Democratising Advanced Medical Visualisation
through Augmented Reality & DICOM Parsing
Medical imaging—especially MRI—presents scan data as flat 2D grayscale slices, demanding years of expert
training to interpret. This severely limits patient communication and restricts medical students without
access to advanced imaging workstations.
NeuroVizAR transforms standard DICOM files into interactive 3D holographic models overlaid on
the real world through a smartphone camera. Built with Unity 2022 LTS, ARFoundation, ARCore, and the fo-dicom
library, it targets medical students, radiologists, and patients in Tier-2/Tier-3 Indian cities—offering a
cost-effective alternative to INR 5–15 lakh DICOM workstations.
Radiologists and clinicians work with 2D image stacks requiring significant expertise to mentally reconstruct
as 3D anatomy. Patients, students, and non-specialist doctors are often unable to understand these images
without guidance.
Conventional DICOM workstations costing INR 5–15 lakhs are out of reach for clinics and colleges in
Tier-2/Tier-3 cities—leaving an enormous gap in accessible medical visualisation.
HoloLens-based AR systems (Gsaxner et al., 2023) exist but cost tens of thousands of dollars. No affordable smartphone-native DICOM viewer with full 3D AR overlay exists for emerging markets.
NeuroVizAR delivers volumetric MRI rendering directly on ARCore/ARKit smartphones using Unity's GPU ray-marching shaders—no specialized hardware. First-of-its-kind for Tier-2 Indian healthcare contexts.
Brain MRI DICOM files from OpenNeuro (openneuro.org), processed via 3D Slicer, then parsed by fo-dicom 5.1.2 within Unity for pixel intensity extraction.
NeuroVizAR is not a radiology tool. It is a medical spatial computing platform serving every stakeholder in healthcare & education — across AR, 3D, VR/WebXR, and DICOM slice viewing.
A fully functional 3D MRI brain viewer with annotated zone labels, MRI slice navigation (Axial / Coronal / Sagittal), real-time Three.js WebGL rendering at 61 fps — running live on web. AR overlay and VR headset modes are in active development.
View Code & Source on GitHubusing FellowOakDicom; using FellowOakDicom.Imaging; public class DicomLoader : MonoBehaviour { public List<DicomSlice> LoadSeries(string folderPath) { var slices = new List<DicomSlice>(); foreach (var file in Directory.GetFiles(folderPath, "*.dcm")) { var dcm = DicomFile.Open(file); var pixels = new DicomImage(dcm.Dataset) .RenderImage().AsClrBitmap(); slices.Add(new DicomSlice(pixels)); } return slices; } }