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<p><b>Purpose:</b> This study evaluates two virtual auxiliary tools, degrees of freedom (DOF) Separation and PinNPivot, to address depth perception limitations and high error rates in manual registration for AR-assisted surgical navigation.</p><p><b>Methods:</b> DOF Separation decouples translation and rotation using six independent controls, minimizing cumulative errors. PinNPivot constrains object motion around virtual pins to stabilize rotation. Their effectiveness in AR remains underexplored. Using a hybrid evaluation system (Vuforia and NDI optical tracking), these tools were compared to unassisted manual registration on two patient-specific phantoms, assessing accuracy, task completion time, and NASA-TLX workload scores.</p><p><b>Results:</b> PinNPivot balanced efficiency and accuracy but was prone to initial pin placement errors. DOF Separation achieved the highest accuracy but required longer task times due to iterative adjustments. NASA-TLX results showed higher cognitive and physical workload for assisted methods.</p><p><b>Conclusion:</b> DOF Separation and PinNPivot improved registration accuracy and efficiency over unassisted manual registration. As software-based tools requiring no additional hardware, they hold promise for enhancing AR-assisted surgical navigation. Future work should validate their clinical applicability in diverse scenarios.</p>

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Manual Registration in AR-Assisted Surgical Navigation: A Comparative Evaluation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月25日

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  • Jiaqi Tang,
  • Abdullah Thabit,
  • Theo van Walsum,
  • Ricardo Marroquim,
  • Mohamed Benmahdjoub

摘要

Purpose: This study evaluates two virtual auxiliary tools, degrees of freedom (DOF) Separation and PinNPivot, to address depth perception limitations and high error rates in manual registration for AR-assisted surgical navigation.

Methods: DOF Separation decouples translation and rotation using six independent controls, minimizing cumulative errors. PinNPivot constrains object motion around virtual pins to stabilize rotation. Their effectiveness in AR remains underexplored. Using a hybrid evaluation system (Vuforia and NDI optical tracking), these tools were compared to unassisted manual registration on two patient-specific phantoms, assessing accuracy, task completion time, and NASA-TLX workload scores.

Results: PinNPivot balanced efficiency and accuracy but was prone to initial pin placement errors. DOF Separation achieved the highest accuracy but required longer task times due to iterative adjustments. NASA-TLX results showed higher cognitive and physical workload for assisted methods.

Conclusion: DOF Separation and PinNPivot improved registration accuracy and efficiency over unassisted manual registration. As software-based tools requiring no additional hardware, they hold promise for enhancing AR-assisted surgical navigation. Future work should validate their clinical applicability in diverse scenarios.