Ultrasound-Guided Real-Time Spinal Motion Visualization for Spinal Instability Assessment
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Abstract (arXiv)
Purpose: Spinal instability is a widespread condition that causes pain, fatigue, and restricted mobility, profoundly affecting patients' quality of life. In clinical practice, the gold standard for diagnosis is dynamic X-ray imaging. However, X-ray provides only 2D motion information, while 3D modalities such as computed tomography (CT) or cone beam computed tomography (CBCT) cannot efficiently capture motion. Therefore, there is a need for a system capable of visualizing real-time 3D spinal motion while minimizing radiation exposure. Methods: We propose ultrasound as an auxiliary modality for 3D spine visualization. Due to acoustic limitations, ultrasound captures only the superficial spinal surface. Therefore, the partially compounded ultrasound volume is registered to preoperative 3D imaging. In this study, CBCT provides the neutral spine configuration, while robotic ultrasound acquisition is performed at maximal spinal bending. A kinematic model is applied to the CBCT-derived spine model for coarse registration, followed by ICP for fine registration, with kinematic parameters optimized based on the registration results. Real-time ultrasound motion tracking is then used to estimate continuous 3D spinal motion by interpolating between the neutral and maximally bent states. Results: The pipeline was evaluated on a bendable 3D-printed lumbar spine phantom. The registration error was 1.941 ± 0.199 mm and the interpolated spinal motion error was 2.01 ± 0.309 mm (median). Conclusion: The proposed robotic ultrasound framework enables radiation-reduced, real-time 3D visualization of spinal motion, offering a promising 3D alternative to conventional dynamic X-ray imaging for assessing spinal instability.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 13 Feb 2026.
Builds on
9 of 25 references.
References · 9
- 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
- 2023Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectivescited by 29
- 2020Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedbackcited by 26
- 2023MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalizationcited by 18
- 2024Machine Learning in Robotic Ultrasound Imaging: Challenges and Perspectivescited by 17
- 2023Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrationscited by 14
- 2025Robotic CBCT Meets Robotic Ultrasoundcited by 3
- 2025Shape Completion and Real-Time Visualization in Robotic Ultrasound Spine Acquisitionscited by 2
- 2025Intelligent Virtual Sonographer (IVS): Enhancing Physician-Robot-Patient Communicationcited by 1
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Citing papers per year: 2026: 1.
1 paper.
- 2026Robotic Ultrasound Makes CBCT Alivecited by 0
- 2026Robotic Ultrasound Makes CBCT Alive10 Mar 2026