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Computer Science > Robotics

arXiv:2211.16544 (cs)
[Submitted on 29 Nov 2022 (v1), last revised 31 Mar 2023 (this version, v2)]

Title:Towards Transcervical Ultrasound Image Guidance for Transoral Robotic Surgery

Authors:Wanwen Chen, Megha Kalia, Qi Zeng, Emily H.T. Pang, Razeyeh Bagherinasab, Thomas D. Milner, Farahna Sabiq, Eitan Prisman, Septimiu E. Salcudean
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Abstract:Purpose: Trans-oral robotic surgery (TORS) using the da Vinci surgical robot is a new minimally-invasive surgery method to treat oropharyngeal tumors, but it is a challenging operation. Augmented reality (AR) based on intra-operative ultrasound (US) has the potential to enhance the visualization of the anatomy and cancerous tumors to provide additional tools for decision-making in surgery. Methods: We propose and carry out preliminary evaluations of a US-guided AR system for TORS, with the transducer placed on the neck for a transcervical view. Firstly, we perform a novel MRI-transcervical 3D US registration study. Secondly, we develop a US-robot calibration method with an optical tracker and an AR system to display the anatomy mesh model in the real-time endoscope images inside the surgeon console. Results: Our AR system reaches a mean projection error of 26.81 and 27.85 pixels for the projection from the US to stereo cameras in a water bath experiment. The average target registration error for MRI to 3D US is 8.90 mm for the 3D US transducer and 5.85 mm for freehand 3D US, and the average distance between the vessel centerlines is 2.32 mm. Conclusion: We demonstrate the first proof-of-concept transcervical US-guided AR system for TORS and the feasibility of trans-cervical 3D US-MRI registration. Our results show that trans-cervical 3D US is a promising technique for TORS image guidance.
Comments: 12 pages, 8 figures. Accepted by Information Processing for Computer Assisted Interventions (IPCAI 2023)
Subjects: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2211.16544 [cs.RO]
  (or arXiv:2211.16544v2 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2211.16544

Submission history

From: Wanwen Chen [view email]
[v1] Tue, 29 Nov 2022 19:12:25 UTC (5,476 KB)
[v2] Fri, 31 Mar 2023 16:19:35 UTC (6,349 KB)
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