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The access to the abdomen and the creation of a pneumoperitoneum is an initial and particularly critical step of minimally invasive laparoscopic procedures. Insertion instruments such as the Veress needle need to be introduced blindly into the abdominal cavity, which is associated with inadvertent visceral and vascular injuries. To ensure safe positioning of the instrument, information about the entry path advancement of the tip through the abdominal wall is needed. The main objective of this work is to demonstrate the capability to acquire information about intracorporeal tissuetool interactions of the Veress needle tip, utilizing acoustic emissions recorded at the extracorporeal end of the needle. In an experimental setup, a Veress needle was inserted in a multitissue- layer phantom with a defined insertion speed. Acoustic emissions were recorded with a MEMS microphone attached to the extracorporeal end of the needle. In addition, the counteraction forces during insertion of the needle were measured and a video of the experiment was recorded as reference. With this setup, an audio database of characteristical insertion events was generated. For the classification of characteristic audio events and detection of tissue-layer crossing, features were calculated in the time and frequency domain. Subsequently, a feature dimensionality reduction was performed. The distribution clustering of the audio database in the three-dimensional feature subspace allows a distinction between certain characteristic audio events. The preliminary results show the capability of this acoustic emission based method to detect events related to the insertion of a Veress needle, such as tissue-layer crossing.

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No abstract

Robotic surgeries are still limited with respect to the surgeon’s natural senses. The tactile sense is exceptional important in conventional clinical procedures. To identify critical structures inside the tissue, palpation is a commonly used technique in conventional open surgeries. The underlying organ or pathological structures conditions (healthy, abnormally hard or soft) can for example be localized and assessed through this process. Palpation needs a tactile sense; however, that is commonly not available or limited in robotic surgeries. The palpation need was already addressed by several research groups that integrated complex sensor-feedback-systems into prototype surgical instruments for robotic systems. We propose a new technique to acquire data of the tissue tool interaction of the surgical instruments. The structure borne transmission path is used to measure acoustic emission (AE) at the outpatient (proximal) end of the instruments with the help of different sensors attached to the surface of the surgical tool. Initial tests were performed using a microphone in combination with a stethoscope. This setup showed promising results and a more integrated prototype was subsequently designed. A piezoelectric charge accelerometer was used as vibration sensor and compared to a MEMS microphone. A signal acquisition system was developed to acquire signals from both sensors in parallel. The sensors were then attached onto the shaft of a daVinci Prograsp Forceps instrument. According to the surgery observation, a series of simulated experiments was conducted. The tip of the grasper was swiped manually over a human subject’s dorsal and palmar hand side, lateral side of neck and over the carotid artery. Additionally, contact with soft tissue and other instruments were evaluated since these are events of interest during surgery. Advanced signal processing techniques allowed the identification and characterization of significant events such as palpation dynamics, contact and pulsation. Signals acquired by the MEMS microphone showed the most promising results. This approach will now be used to build a prototype for further evaluation in a clinical setup. The paper presents the first results that show that this novel technique can provide valuable information about the tool-tissue interaction in robotic surgery that typically can only be obtained through advanced distal sensor systems or actual human touch.