Year: 2023
Type: dissertation
Abstract: Medical imaging, including Computed Tomography (CT) or Magnetic Resonance (MR), is used for verifying diagnoses and planning treatment. Compared to 2D medical images, 3D medical images have been found to increase spatial understanding of human structures in certain contexts. Emerging technologies, including viewing 2D medical images as 3D models in Extended Reality (XR), add interaction possibilities. Specifically, Virtual Reality (VR) allows medical professionals to view images in an immersive environment, without worrying about the limitations of the real environment (e.g., bad lighting). In addition, AI-generated segmentation of medical images allows the automation of a labor-intensive task usually done by radiologists. Movement, also called locomotion, is a necessary part of VR interaction. Movement methods allow the user to adjust their location in the virtual environment. The context of the VR environment can influence the suitability of a movement method, while the choice of the movement method can impact user experience. The context of 3D model interaction differs from typical VR interaction, as the user needs to inspect the model from close with small-scale, detailed movements. For research gap verification, a methodological review was conducted. 42 papers on 3D-XR model interaction found in Andor and Google Scholar were included. Even though half of the papers mentioned using movement methods, an experiment comparing them was not included in any of the papers. In the current study, an experiment was conducted to compare three movement methods, Diving, Grabbing, and Teleporting, regarding their suitability for 3D-VR image interaction. Diving was found to be significantly more successful than Teleporting. Diving and Teleporting were found equally fast compared to Grabbing, which was significantly slower. Diving, the less-studied method, received the best scores in most ratings. However, the opinions varied. Therefore, a VR system for 3D-VR medical image interaction should include multiple movement methods.
Author: Orsmaa, Lotta
Institution: Tampere University
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7132920465