Real-Time Cardiovascular Hemodynamics: Importing DICOM Segmentations into UE5 for Surgical Digital Twins

By Rizowan Ahmed (@riz1raj)
Senior Technology Analyst | Covering Enterprise IT, Hardware & Emerging Trends

For decades, medical visualization has been limited to static, two-dimensional representations. Despite advancements in computed tomography (CT) and magnetic resonance imaging (MRI) hardware, surgeons are still routinely forced to mentally map flat, greyscale DICOM slices on PACS monitors into three-dimensional, dynamic surgical realities. This cognitive gap can introduce inefficiencies during surgical planning.

The solution lies in the convergence of clinical imaging and real-time interactive computer graphics. By leveraging modern game engines, we can transition from static diagnostic images to interactive, patient-specific surgical digital twins. However, building these platforms requires bridging two different worlds: clinical medical imaging and real-time game engineering. This guide details how to import DICOM volumetric segmentation datasets into Unreal Engine 5 for real-time CFD visualization, bypassing standard pipeline bottlenecks to achieve high-fidelity real-time visualization.

The Pipeline Bottleneck: Why DICOM to Unreal Engine is Broken by Default

Before importing an asset, we must confront a fundamental architectural conflict. Medical imaging data is spatial, volumetric, and scalar (typically represented as Hounsfield Units in CT scans). Unreal Engine 5 (UE5), conversely, is a surface-boundary engine designed to render polygonal meshes, materials, and light transport.

Furthermore, the coordinate systems are mismatched. DICOM files utilize a right-handed coordinate system—typically LPS (Left-Posterior-Superior) or RAS (Right-Anterior-Superior)—where coordinates are measured in millimeters from an arbitrary scanner-defined origin. Unreal Engine 5 utilizes a left-handed, Z-up coordinate system where 1 unit equals 1 centimeter. Simply importing a raw mesh converted from DICOM into UE5 can result in inverted normals, incorrect scaling, and mirrored anatomy.

Key Technical Challenges in the Pipeline

• Voxel-to-Mesh Translation: Converting volumetric scalar fields into manifold, non-self-intersecting polygonal meshes while preserving anatomical features.
• Coordinate Space Transformation: Correctly mapping LPS/RAS coordinates to Unreal's left-handed Z-up space while preserving the anatomical orientation matrix.
• CFD Data Alignment: Aligning transient velocity vectors and pressure fields from Computational Fluid Dynamics (CFD) solvers with the anatomical geometry.
• Performance Budgets: Rendering complex vascular structures alongside dynamic volumetric fluids without inducing latency.

Step-by-Step: How to Import DICOM Volumetric Segmentation Datasets into Unreal Engine 5 for Real-Time CFD Visualization

To establish a reliable pipeline, we bypass generic FBX exporters and build a structured, reproducible data ingestion workflow using Python, 3D Slicer (or ITK-SNAP), and Unreal Engine's Datasmith or USD importer.

Step 1: Volumetric Segmentation and Isosurface Extraction

Raw DICOM data must first be segmented to isolate the target cardiovascular structures (e.g., the thoracic aorta or coronary tree). Using tools like 3D Slicer (leveraging segmentation extensions), we generate a binary labelmap of the blood pool and vessel walls.

Once segmented, apply a windowed sinc filter to smooth the mesh while preserving volume, and export the segmentation as a high-fidelity Universal Scene Description (USD) or glTF 2.0 file. USD is preferred here as it natively preserves the transform matrices and metadata from the clinical coordinate space.

Step 2: Resolving the Coordinate System Discrepancy

To automate the transformation from LPS/RAS to Unreal Engine's coordinate space, we can write a Python pre-processing script utilizing the trimesh and numpy libraries. The transformation matrix $T$ to convert RAS (3D Slicer) to Unreal Engine (Z-up, Left-Handed, Centimeters) is defined as:

# Python Transformation Matrix
import numpy as np
import trimesh

# Scale from mm to cm, swap Y and Z, invert X for left-handedness
T = np.array([
    [-0.1,  0.0,  0.0,  0.0],
    [ 0.0,  0.0,  0.1,  0.0],
    [ 0.0,  0.1,  0.0,  0.0],
    [ 0.0,  0.0,  0.0,  1.0]
])

mesh = trimesh.load('aorta_segmentation.usd')
mesh.apply_transform(T)
mesh.export('aorta_ue5_ready.usd')

Step 3: Ingesting into Unreal Engine 5 with Nanite

With our coordinate system corrected, we import the USD file into UE5. Traditional rendering pipelines would require severe decimation of this medical mesh, potentially losing surface details that dictate fluid flow. In UE5, we can leverage Nanite to preserve geometric detail.

Upon import, open the Static Mesh Editor, navigate to the Nanite Settings, and check Enable Nanite. Set the Keep Percent Triangles to 100%. Nanite compresses the vascular mesh into an optimized run-time representation, allowing us to render detailed anatomical structures with minimal CPU draw-call overhead.

Step 4: Importing and Mapping Transient CFD Data

CFD simulations output transient velocity fields $(u, v, w)$ and pressure fields ($p$) across a discrete volume mesh. To visualize this in real-time, we can map this data onto our imported geometry by converting the CFD state files into OpenVDB volumes or structured Sparse Volume Textures (SVTs).

In UE5, we read these SVTs using the Niagara Fluids framework or custom HLSL shaders. By sampling the SVT at the world position of our Niagara particles, we can drive particle velocities directly from the physical CFD solver outputs, creating a real-time representation of blood flow vectors.

Harnessing Nanite and Lumen for Hemodynamic Realism

Visualizing fluid dynamics inside the vascular mesh helps convey complex physical interactions—such as Wall Shear Stress (WSS) and flow patterns—intuitively.

Nanite: Preserving the Boundary Layer

In fluid dynamics, the boundary layer near the vessel wall is where critical interactions occur. If the imported mesh is heavily decimated, the boundary layer representation becomes artificially smooth, potentially obscuring micro-turbulences. Nanite preserves these surface structures, allowing visualization of boundary layer interactions without sacrificing frame rates.

Lumen: Simulating Subsurface Scattering and Depth Perception

Traditional flat-shaded medical visualizations can make it difficult to gauge the relative spatial depth of complex vessels. UE5's Lumen dynamic global illumination engine helps address this by providing real-time, multi-bounce indirect lighting and soft shadows within the vascular lumen.

By applying a physically-based material with Subsurface Scattering (SSS) to the vascular wall, we can simulate how light interacts with the tissue. When paired with dynamic, emissive CFD particles representing flow, this provides intuitive spatial cues regarding vessel wall depth and flow concentration.

The CFD Visualization Engine: Niagara and Sparse Volume Textures

To visualize the fluid dynamics inside the vascular mesh, we implement a Niagara Particle System that reads transient velocity fields from 3D textures.

We structure our CFD data into a 4D RGBA volume texture (where RGB represents the normalized $X, Y, Z$ velocity vectors, and A represents the pressure or Wall Shear Stress). At runtime, the Niagara system spawns particles at the inlet of the vessel. In the Niagara Particle Update script, we write a custom HLSL dynamic input to sample the volume texture:

// HLSL Niagara Custom Expression
float4 CFDData = Texture3DSample(VolumeTexture, VolumeSampler, UVW);
float3 VelocityVector = CFDData.rgb * MaxCFDVelocity;
float LocalPressure = CFDData.a;

// Update particle velocity and color based on local hemodynamics
OutVelocity = VelocityVector;
OutColor = lerp(LowPressureColor, HighPressureColor, LocalPressure);

This approach allows us to visualize dynamic flow indicators in real-time. Because the simulation calculations are pre-computed in a dedicated CFD solver and baked into the volume textures, the runtime overhead in Unreal Engine is minimized.

Architectural Decisions and Hardware Constraints

Deploying this digital twin pipeline in a clinical or research environment requires appropriate hardware targeting.

• GPU Requirements: High-performance professional GPUs with significant VRAM are recommended to handle the rendering of high-resolution Sparse Volume Textures and Nanite geometry streaming.
• Storage Architecture: Utilize high-speed NVMe SSDs to minimize latency when streaming volume textures between system RAM and GPU VRAM.
• Clinical Delivery: To deploy these visualizations, leverage Unreal Engine Pixel Streaming. The rendering is performed on a secure, GPU-accelerated server, streaming a low-latency, interactive WebRTC video feed to compatible displays or mixed-reality headsets.

Future Directions

The integration of raw clinical DICOM data directly into real-time, physically-lit game engines represents a significant advancement in personalized medicine. Ongoing developments in automated segmentation and real-time GPU-based CFD solvers continue to bring interactive, patient-specific surgical digital twins closer to standard clinical practice.

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