CT Scan Visualization in Houdini

Goal of the Research

Medical imaging has become a cornerstone of modern healthcare, with CT (Computed Tomography) scans widely used for diagnosis, surgical planning, and biomedical research. Despite their importance, CT data is often constrained within specialized medical software, limiting cross-disciplinary use in education, research, and visual effects. At the same time, advanced computer graphics platforms such as Houdini provide powerful volumetric and procedural workflows, but lack direct support for medical imaging standards like DICOM.

This research explores the integration of CT scan visualization into Houdini through the development of a Houdini Digital Asset (HDA). The objective is to create a tool that not only converts CT scan data into interactive 3D volumes, but also enhances it with advanced features for clarity, accuracy, and cross-disciplinary usability. Unlike traditional medical viewers, this tool leverages Houdini’s procedural and generative framework, opening new possibilities for scientific visualization, simulation, and digital content creation.

CT & DICOM Procedural Graphics AI Denoising Segmentation Iso-Surface → Mesh Interactive 3D

Core Research Objectives

Automated Tissue Differentiation – separating muscles, bones, skin, and organs into distinct volumetric layers to enable clear visualization of anatomical structures.
Interactive 3D Exploration – providing real-time zooming, slicing, and rotation of volumetric data within Houdini’s viewport.
AI-Powered Denoising – reducing scan noise using machine learning techniques, improving structural visibility and making anomalies easier to detect.
Region Isolation – enabling users to select and isolate specific anatomical areas, such as an organ, bone, or localized tissue region, for focused analysis or study.
Instant Geometry Extraction – converting volumetric iso-surfaces into clean, exportable 3D meshes for medical simulation, digital human creation, or scientific visualization.

Applications

Medicine – assisting surgeons in pre-operative planning through accurate, manipulatable 3D models of patient anatomy.
Education – providing medical schools with interactive anatomy models that can be explored in real time.
Research – offering scientists flexible tools for analyzing volumetric datasets, integrating AI workflows, and exploring multi-modal imaging fusion.
Visual Effects – enabling artists to create realistic digital doubles or anatomical effects directly from real-world medical data.

Future Directions

Beyond visualization, the research paves the way for advanced workflows such as AI-assisted anomaly detection, multi-modal imaging integration (CT, MRI, PET), and collaborative real-time exploration in VR/AR environments. Future development may also include scalability for high-resolution datasets, interoperability with biomedical platforms and open standards, and procedural anatomy modeling that enhances or reconstructs incomplete scans.

The long-term vision is to establish a procedural framework that bridges medical imaging accuracy with the flexibility of computer graphics, enabling breakthroughs in healthcare, education, research, and digital media.

Technology Stack

The development of this tool combines medical imaging libraries with procedural 3D workflows. The chosen stack ensures flexibility, performance, and cross-disciplinary use across medicine, education, research, and visual effects.

Python Libraries

pydicom – a Python library for reading and writing DICOM files, the standard format for medical imaging. It enables direct access to CT scan voxel data.

OpenVDB / pyopenvdb – an open standard for sparse volumetric data. While pyopenvdb can convert medical data into VDB volumes, it often requires complex compilation across platforms. This project leverages Houdini’s built-in VDB tools in Houdini for portability and performance.

3D Software Platforms

Maya – a powerful 3D modeling and animation tool, widely used in film and games. While strong for character work, it has limited flexibility for volumetric data.

Houdini – chosen as the primary platform because of its procedural control and versatility. Houdini allows direct manipulation of volumetric geometry, supports fast simulations, and provides robust VDB integration. In addition, it offers advanced shading and rendering workflows, making it possible to move seamlessly from scientific visualization to cinematic presentation.

Why Houdini?

Unmatched procedural control over geometry and volumes.
Native support for VDB workflows without cross-platform compilation issues.
Ability to run simulations for dynamic anatomy or surgical scenarios.
Full shader assignment and rendering pipeline inside one environment.
Bridges the gap between scientific accuracy and artistic creativity.

Installation Instructions

Houdini ships with its own embedded Python interpreter. By default, this environment does not include pip, so an extra step is required before external Python libraries can be installed. The tool requires the pydicom library to read and process medical DICOM files. Below are platform-specific instructions for enabling pip and installing the required libraries inside Houdini’s Python environment.

Windows

On Windows, Houdini’s Python environment is located inside the installation directory. To enable pip, download the official get-pip.py script and run it using Houdini’s Python executable. Once pip is installed, additional libraries can be installed directly into Houdini’s Python.

"C:\Program Files\Side Effects Software\Houdini XX.X.X\python39\python.exe" get-pip.py
"C:\Program Files\Side Effects Software\Houdini XX.X.X\python39\python.exe" -m pip install pydicom

After this step, you can open a Command Prompt window, point to Houdini’s Python, and install any other required libraries in the same way.

macOS

On macOS, Houdini’s embedded Python is located inside the application bundle. Pip can be accessed using the -m option with Python. The following command installs pydicom:

/Applications/Houdini/HoudiniXX.X.X/Frameworks/Python.framework/Versions/Current/bin/python3 -m pip install pydicom

Run this command from a Terminal (bash or zsh). Replace XX.X.X with the version of Houdini you have installed.

Linux

On Linux, Houdini is usually installed under /opt. The embedded Python can be accessed through the python binary inside the installation directory. Use the following command to install pydicom:

/opt/hfsXX.X.X/python/bin/python3 -m pip install pydicom

If pip is not already available, it can be bootstrapped by running:

/opt/hfsXX.X.X/python/bin/python3 -m ensurepip

This will enable pip inside Houdini’s Python environment, after which additional packages can be installed.

Note: In all cases, replace XX.X.X with the version of Houdini installed on your system. Restart Houdini after installation to ensure the libraries are recognized.

About DICOM

DICOM (Digital Imaging and Communications in Medicine) is the standard file format for medical imaging data, including CT, MRI, and PET scans. Each DICOM file contains both the imaging data (voxels) and metadata, such as patient information, scan parameters, and acquisition details.

DICOM files are commonly obtained from:

Hospital PACS (Picture Archiving and Communication Systems) for clinical data.
Open medical imaging repositories, such as OsiriX Open DICOM Datasets or The Cancer Imaging Archive (TCIA).
Academic datasets for research, such as NIH Open Data and Other Free Medical Imaging Repositories.

This tool reads DICOM files directly, enabling researchers, artists, and educators to convert medical scans into interactive 3D visualizations.

Ease of Use

There are two ways to start using the CT Scan visualization tool:

Download the pre-built HDA from Vedha Studios Inc. – recommended for most users. Simply install the Houdini Digital Asset, drag-and-drop your DICOM folder into the parameter window, and visualize the 3D data immediately.
Build your own Python SOP node – for advanced users. Load DICOM files with pydicom and convert the voxel data into VDB volumes manually. This offers complete control over processing, thresholds, and rendering.

The workflow is fully interactive: after loading, the tool automatically processes the data and generates volumetric geometry ready for viewing, slicing, and rendering.

Join the Research

Vedha Studios R&D welcomes collaboration from researchers, developers, and enthusiasts interested in advancing medical visualization, procedural workflows, and volumetric analysis. If you are passionate about combining medical imaging with 3D procedural graphics, there are several ways to get involved:

Contribute Code or Tools – help improve the Houdini Digital Asset, add features for iso-surface extraction, AI denoising, or export workflows.
Research Collaboration – collaborate on studies involving medical imaging, visualization techniques, or volumetric simulations.
Educational Projects – assist in developing interactive learning modules for anatomy, surgical training, or biomedical research.
Feedback and Testing – provide insights, test datasets, and suggest improvements to make the tool more robust and user-friendly.

To get involved, please reach out via email at research@vedhastudios.ca or visit our Research Portal for more details.