Integrating Confidence Maps and Visual Servoing for Needle Tracking in Robotic US-Guided Percutaneous Nephrolithotomy
Ultrasound (US)-guided percutaneous nephrolithotomy (PCNL) is a minimally invasive procedure to remove large kidney stones through an incision in the patient’s back. PCNL requires a high level of dexterity to steer a surgical tool while visualizing it using US images. A robotic system tha...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Open Journal of Instrumentation and Measurement |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11045695/ |
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| Summary: | Ultrasound (US)-guided percutaneous nephrolithotomy (PCNL) is a minimally invasive procedure to remove large kidney stones through an incision in the patient’s back. PCNL requires a high level of dexterity to steer a surgical tool while visualizing it using US images. A robotic system that controls the US probe to automatically image the tool would alleviate the surgeon’s cognitive workload and potentially lead to more accurate kidney access. We propose a novel algorithm that combines visual servoing and confidence maps to track the position of a manually steered needle using a robotically actuated US probe. The algorithm automatically adjusts the position of the US probe so that the same longitudinal portion of the needle shaft is visible in the image, while simultaneously ensuring acoustic contact between the US probe and the tissue over uneven surfaces. Unlike previous methods, where confidence maps were used for probe positioning with static targets, this article introduces the first unified algorithm that optimizes image quality while tracking a moving tool. It ensures continuous probe–tissue contact on uneven surfaces and does not require prior knowledge of the needle’s trajectory or additional sensors. The algorithm, evaluated in phantom tissue and in a realistic kidney mannequin, shows an average tool tracking accuracy of 1.65 and 1.17 mm, respectively, confirming its ability to reliably track a manually inserted tool during PCNL. |
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| ISSN: | 2768-7236 |