Wireless Tissue Palpation for Intraoperative Detection of Lumps in the Soft Tissue

In an open surgery, identification of precise margins for curative tissue resection is performed by manual palpation. This is not the case for minimally invasive and robotic procedures, where tactile feedback is either distorted or not available. In this paper, we introduce the concept of intraoperative wireless tissue palpation. The wireless palpation probe (WPP) is a cylindrical device (15 mm in diameter, 60 mm in length) that can be deployed through a trocar incision and directly controlled by the surgeon to create a volumetric stiffness distribution map of the region of interest. This map can then be used to guide the tissue resection to minimize healthy tissue loss. The wireless operation prevents the need for a dedicated port and reduces the chance of instrument clashing in the operating field. The WPP is able to measure in real time the indentation pressure with a sensitivity of 34 Pa, the indentation depth with an accuracy of 0.68 mm, and the probe position with a maximum error of 11.3 mm in a tridimensional workspace. The WPP was assessed on the benchtop in detecting the local stiffness of two different silicone tissue simulators (elastic modulus ranging from 45 to 220 kPa), showing a maximum relative error below 5%. Then, in vivo trials were aimed to identify an agar-gel lump injected into a porcine liver and to assess the device usability within the frame of a laparoscopic procedure. The stiffness map created intraoperatively by the WPP was compared with a map generated ex vivo by a standard uniaxial material tester, showing less than 8% local stiffness error at the site of the lump

Wireless tissue palpation: Head characterization to improve tumor detection in soft tissue

For surgeons performing open procedures, the sense of touch is a valuable tool to directly access buried structures and organs, to identify their margins, detect tumors, and prevent undesired cuts. Minimally invasive surgical procedures provide great benefits for patients; however, they hinder the surgeon's ability to directly manipulate the tissue. In our previous work, we developed a Wireless Palpation Probe (WPP) to restore tissue palpation in Minimally Invasive Surgery (MIS) by creating a real-time stiffness distribution map of the target tissue. The WPP takes advantage of a field-based magnetic localization algorithm to measure its position, orientation, and tissue indentation depth, in addition to a barometric sensor measuring indentation tissue pressure. However, deformations of both the tissue and the silicone material used to cover the pressure sensors introduce detrimental nonlinearities in sensor measurements. In this work, we calibrated and characterized different diameter WPP heads with a new design allowing exchangeability and disposability of the probe head. Benchtop trials showed that this method can effectively reduce error in sensor pressure measurements up to 5% with respect to the reference sensor. Furthermore, we studied the effect of the head diameter on the device's spatial resolution in detecting tumor simulators embedded into silicone phantoms. Overall, the results showed a tumor detection rate over 90%, independent of the head diameter, when an indentation depth of 5 mm is applied on the tissue simulator

Endoscopic Tactile Capsule for Non-Polypoid Colorectal Tumour Detection

An endoscopic tactile robotic capsule, embedding miniaturized MEMS force sensors, is presented. The capsule is conceived to provide automatic palpation of non-polypoid colorectal tumours during colonoscopy, since it is characterized by high degree of dysplasia, higher invasiveness and lower detection rates with respect to polyps. A first test was performed employing a silicone phantom that embedded inclusions with variable hardness and curvature. A hardness-based classification was implemented, demonstrating detection robustness to curvature variation. By comparing a set of supervised classification algorithms, a weighted 3-nearest neighbor classifier was selected. A bias force normalization model was introduced in order to make different acquisition sets consistent. Parameters of this model were chosen through a particle swarm optimization method. Additionally, an ex-vivo test was performed to assess the capsule detection performance when magnetically-driven along a colonic tissue. Lumps were identified as voltage peaks with a prominence depending on the total magnetic force applied to the capsule. Accuracy of 94 % in hardness classification was achieved, while a 100 % accuracy is obtained for the lump detection within a tolerance of 5 mm from the central path described by the capsule. In real application scenario, we foresee our device aiding physicians to detect tumorous tissues

ARTICLE IN PRESS G Model

a b s t r a c t For surgeons performing open procedures, the sense of touch is a valuable tool to directly access buried structures and organs, to identify their margins, detect tumors, and prevent undesired cuts. Minimally invasive surgical procedures provide great benefits for patients; however, they hinder the surgeon's ability to directly manipulate the tissue. In our previous work, we developed a Wireless Palpation Probe (WPP) to restore tissue palpation in Minimally Invasive Surgery (MIS) by creating a real-time stiffness distribution map of the target tissue. The WPP takes advantage of a field-based magnetic localization algorithm to measure its position, orientation, and tissue indentation depth, in addition to a barometric sensor measuring indentation tissue pressure. However, deformations of both the tissue and the silicone material used to cover the pressure sensors introduce detrimental nonlinearities in sensor measurements. In this work, we calibrated and characterized different diameter WPP heads with a new design allowing exchangeability and disposability of the probe head. Benchtop trials showed that this method can effectively reduce error in sensor pressure measurements up to 5% with respect to the reference sensor. Furthermore, we studied the effect of the head diameter on the device's spatial resolution in detecting tumor simulators embedded into silicone phantoms. Overall, the results showed a tumor detection rate over 90%, independent of the head diameter, when an indentation depth of 5 mm is applied on the tissue simulator

Wireless Tissue Palpation: head characterization to improve tumor detection in soft tissue

Abstract For surgeons performing open procedures, the sense of touch is a valuable tool to directly access buried structures and organs, to identify their margins, detect tumors, and prevent undesired cuts. Minimally invasive surgical procedures provide great benefits for patients; however, they hinder the surgeon's ability to directly manipulate the tissue. In our previous work, we developed a Wireless Palpation Probe (WPP) to restore tissue palpation in Minimally Invasive Surgery (MIS) by creating a real-time stiffness distribution map of the target tissue. The WPP takes advantage of a field-based magnetic localization algorithm to measure its position, orientation, and tissue indentation depth, in addition to a barometric sensor measuring indentation tissue pressure. However, deformations of both the tissue and the silicone material used to cover the pressure sensors introduce detrimental nonlinearities in sensor measurements. In this work, we calibrated and characterized different diameter WPP heads with a new design allowing exchangeability and disposability of the probe head. Benchtop trials showed that this method can effectively reduce error in sensor pressure measurements up to 5 % with respect to the reference sensor. Furthermore, we studied the effect of the head diameter on the devices spatial resolution in detecting tumor simulators embedded into silicone phantoms. Overall, the results showed a tumor detection rate over 90 %, independent of the head diameter, when an indentation depth of at 5 mm is applied on the tissue simulator

Robotic simulators for tissue examination training with multimodal sensory feedback

Tissue examination by hand remains an essential technique in clinical practice. The effective application depends on skills in sensorimotor coordination, mainly involving haptic, visual, and auditory feedback. The skills clinicians have to learn can be as subtle as regulating finger pressure with breathing, choosing palpation action, monitoring involuntary facial and vocal expressions in response to palpation, and using pain expressions both as a source of information and as a constraint on physical examination. Patient simulators can provide a safe learning platform to novice physicians before trying real patients. This paper reviews state-of-the-art medical simulators for the training for the first time with a consideration of providing multimodal feedback to learn as many manual examination techniques as possible. The study summarizes current advances in tissue examination training devices simulating different medical conditions and providing different types of feedback modalities. Opportunities with the development of pain expression, tissue modeling, actuation, and sensing are also analyzed to support the future design of effective tissue examination simulators

A Framework for Tumor Localization in Robot-Assisted Minimally Invasive Surgery

Manual palpation of tissue is frequently used in open surgery, e.g., for localization of tumors and buried vessels and for tissue characterization. The overall objective of this work is to explore how tissue palpation can be performed in Robot-Assisted Minimally Invasive Surgery (RAMIS) using laparoscopic instruments conventionally used in RAMIS. This thesis presents a framework where a surgical tool is moved teleoperatively in a manner analogous to the repetitive pressing motion of a finger during manual palpation. We interpret the changes in parameters due to this motion such as the applied force and the resulting indentation depth to accurately determine the variation in tissue stiffness. This approach requires the sensorization of the laparoscopic tool for force sensing. In our work, we have used a da Vinci needle driver which has been sensorized in our lab at CSTAR for force sensing using Fiber Bragg Grating (FBG). A computer vision algorithm has been developed for 3D surgical tool-tip tracking using the da Vinci \u27s stereo endoscope. This enables us to measure changes in surface indentation resulting from pressing the needle driver on the tissue. The proposed palpation framework is based on the hypothesis that the indentation depth is inversely proportional to the tissue stiffness when a constant pressing force is applied. This was validated in a telemanipulated setup using the da Vinci surgical system with a phantom in which artificial tumors were embedded to represent areas of different stiffnesses. The region with high stiffness representing tumor and region with low stiffness representing healthy tissue showed an average indentation depth change of 5.19 mm and 10.09 mm respectively while maintaining a maximum force of 8N during robot-assisted palpation. These indentation depth variations were then distinguished using the k-means clustering algorithm to classify groups of low and high stiffnesses. The results were presented in a colour-coded map. The unique feature of this framework is its use of a conventional laparoscopic tool and minimal re-design of the existing da Vinci surgical setup. Additional work includes a vision-based algorithm for tracking the motion of the tissue surface such as that of the lung resulting from respiratory and cardiac motion. The extracted motion information was analyzed to characterize the lung tissue stiffness based on the lateral strain variations as the surface inflates and deflates