Clinical Scorecard: Depth Buffer-Based Multi-Volume Rendering for Enhanced Surgical Planning in Virtual Reality
At a Glance
Category
Detail
Condition
Visualization challenges in surgical planning involving multiple overlapping volumetric datasets
Key Mechanisms
Direct volume rendering with depth buffer handling for multiple intersecting volumes in VR
Target Population
Surgeons and clinicians involved in surgical planning and training
Care Setting
Virtual reality surgical planning environments connected to desktop systems
Key Highlights
Novel multi-volume rendering approach enables visualization of dozens of independent volume fragments with real-time performance suitable for VR surgical planning.
Depth buffer technique correctly handles occlusion and intersection of multiple volumes, overcoming limitations of single-volume and mesh-based rendering.
Close collaboration with experienced spine and neurosurgeons ensured clinical relevance for complex anatomical visualization, minimally invasive approach planning, and medical education.
Guideline-Based Recommendations
Diagnosis
Use direct volume rendering to retain original raw volumetric data for higher image quality and accuracy.
Segment volumes even roughly to enable high-quality visualization without loss of detail.
Management
Employ depth buffer-based multi-volume rendering to allow interactive manipulation of multiple overlapping datasets in VR.
Utilize optimization techniques such as early ray termination, empty space skipping, and foveated rendering to maintain performance.
Monitoring & Follow-up
Engage experienced clinicians for qualitative assessment and iterative feedback during development and clinical use.
Monitor rendering performance to ensure acceptable frame rates and image quality to prevent VR-induced motion sickness.
Risks
Be aware of potential performance degradation when rendering multiple volumes simultaneously without optimization.
Avoid resampling entire scenes into single volumes to prevent loss of image quality and preclude real-time interaction.
by Balázs Faludi, Marek Żelechowski, Maria Licci, Norbert Zentai, Attill Saemann, Daniel Studer, Georg Rauter, Raphael Guzman, Carol Hasler, Gregory F. Jost, Philippe C. Cattin