using mutual information as the registration criterion. It was found that the absolute displacement misregistrations in two directions (x, y) were 1.2 +/- 0.7mm and 0.5 +/- 0.3 mm, respectively, and rotation disagreement about the z axis 0.3 +/- 0.3 degrees. It was concluded the proposed method was applicable to image registration of portal and DRR images in radiation therapy.

[Development of computer assisted learning program using cone beam projection for head radiography].

We present a computer assisted learning (CAL) program to simulate head radiography. The program provides cone beam projections of a target volume, simulating three-dimensional computed tomography (CT) of a head phantom. The generated image is 512 x 512 x 512 pixels with each pixel 0.6 mm on a side. The imaging geometry, such as X-ray tube orientation and phantom orientation, can be varied. The graphical user interface (GUI) of the CAL program allows the study of the effects of varying the imaging geometry; each simulated projection image is shown quickly in an adjoining window. Simulated images with an assigned geometry were compared with the image obtained using the standard geometry in clinical use. The accuracy of the simulated image was verified through comparison with the image acquired using radiography of the head phantom, subsequently processed with a computed radiography system (CR image). Based on correlation coefficient analysis and visual assessment, it was concluded that the CAL program can satisfactorily simulate the CR image. Therefore, it should be useful for the training of head radiography.