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…
In the largest real-fluoroscopy test to date, xvr aligned live X-rays to a patient's own 3D scans with high accuracy in seconds, across anatomies, scan types and hospitals, without manual labeling.
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
How should this claim be treated?
Truvace Impact Record TRV-2026-1135, v2: “Rapid patient-specific neural networks for X-ray to volume registration.” Truvace, 2026-11-04. /record/TRV-2026-1135 (accessed at citation time). sha256 79e6b8736b5d5378…
Calibration history
Every change to this record since certification, in the open.
Editorial pass: sharper headline; gain/problem restated as concrete outcomes.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1135 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.