Development of an Electrical Impedance Tomography Coupled Surgical Stapler for Tissue Characterization.

OBJECTIVE: This study explores the feasibility of coupling Electrical Impedance Tomography (EIT) to a standard-of-care laparoscopic surgical stapler, stapler+EIT, with the long-term goal of enabling intraoperative tissue differentiation for tumor margin detection. METHODS: Two custom printed-circuit-board-based electrode arrays with 60 and 8 electrodes, respectively, matching the stapler geometry, served as the jaws of an electrode-integrated surrogate stapler+EIT device. The device was evaluated through a series of simulations and bench-top imaging experiments of agar-gel phantoms and bovine tissue samples to evaluate the technique and determine optimal imaging parameters. RESULTS: Electrodes localized to only one jaw (the 60-electrode side) of the stapler outperformed a dual-jaw distribution of electrodes. Using this one-sided electrode array, reconstructions achieved an Area-Under-the-Curve (AUC) ≥ 0.94 for inclusions with conductivity contrasts of ≥30% for any size considered on measured agar-gel tests, and an AUC of 0.80 for bovine tissue samples. CONCLUSION: A stapler+EIT algorithm has been tuned and evaluated on challenging phantom tests and demonstrated to produce accurate reconstructions. SIGNIFICANCE: This work is an important step in the development of a surgical stapler+EIT technique for margin assessment.

Design of electrical impedance spectroscopy sensing surgical drill using computational modelling and experimental validation.

Electrical Impedance Spectroscopy (EIS) sensing surgical instruments could provide valuable and real-time feedback to surgeons about hidden tissue boundaries, therefore reducing the risk of iatrogenic injuries. In this paper, we present an EIS sensing surgical drill as an example instrument and introduce a strategy to optimize the mono-polar electrode geometry using a finite element method (FEM)-based computational model and experimental validation. An empirical contact impedance model and an adaptive mesh refinement protocol were developed to accurately preserve the behaviour of sensing electrodes as they approach high impedance boundaries. Specifically, experiments with drill-bit, cylinder, and conical geometries suggested a 15%-35% increase in resistance as the sensing electrode approached a high impedance boundary. Simulations achieved a maximum mean experiment-to-simulation mismatch of +1.7% for the drill-bit and +/-11% range for other electrode geometries. The simulations preserved the increase in resistance behaviour near the high impedance boundary. This highly accurate simulation framework allows us a mechanism for optimizing sensor geometry without costly experimental evaluation.

Bioimpedance Sensing Surgical Drill - In Vivo Porcine Model.

Surgical drilling to place dental implants in the mandible and maxilla is associated high risk of iatrogenic injuries to inferior alveolar nerve and maxillary sinus. Real-time tissue margin sensing at the drill-tip using electrical impedance spectroscopy (EIS) could reduce this risk by providing feedback to surgeons. Studies with saline analogues, ex-vivo tissues, in-situ tissues and computer models have been previously conducted to evaluate these impedance sensors. Understanding in-vivo electrical properties of tissues in the mandible and maxilla is critical to further develop the sensor and tissue margin sensing algorithms. In this paper, we propose an in-vivo animal model using pigs and discuss methods to test the sensor. Intra-operative imaging and optical tracking systems to assist in surgical navigation are described. The process of registering imaging and tracking information to localize impedance measurement sites within the anatomy are detailed. Results from one in-vivo case of drilling through the mandible are presented and discussed. Clinical Relevance- This model is crucial for characterizing in-vivo electrical properties of mandibular and maxillary tissues encountered during dental implant surgical drilling and for translating bioimpedance sensing drill technology to clinical space.

Bioimpedance Sensing Surgical Drill - Computational Modelling and Experimental Validation.

Surgical drilling to fixate dental implants is associated with high risk of injury to the inferior alveolar nerve (IAN) and the maxillary sinus. Current common practice is to use pre-operative radiographs to plan and drill with no real-time feedback of drill tip position with respect to these critical structures. Real-time proximity sensing of the IAN and maxillary sinus by measuring the electrical impedance properties of tissues, directly from the drill tip, while drilling may reduce and eventually eliminate this risk. Sensing impedance to detect tissue boundaries needs sensor geometry optimization for maximum detection distance. We have created a finite element method (FEM) based simulation platform that yields accurately impedances for different conductivities, frequencies and sensor geometries.