Synthetic Image Source Guide

Dynamically generate clinical-grade dummy images and phantoms without relying on existing datasets.

Generation

Dynamic Phantoms

Construct DICOM images from scratch using analytical geometry (checkerboards, rings, gradients), dense voxelized blocks, or complex smooth NURBS models to emulate organic tissue properties.

How to use it Generate a massive 1000-slice Voxelized CT scan out of thin air to test the volumetric rendering capabilities of a new 3D workstation.
Fidelity

Noise & Contrast Models

Enhance realism by injecting configurable mathematical noise profiles (Poisson, Speckle, Salt & Pepper, or Gaussian). Adjust contrast levels instantly before triggering an acquisition.

How to use it Simulate a low-dose scan by injecting heavy Poisson noise into a phantom image, verifying how downstream AI algorithms handle degraded image quality.
Privacy

Zero PHI Footprint

By relying purely on mathematically generated images, your entire testing workflow contains zero Patient Identifying Data (PID) or Protected Health Information (PHI), entirely sidestepping HIPAA and GDPR constraints.

Compliance Tip Utilise synthetic generation when working with external developers, ensuring no clinical data leaves your local network.