
Horos is a high-performance, open-source 64-bit medical imaging viewer for macOS, built upon the established OsiriX framework. By 2026, it has maintained its status as the premier free alternative to enterprise-grade PACS workstations, widely utilized by radiologists, researchers, and veterinary practitioners globally. Its technical architecture leverages Apple's Cocoa framework and OpenGL for hardware-accelerated 3D rendering, enabling complex visualizations such as Multi-Planar Reconstruction (MPR), 3D Volume Rendering, and Maximum Intensity Projection (MIP). Horos distinguishes itself through a modular plugin architecture, allowing developers to extend its capabilities for specific clinical workflows or research needs. While the core software remains free under the LGPL license, it integrates seamlessly with Horos Cloud (powered by Purview) for cloud-based reporting, storage, and cross-institutional image sharing. As AI-driven diagnostics grow in 2026, Horos serves as a critical local validation node where clinicians can audit AI-generated segmentations and findings within a native DICOM environment, ensuring data integrity before final reporting.
Real-time reconstruction of 3D data into axial, sagittal, and coronal planes with adjustable slice thickness.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
OpenGL-based volumetric visualization using ray-casting algorithms to display bone, tissue, and vascular structures.
Quantitative measurement tools for Region of Interest (ROI) calculating Mean, SD, and Area in Hounsfield units.
Comprehensive metadata scrubbing utility that removes PHI (Protected Health Information) while retaining clinical integrity.
Modular C++ and Objective-C framework allowing external developers to create custom tools.
Legacy-compatible media creation with integrated DICOM viewers for patient distribution.
Full utilization of system memory to load large-scale imaging datasets (e.g., 5000+ slice CT scans).
Clinicians need to review high-resolution scans outside the hospital network.
Registry Updated:2/7/2026
Key images are exported as high-res JPEGs for the report.
Researchers need to verify if an AI segmentation correctly identified a liver lesion.
Visualizing complex bone fractures in small animals before surgery.