Revolutionizing Neuroimaging: A VR Exploration of the Brain

by Mohamed Messaoudi & William Andrew Nickols

Advised by Dr. Ivan Rodrıguez-Conde & Dr. Ahmed Abu Halimeh

Scanned Slices of the Human Brain. [1]

The Emerging Analytics Center (EAC) at the University of Arkansas at Little Rock (UA Little Rock) embarks on a transformative project, generously funded by DART (Data Analytics that are Robust and Trusted) [2]. This initiative aims to leverage the immersive capabilities of VR headsets to improve the analysis and visualization of medical data. Situated at the crossroads of neuroscience and virtual reality, this research aligns with the UA Little Rock’s commitment to cutting-edge and practically applied technology. The project explores the potential of advanced technology to elevate healthcare practices.

VR headsets, like the Meta Quest, play a central role in this endeavor. Instead of using old-fashioned methods, the project envisions a dynamic and interactive experience for understanding complex medical images, providing a visually impressive exploration of the human brain. This project goes beyond technical innovation, impacting medical education, diagnostics, and research. The immersive experience delivered by VR headsets elevates our understanding of the human brain into a three-dimensional space, offering unprecedented detail and engagement.

A significant number of research serves as testament to the successful integration of VR technologies in various medical disciplines. These studies collectively underlay the significant contributions VR has made to advancing clinical practices and medical education. “HMD-Based Virtual and Augmented Reality in Medical Education” [3] is a great article that examined the use of head-mounted display (HMD) in medical education. The review encompassed studies involving medical students, surgical trainees, and nursing interns using various AR/VR devices like the Oculus Rift, HTC VIVE, Gear VR, and HoloLens. The outcomes of these studies indicated improvements in areas such as surgical training and anatomy learning when compared to traditional teaching methods.

Another article in “A Case-Based Study with Radiologists Performing Diagnosis Tasks in Virtual Reality” [4]. This study assesses the diagnostic effectiveness of a VR interface in fracture identification over 3D volumetric reconstructions. It involves 16 medical specialists using a VR volume viewer compatible with Oculus Rift and other head-mounted displays. The study found that the VR interface was highly effective in identifying superficial fractures on head CTs. This highlights VR’s potential in enhancing the diagnostic process in radiology.

Figure 1: Converting multiple 2D arrays into a single 3D array. On the left, there is a series of 2D images, which represent individual slices of MRI or CT images. These slices are stacked on top of each other to form a sequence (denoted as N x 2D Arrays). On the right, these slices are conceptually transformed into a 3D array, suggesting the construction of a three-dimensional model from these 2D slices. [5]

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.

[1] OpenAI. A series of detailed mri brain scans. [Image]. DALL·E, 2024. URL

[2] NSF. Data Analytics that are Robust and Trusted (DART): From smart curation to socially aware decision making, 2020. URL

[3] Xuanhui Xu, Eleni Mangina, and Abraham G Campbell. Hmd-based virtual and augmented reality in medical education: a systematic review. Frontiers in Virtual Reality, 2:692103, 2021. URL

[4] J Venson, JA Berni, C Edmilson da Silva Maia, AM da Silva, M Cordeiro d’Ornellas, and A Maciel. A case-based study with
radiologists performing diagnosis tasks in virtual reality. In InMEDINFO 2017: Precision Healthcare through Informatics:
Proceedings of the 16th World Congress on Medical and Health Informatics, volume 245, page 244. IOS Press, 2018. URL

[5] Jianlong Zhou and Klaus D Tonnies. State of the art for volume rendering. Simulation, pages 1–29, 2003. URL

[6] Wikipedia. Volume Rendering, 2020. URL

[7] The DICOM standard. URL

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