Rapid patient-specific neural networks for intraoperative X-ray to volume registration
Paper • 2503.16309 • Published • 1
xvr: X-ray to Volume Registration
xvr is a PyTorch package for training, fine-tuning, and performing 2D/3D X-ray to CT/MR registration using pose regression models. It provides a streamlined CLI and API for training patient-specific registration models efficiently. Key features include significantly faster training than comparable methods, submillimeter registration accuracy, and human-interpretable pose parameters.
Key Features
- 🚀 Single CLI/API for training and registration.
- ⚡️ Significantly faster training than existing methods.
- 📐 Submillimeter registration accuracy.
- 🩺 Human-interpretable pose parameters.
- 🐍 Pure Python/PyTorch implementation.
- 🖥️ Cross-platform support (macOS, Linux, Windows).
xvr leverages DiffDRR, the differentiable X-ray renderer.
Installation and Usage
This repository contains pretrained pose regression models to be run on public benchmarks datasets (reformatted into DICOM/NIfTI format) available here.
It also contains a foundation pose regression model (wbct/model.pth) intended to be quickly finetuned on new scans. Please refer to the GitHub repository for detailed installation instructions, usage examples, and documentation on training, finetuning, and registration.