MIT's xvr aligns surgical X-rays with 3D scans in seconds at sub-millimeter precision
MIT News AI · rss · 2026-09-16
MIT CSAIL researchers and collaborators published xvr (X-ray volume registration) in Nature, a technique that rapidly and accurately matches real-time X-rays taken during minimally invasive surgery with preoperative CT/MRI scans, helping clinicians precisely steer catheters and endoscopes.
How it works:
- Manual registration of 2D X-rays to 3D scans is slow and requires decades of training; existing AI models fail to generalize across diverse patient anatomy.
- xvr uses physics-based simulation of the X-ray process to generate 1,000 synthetic X-rays per second from a patient's own 3D scan — fully patient-specific, with no hallucination risk.
- The team pretrained a foundation model on whole-body scans from over 2,000 patients; it adapts to a new patient in 5 minutes with accuracy matching from-scratch training, at sub-millimeter precision.
Validation: Tested on the largest real 2D/3D registration dataset spanning five hospitals, dozens of bone and organ systems, and adult plus pediatric patients, xvr outperformed existing AI methods by an order of magnitude while running fast enough for emergency procedures like stroke interventions. The team is now working with surgical robotics companies on navigation tools. Lead author is Vivek Gopalakrishnan; funding came from NIH and other MIT programs.
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