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X-ray to Volume Registration

Training patient-specific 2D/3D registration models in 5 minutes

  • 🚀 A single CLI/API for training models and registering clinical data
  • ⚡️ 100x faster patient-specific model training than DiffPose
  • 📐 Submillimeter registration accuracy with new image similarity metrics
  • 🧭 Human-interpretable pose parameters for training your own models
  • 🐍 Pure Python/PyTorch implementation
  • 💾 Supports macOS, Linux, and Windows

Paper

Vivek Gopalakrishnan, David-Dimitris Chlorogiannis, Andrew Abumoussa, Anna M. Larson, Nazim Haouchine, Darren B. Orbach, Sarah Frisken, Neel Dey, and Polina Golland. [*Rapid patient-specific neural networks for X-ray to volume registration.*](https://doi.org/10.1038/s41586-026-11045-x) _Nature_ (2026): 1-9.