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On Surgical Planning of Percutaneous Nephrolithotomy with Patient-Specific CTRs

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 (MICCAI 2022)

Abstract

Percutaneous nephrolithotomy (PCNL) is considered a first-choice minimally invasive procedure for treating kidney stones larger than 2 cm. It yields higher stone-free rates than other minimally invasive techniques and is employed when extracorporeal shock wave lithotripsy or uteroscopy are, for instance, infeasible. Using this technique, surgeons create a tract through which a scope is inserted for gaining access to the stones. Traditional PCNL tools, however, present limited maneuverability, may require multiple punctures and often lead to excessive torquing of the instruments which can damage the kidney parenchyma and thus increase the risk of hemorrhage. We approach this problem by proposing a nested optimization-driven scheme for determining a single tract surgical plan along which a patient-specific concentric-tube robot (CTR) is deployed so as to enhance manipulability along the most dominant directions of the stone presentations. The approach is illustrated with seven sets of clinical data from patients who underwent PCNL. The simulated results may set the stage for achieving higher stone-free rates through single tract PCNL interventions while decreasing blood loss.

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Acknowledgements

This work was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health through Grant Number R01DK119269 and by the Natural Sciences and Engineering Research Council (NSERC) of Canada grant RGPIN1345 (Discovery Grant), and the Canada Research Chairs Program.

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Correspondence to Filipe C. Pedrosa .

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Pedrosa, F.C. et al. (2022). On Surgical Planning of Percutaneous Nephrolithotomy with Patient-Specific CTRs. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13437. Springer, Cham. https://doi.org/10.1007/978-3-031-16449-1_60

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  • DOI: https://doi.org/10.1007/978-3-031-16449-1_60

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