Our methodology employs the advanced technique of volumetric rendering. Volumetric rendering, broadly defined as “a set of techniques used to display a 2D projection of a 3D discretely sampled data set” [6], serves as a powerful tool in the visualization of complex anatomical structures. In our process, we utilize 3D voxel data from a medical CT scan, where each voxel represents the density at that specific point in the brain. The data, conforming to the DICOM (Digital Imaging and Communications in Medicine [7]) standard, is read from the file and stored in a 3D texture, which fits neatly into a virtual box. It is important to note that DICOM files standard allows the storing and transmission of medical imaging information, including MRIs and CTs. DICOM files carry header data such as patient information, image acquisition parameters, and other metadata. They also contains pixel data, which represents the actual image.

During rendering, we display this box, only drawing its back faces (refer to Figure 1). We then employ a technique called raymarching to visualize the data. Raymarching is a rendering method where rays are cast through the 3D space to determine the visible surfaces. As these rays traverse the box, they fetch the densities of each voxel within, enabling us to render the intricate structures inside the brain. Rather than relying on conventional 2D representations, volumetric rendering allows us to transform data from neuroimages or brain CT scans into immersive 3D visualizations. This not only enhances the accuracy of visualizing intricate details within the brain, but also provides a more comprehensive and intuitive understanding of the neuroanatomical structures under examination.

Using VR in neuroimaging analysis offers several advantages over using traditional software like 3D Slicer. VR provides an immersive environment where complex neural structures can be visualized and manipulated in a three-dimensional space, enhancing the spatial understanding of the brain’s anatomy which can potentially facilitate the identification of abnormalities. However, there are some cons to consider. The learning curve for effectively utilizing VR technology can be steep for those without prior experience, as it requires acclimation to the VR interface and navigation within a virtual space. Additionally, while VR headsets are becoming more widely available, they are not yet as ubiquitous as conventional computer systems. The cost and the need for compatible hardware can be barriers to widespread adoption in all medical facilities. Despite these challenges, the potential benefits of enhanced spatial resolution and interactive capabilities make VR a compelling tool for neuroimaging analysis.

This ongoing initiative at the EAC not only represents a project but stands as evidence of UA Little Rock’s commitment to advancing knowledge and embracing innovative approaches in the realm of healthcare technology. Throughout this project, the EAC emphasizes its commitment to provide solution that remain at the forefront of reliable and robust data analytics in the pursuit of transformative advancements.