The advancement of intelligent surgery has imposed greater requirements on the precision and real-time performance of pulmonary minimally invasive surgical navigation. However, existing intraoperative navigation techniques, including optical tracking, X-ray imaging, and magnetic resonance imaging (MRI), have inherent limitations such as inadequate real-time performance, complicated workflows, strong equipment dependency, and restricted visual fields. These constraints hinder the ability of interventional surgeries to provide continuous and stable three-dimensional coordinate feedback in deep, non-line-of-sight environments. Therefore, this study proposes an electric current field source reconstruction method for determining the terminal coordinates of surgical actuators. An electric current is injected from the tip of the surgical instrument, creating an electric field within the human tissue. The potential measured by surface electrodes are then used to reconstruct the current source coordinates, enabling real-time and active sensing of the surgical probe coordinates. A mathematical model for electric current field-based coordinate positioning was developed, involving analyses of the forward and inverse problems as well as coordinate reconstruction. Random single-point positioning simulations were conducted, and a 16 + 1-electrodes experimental platform was constructed for coordinates navigation tests to evaluate positioning and navigation performance. In addition, dynamic positioning experiments of multiple physiological tissues were carried out to assess the robustness and anti-interference capability of the proposed method. Experimental results indicate that the positioning error remains within 2 mm under single-point, linear, and curved trajectory conditions, satisfying the precision requirements for intraoperative navigation. This method significantly improves the accuracy and safety of surgical positioning and navigation, thereby holding substantial engineering significance and clinical value for the advancement of intelligent surgical systems.
Zhang et al. (Thu,) studied this question.
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