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Updated: Dec 21, 2025
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Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation
Published on: September 2, 2025
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A recent review and a taxonomy for hard and soft tissue visualization-based mixed reality
Selina Tuladhar1, Nada AlSallami2, Abeer Alsadoon1,3
1School of Computing and Mathematics, Charles Sturt University, Sydney, New South Wales, Australia.
Summary
This study introduces the Data, Visualization processing, and View (DVV) taxonomy to assess mixed reality (MR) systems in image-guided surgery (IGS). The DVV taxonomy helps improve MR visualization for surgeons and researchers in the operating room.
Area of Science:
- Medical Visualization
- Surgical Technology
- Human-Computer Interaction
Background:
- Mixed reality (MR) visualization is increasingly adopted in image-guided surgery (IGS) for both hard and soft tissue procedures.
- Real-time implementation of MR systems in surgery remains limited due to challenges in setup, evaluation, and integration with operating room environments and medical imaging.
- Key limitations include a lack of end-user consideration and incomplete unification of medical images within surgical workflows.
Purpose of the Study:
- To introduce and utilize the Data, Visualization processing, and View (DVV) taxonomy for evaluating current MR systems in surgical applications.
- To provide a comprehensive framework encompassing all necessary components for validating MR systems in hard and soft tissue surgeries.
- To enhance the development and application of MR systems by providing a structured evaluation method for researchers and surgeons.
Main Methods:
- The study employs the DVV taxonomy to systematically evaluate existing MR systems.
- The taxonomy was validated and verified through comparative analysis of 24 state-of-the-art MR visualization solutions.
- Evaluation criteria included system comparison, completeness, and acceptance criteria.
Main Results:
- The DVV taxonomy was successfully applied to classify, evaluate, and validate 24 contemporary MR visualization solutions.
- The evaluation demonstrated that most aspects of the selected MR solutions were assessed and validated using the DVV framework.
- The results confirm the applicability and effectiveness of the DVV taxonomy in the context of surgical MR visualization.
Conclusions:
- The DVV taxonomy serves as a valuable resource for advancing MR visualization in IGS.
- It facilitates the classification, evaluation, and validation of MR systems, aiding in the refinement of surgical visualization processes.
- The taxonomy offers significant benefits for end-users and guides future improvements in MR technology for surgical applications.
