Ultrasound-guided real-time spinal motion visualization for spinal instability assessment

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: 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.

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Publication Details

Journal
International Journal of Computer Assisted Radiology and Surgery
Published
2026-04-17
DOI
https://doi.org/10.1007/s11548-026-03627-x
Primary Topic
Medical Imaging and Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Ultrasound-guided real-time spinal motion visualization for spinal instability assessment

F Li, Tianyu Song, Nassir Navab, Yuan Bi et al.
International Journal of Computer Assisted Radiology and Surgery
Medical Imaging and Analysis
article

Ultrasound-guided real-time spinal motion visualization for spinal instability assessment

F Li, Tianyu Song, Nassir Navab, Yuan Bi, Zhongliang Jiang
article en

Abstract

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: 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.

International Journal of Computer Assisted Radiology and Surgery
Munich Center for Quantum Science and Technology (DE), Munich Center for Machine Learning, Technical University of Munich (DE), University of Hong Kong (HK)
Bayerische Forschungsstiftung
Openalex Percentile: Top 15%
Medical Imaging and Analysis
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