Winston–Lutz QA Python Toolkit

Synthetic DICOM Generator + Automated Image Analysis + 3D Isocenter Reconstruction

🎯 Generate, analyze, and validate Winston–Lutz QA images with a complete Python toolkit designed for medical physicists, researchers, and students.

This toolkit provides an end-to-end workflow for Winston–Lutz quality assurance—from generating realistic synthetic EPID DICOM images with known ground truth, to automated BB detection, to reconstructing the three-dimensional radiation isocenter using multiple gantry and couch angles.

📦 What's Included

Part 1 — Synthetic Winston–Lutz DICOM Generator

Generate realistic EPID Winston–Lutz images with configurable:

• Gantry angle
• Couch angle
• 3D BB offsets (X, Y, Z)
• Field size
• Image noise and blur

Perfect for:

• Algorithm development
• Software verification
• Education and training
• Ground-truth validation

Part 2 — Automated Single-Image WL Analysis

Automatically analyzes a Winston–Lutz image by:

• Detecting the radiation field center
• Detecting the BB center with sub-pixel accuracy
• Computing image offsets (dU and dV)
• Displaying annotated analysis figures
• Producing easy-to-read numerical output

No manual measurements required.

Part 3 — 3D Radiation Isocenter Reconstruction

Combine multiple Winston–Lutz images acquired at different gantry and couch angles to estimate the three-dimensional radiation isocenter.

Features include:

• Automatic parsing of gantry and couch angles from filenames
• Reconstruction of BB offsets in IEC coordinates (X, Y, Z)
• Calculation of the total 3D isocenter deviation
• Visualization of projection geometry
• Validation using the included synthetic image generator with known ground truth

✅ Ideal For

• Medical Physicists
• Radiation Oncology QA
• Winston–Lutz Commissioning
• Researchers
• Graduate Students
• Medical Physics Education
• QA Automation Projects

🚀 Why This Toolkit?

Instead of manually measuring Winston–Lutz images, this toolkit enables you to:

• Generate realistic synthetic test datasets with known 3D offsets
• Develop and verify image-processing algorithms
• Automatically detect BB and radiation field centers
• Reconstruct the 3D radiation isocenter from multiple gantry and couch angles
• Validate your implementation against known ground truth

Whether you're learning the Winston–Lutz test, building your own QA software, or validating new algorithms, this toolkit provides a complete end-to-end workflow from image generation to 3D reconstruction.

📨 Support is included with every purchase.