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

## TL;DR

This study compares two AR tools for surgical navigation, finding that they improve accuracy but require more time and effort than traditional methods.

## Contribution

The study introduces and evaluates two novel software-based tools for AR-assisted surgical registration.

## Key findings

- DOF Separation achieved the highest accuracy but required longer task times.
- PinNPivot balanced efficiency and accuracy but had initial pin placement errors.
- Assisted methods increased cognitive and physical workload compared to unassisted registration.

## Abstract

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.