Volumetric rendering has become a reliable tool to perform image analysis and diagnostics. As the adoption of 3D medical imagery grows, enhancements are needed to increase the comprehension of patient conditions.

We present an expansion upon existing methodologies for volumetric rendering. We will present a literature survey that describes existing algorithms and methods and will highlight our contribution to the technology. Our approach combines the following techniques into a single gaming environment:

Volumetric rendering within VR (Virtual Reality) for intuitive interaction with simulated patient data
Improved human anatomy representation with real-time manipulation of depth and shadows
Simulation of x-ray appearance and density coloring for data visualization options
Investigation of data relationships with the overlay of multiple datasets for data fusion

This paper presents a study on the success of these combined techniques to better evaluate anomalies in medical data.

Viewing medical data in VR empowers the observer with a better understanding of the data’s relationships. The digital patient can be inspected from all angles in relation to multiple scan types. This observation practice is computationally expensive; using VR to interact with rendered volumes increases the need for performance enhancements. Our approach addresses optimization methods necessary to perform the listed techniques and maintain framerates to provide a quality user experience.

To effectively examine our approach, a use case study will be performed. The study will use a specific medical condition understood to be historically limiting to visualize and analyze using traditional methods such as coronary artery disease (CAD) or Chiari Malformations. Healthcare professionals trained in medical imaging will analyze and interpret datasets affected by the condition. They will perform this analysis with and without our methods. The results will be used to showcase the potential diagnostic benefits of our volume rendering workflow for medical modeling and simulation.

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Dr. Chase Mitchell