Modeling the surgical exposure of anatomy in robot-assisted laparoscopic partial nephrectomy

Michael A. Kokko; John D. Seigne; Douglas W. Van Citters; Ryan J. Halter

doi:10.1117/12.2550605

16 March 2020 Modeling the surgical exposure of anatomy in robot-assisted laparoscopic partial nephrectomy

Michael A. Kokko, John D. Seigne, Douglas W. Van Citters, Ryan J. Halter

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 113151H (2020) https://doi.org/10.1117/12.2550605
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Although robotic instrumentation has revolutionized manipulation in oncologic laparoscopy, there remains a significant need for image guidance during the exposure portion of certain abdominal procedures. The high degree of mobility and potential for deformation associated with abdominal organs and related structures poses a significant challenge to implementing image-based navigation for the initial phase of robot-assisted laparoscopic partial nephrectomy (RALPN). This work introduces two key elements of a RALPN exposure simulation framework: a model for laparoscopic exposure and a compact representation of anatomical geometry suitable for integration into a statistical estimation framework. Data to drive the exposure simulation were collected during a clinical RALPN case in which the robotic endoscope was tracked in six dimensions. An initial rigid registration was performed between a preoperative CT scan and the frame of the optical tracker, allowing the endoscope trajectory to be replayed over tomography to simulate anatomical observations with realistic kinematics. CT data from five study subjects were combined with four publicly available datasets to produce a mean kidney shape. This template kidney was fit back to each of the input models by optimally tuning a set of eight parameters, achieving an average RMSE of 2.18mm. These developments represent important steps toward a full, clinically-relevant framework for simulating organ exposure and testing navigation algorithms. In future work, a particle filter estimation scheme will be integrated into the simulation to incrementally optimize correspondences between parametric anatomical models and simulated or reconstructed endoscopic observations.

Conference Presentation

Citation Download Citation

Michael A. Kokko, John D. Seigne, Douglas W. Van Citters, and Ryan J. Halter "Modeling the surgical exposure of anatomy in robot-assisted laparoscopic partial nephrectomy", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113151H (16 March 2020); https://doi.org/10.1117/12.2550605

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