zimmeryWo/simcbctgenerator-sct-model-PELVIS

SimCBCT Generator — Synthetic CT, Pelvis

A 3D regression model that generates synthetic CT (sCT) images from Cone-Beam CT (CBCT) scans of the PELVIS region. Part of the SimCBCTGenerator framework, which was used to generate the training data for this model (aligned input/output pairs by simulating CBCT data).

The model is trained with an nnUNet regression pipeline (nnUNetTrainerRegression_mae_deep, 3D full-resolution).

Intended Use

Convert acquired CBCT images to synthetic CT for pelvis anatomy. Typical applications include adaptive radiotherapy, where sCT is needed for dose recalculation or replanning directly from CBCT without a new planning CT acquisition. It was extensively tested for Elekta machines but initial inspection also results in good performance for Varian datasets!

Model Files

File	Description
checkpoints/model.pt	TorchScript compiled model (~390 MB)
checkpoints/metadata.json	Patch size, normalization stats, inference config
sct_generator.py	Self-contained inference class — no nnUNet install required

The model.pt is a TorchScript module exported with torch.jit.save() and must be loaded with torch.jit.load().

Requirements

torch
scipy
numpy

Quick Start

import numpy as np
from sct_generator import StandaloneRegressionInference

# Load model (pass the directory containing model.pt and metadata.json)
model = StandaloneRegressionInference(
    model_path="checkpoints/",
    device="cuda"   # or "cpu"
)

# cbct_volume: 3D numpy array of HU values, shape (D, H, W)
cbct_volume = np.load("your_cbct_volume.npy")

# Run inference — returns sCT volume in HU, same spatial shape as input
sct_output = model.predict(cbct_volume)

predict() handles internally:

• Z-score normalization of the CBCT input
• Sliding-window tiled inference with Gaussian patch blending
• Denormalization of the sCT output back to HU values with the precomputed statistics

Training Details

Field	Value
Trainer	nnUNetTrainerRegression_mae_deep
Configuration	3d_fullres
Fold	all
Loss	MAE
Input	Physics-based simulated CBCT
Output	CT
Anatomy	Pelvis

Citation

If you use this model or the SimCBCTGenerator framework, please cite:

@article{zimmermann2026simcbct,
  title   = {Eliminating Registration Bias in Synthetic CT Generation:
             A Physics-Based Simulation Framework},
  author  = {Zimmermann, Lukas and Rauter, Michael and Schmid, Maximilian
             and Georg, Dietmar and Kn\"{a}usl, Barbara},
  journal = {arXiv preprint arXiv:2602.02130},
  year    = {2026}
}

Framework repository: github.com/openvoxelmed/simcbctgenerator