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ORIGINAL RESEARCH article

Front. Robot. AI

Sec. Biomedical Robotics

This article is part of the Research TopicRobotics in Orthopedics and NeurosurgeryView all 3 articles

3D Path Planning for Robot-assisted Vertebroplasty from Arbitrary Bi-plane X-ray via Differentiable Rendering

Provisionally accepted
Blanca  InigoBlanca Inigo1*Benjamin  D. KilleenBenjamin D. Killeen1Rebecca  ChoiRebecca Choi2Michelle  SongMichelle Song1Ali  UneriAli Uneri2Majid  KhanMajid Khan2Christopher  BaileyChristopher Bailey2Axel  KriegerAxel Krieger1Mathias  UnberathMathias Unberath1
  • 1Johns Hopkins University, Baltimore, United States
  • 2Johns Hopkins Medicine, Baltimore, United States

The final, formatted version of the article will be published soon.

Robotic systems are transforming image-guided interventions by enhancing accuracy and minimizing radiation exposure. A significant challenge in robotic assistance lies in surgical path planning, which often relies on the registration of intraoperative 2D images with preoperative 3D CT scans. This requirement can be burdensome and costly, particularly in procedures like vertebroplasty, where preoperative CT scans are not routinely performed. To address this issue, we introduce a differentiable rendering-based framework for 3D transpedicular path planning utilizing bi-planar 2D X-rays. Our method integrates differentiable rendering with a vertebral atlas generated through a Statistical Shape Model (SSM) and employs a learned similarity loss to refine the SSM shape and pose dynamically, independent of fixed imaging geometries. We evaluated our framework in two stages: first, through vertebral reconstruction from orthogonal X-rays for benchmarking, and second, via clinician-in-the-loop path planning using arbitrary-view X-rays. Our results indicate that our method outperformed a normalized cross-correlation baseline in reconstruction metrics (DICE: 0.75 vs. 0.65) and achieved comparable performance to the state-of-the-art model ReVerteR (DICE: 0.77), while maintaining generalization to arbitrary views. Success rates for bipedicular planning reached 82% with synthetic data and 75% with cadaver data, exceeding the 66% and 31% rates of a 2D-to-3D baseline, respectively. In conclusion, our framework demonstrates the feasibility of versatile, CT-free 3D path planning for robot-assisted vertebroplasty, accommodating diverse intraoperative imaging conditions without requiring preoperative CT scans.

Keywords: artificial intelligence, Computer-assisted interventions, deep learning, Fluoroscopy, Intraoperative planning, Spine surgery

Received: 02 Dec 2025; Accepted: 09 Feb 2026.

Copyright: © 2026 Inigo, Killeen, Choi, Song, Uneri, Khan, Bailey, Krieger and Unberath. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Blanca Inigo

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