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Purpose <p>Conventional Mixed Reality (MR) surgical navigation suffers from two key limitations: reliance on the surgeon’s depth perception for manual registration, and the inability to automatically compensate for intraoperative patient movement. This study aims to overcome these by developing an integrated MR navigation system that enhances precision, robustness, and clinical workflow.</p> Methods <p>The core innovations of the neurosurgical navigation system include: a novel hybrid Vuforia-SPAAM registration pipeline that leverages image-based target detection to automate the initial alignment, eliminating manual error; and a dedicated real-time calibration algorithm that dynamically tracks patient motion via the Optical Tracking System (OTS) and continuously updates the virtual overlay, ensuring persistent spatial registration without need for manual re-registration. The platform provides interactive visualization of 3D brain models for surgical guidance.</p> Results <p>Experimental results show that the system achieved a mean TRE of 2.29 ± 0.49 mm and a navigation accuracy deviation of 0.49 mm ± 0.24 mm, demonstrating stable performance and sub-millimetre precision. Compared with other SPAAM-based methods, it offers higher registration accuracy and improved usability.</p> Conclusion <p>This work has successfully developed a high-precision, robust neurosurgical MR navigation system. Surgical simulation experiments will be conducted in the future.</p>

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Automated Registration and Real-Time Calibration Methods for a Mixed Reality Neurosurgical Navigation System

  • 期刊论文

  • 发表日期：

    2026年2月18日

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  • Shiqi Lin,
  • Rongqian Yang,
  • Kehai Peng,
  • Shizhong Jiang

摘要

Purpose

Conventional Mixed Reality (MR) surgical navigation suffers from two key limitations: reliance on the surgeon’s depth perception for manual registration, and the inability to automatically compensate for intraoperative patient movement. This study aims to overcome these by developing an integrated MR navigation system that enhances precision, robustness, and clinical workflow.

Methods

The core innovations of the neurosurgical navigation system include: a novel hybrid Vuforia-SPAAM registration pipeline that leverages image-based target detection to automate the initial alignment, eliminating manual error; and a dedicated real-time calibration algorithm that dynamically tracks patient motion via the Optical Tracking System (OTS) and continuously updates the virtual overlay, ensuring persistent spatial registration without need for manual re-registration. The platform provides interactive visualization of 3D brain models for surgical guidance.

Results

Experimental results show that the system achieved a mean TRE of 2.29 ± 0.49 mm and a navigation accuracy deviation of 0.49 mm ± 0.24 mm, demonstrating stable performance and sub-millimetre precision. Compared with other SPAAM-based methods, it offers higher registration accuracy and improved usability.

Conclusion

This work has successfully developed a high-precision, robust neurosurgical MR navigation system. Surgical simulation experiments will be conducted in the future.