The potential of 3D models and augmented reality in teaching cross-sectional radiology.
Med Teach
; 45(10): 1108-1111, 2023 10.
Article
em En
| MEDLINE
| ID: mdl-37542360
ABSTRACT
What was the educational challenge?The complexity and variability of cross-sectional imaging present a significant challenge in imparting knowledge of radiologic anatomy to medical students.What was the solution?Recent advancements in three-dimensional (3D) segmentation and augmented reality (AR) technology provide a promising solution. These advances allow for the creation of interactive, patient-specific 3D/AR models which incorporate multiple imaging modalities including MRI, CT, and 3D rotational angiography can help trainees understand cross-sectional imaging.How was the solution implemented?To create the model, DICOM files of patient scans with slice thicknesses of 1 mm or less are exported to a computer and imported to 3D Slicer for registration. Once registered, the files are segmented with Vitrea software utilizing thresholding, region growing, and edge detection. After the creation of the models, they are then imported to a web-based interactive viewing platform and/or AR application.What lessons were learned that are relevant to a wider global audience?Low-resource 3D/AR models offer an accessible and intuitive tool to teach radiologic anatomy and pathology. Our novel method of creating these models leverages recent advances in 3D/AR technology to create a better experience than traditional high and low-resource 3D/AR modeling techniques. This will allow trainees to better understand cross-sectional imaging.What are the next steps?The interactive and intuitive nature of 3D and AR models has the potential to significantly improve the teaching and presentation of radiologic anatomy and pathology to a medical student audience. We encourage educators to incorporate 3D segmentation models and AR in their teaching strategies.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Radiologia
/
Realidade Aumentada
Tipo de estudo:
Diagnostic_studies
/
Prevalence_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article