aceRapid patient-specific neural networks for X-ray to volume registration
Advanced navigation techniques in image-guided interventions and surgical robotics require the rapid and precise alignment of three-dimensional (3D) preoperative volumes (such as computed tomography and magnetic resonance imaging) to two-dimensional (2D) intraoperative images (such as X-ray fluoroscopy) 1,2 . However, existing 2D/3D registration methods fail to generalize across the broad spectrum of fluoroscopy-guided procedures: intensity-based optimizers require per-individual hyperparameter tuning 3,4 , whil…
Self-supervised patient-specific neural network with physics-based simulation aligns intraoperative X-ray fluoroscopy to preoperative 3D volumes in seconds with order-of-magnitude accuracy improvement across anatomies and hospitals.
Method is presented for rigid registration only and depends on availability of a patient preoperative 3D volume for simulation and fine-tuning.
Evidence
- Peer-reviewedNature2026-09-16
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Truvace Impact Record TRV-2026-1135, v1: “Rapid patient-specific neural networks for X-ray to volume registration.” Truvace, 2026-09-18. /record/TRV-2026-1135 (accessed at citation time). sha256 96ee2a2584e9bdc1…
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