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Smartphone Augmented Reality CT-Based Platform for Needle Insertion Guidance: A Phantom Study

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Abstract

Objective

To develop and assess the accuracy of an augmented reality (AR) needle guidance smartphone application.

Methods

A needle guidance AR smartphone application was developed using Unity and Vuforia SDK platforms, enabling real-time displays of planned and actual needle trajectories. To assess the application’s accuracy in a phantom, eleven operators (including interventional radiologists, non-interventional radiology physicians, and non-physicians) performed single-pass needle insertions using AR guidance (n = 8) and CT-guided freehand (n = 8). Placement errors were measured on post-placement CT scans. Two interventional radiologists then used AR guidance (n = 3) and CT-guided freehand (n = 3) to navigate needles to within 5 mm of targets with intermediate CT scans permitted to mimic clinical use. The total time and number of intermediate CT scans required for successful navigation were recorded.

Results

In the first experiment, the average operator insertion error for AR-guided needles was 78% less than that for CT-guided freehand (2.69 ± 2.61 mm vs. 12.51 ± 8.39 mm, respectively, p < 0.001). In the task-based experiment, interventional radiologists achieved successful needle insertions on each first attempt when using AR guidance, thereby eliminating the need for intraoperative CT scans. This contrasted with 2 ± 0.9 intermediate CT scans when using CT-guided freehand. Additionally, average procedural times were reduced from 13.1 ± 6.6 min with CT-guided freehand to 4.5 ± 1.3 min with AR guidance, reflecting a 66% reduction.

Conclusions

All operators exhibited superior needle insertion accuracy when using the smartphone-based AR guidance application compared to CT-guided freehand. This AR platform can potentially facilitate percutaneous biopsies and ablations by improving needle insertion accuracy, expediting procedural times, and reducing radiation exposures.

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Acknowledgements

This research was supported by the Intramural Research Program of the NIH Center for Interventional Oncology. The authors thank Elizabeth Levin, Reza Seifabadi, Michal Mauda-Havakuk, Ivane Bakhutashvili, and Andrew Mikhail for contributing their time as operators in Experiment 1. We also thank Ayele Negussie for constructive conversations on the design of the test phantoms.

Funding

This work was supported by the Center for Interventional Oncology in the Intramural Research Program of the National Institutes of Health (NIH) by intramural NIH Grants NIH Z01 1ZID BC011242 and CL040015.

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Authors and Affiliations

  1. Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA

    Rachel Hecht, Ming Li, Quirina M. B. de Ruiter, William F. Pritchard, Venkatesh Krishnasamy, Wael Saad, John W. Karanian & Bradford J. Wood

  2. Biostatistics and Clinical Epidemiology Service, Clinical Center, National Institutes of Health, Bethesda, MD, 20892, USA

    Xiaobai Li

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  1. Rachel Hecht
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Correspondence to Ming Li.

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Hecht, R., Li, M., de Ruiter, Q.M.B. et al. Smartphone Augmented Reality CT-Based Platform for Needle Insertion Guidance: A Phantom Study. Cardiovasc Intervent Radiol 43, 756–764 (2020). https://doi.org/10.1007/s00270-019-02403-6

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  • DOI: https://doi.org/10.1007/s00270-019-02403-6

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