An Experimental and Numerical Study on Tactile Neuroimaging: A Novel Minimally Invasive Technique for Intraoperative Brain Imaging

This is the peer reviewed version of the following article: Moslem Sadeghi-Goughari, Yanjun Qian, Soo Jeon, Sohrab Sadeghi and Hyock-Ju Kwon, “An Experimental and Numerical Study on Tactile Neuroimaging: A Novel Minimally Invasive Technique for Intraoperative Brain Imaging,” accepted to The International Journal of Medical Robotics and Computer Assisted Surgery which has been published in final form at: https://doi.org/10.1002/rcs.1893. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Background The success of tumor neurosurgery is highly dependent on the ability to accurately localize the operative target, which may be shifted during the operation. Performing an intraoperative brain imaging is crucial in minimally invasive neurosurgery to detect the effect of brain shift on the tumor’s location, and to maximize the efficiency of tumor resection. Method The major objective of this research is to introduce the tactile neuroimaging as a novel minimally invasive technique for intraoperative brain imaging. To investigate the feasibility of the proposed method, an experimental and numerical study was first performed on silicone phantoms mimicking the brain tissue with a tumor. Then the study was extended to a clinical model with the meningioma tumor. Results The stress distribution on the brain surface has high potential to intraoperatively localize the tumor. Conclusion Results suggest that tactile neuroimaging can be used to provide a non-invasive, and real-time intraoperative data on tumor’s features.Natural Sciences and Engineering Research Council || RGPIN/2015-05273, RGPIN/2015-04118, RGPAS/354703-201

A FEEDBACK-BASED DYNAMIC INSTRUMENT FOR MEASURING THE MECHANICAL PROPERTIES OF SOFT TISSUES

In this paper, a novel feedback-based dynamic instrument integrated into a Minimally- Invasive-Surgery (MIS) tool to evaluate the mechanical impedance of soft tissues is presented. This instrument is capable of measuring viscoelasticity of tissues if specific boundary conditions are known. Some important advantages of the proposed instrument are that it is robust and simple in comparison to other similar instruments as it does not require magnitude information of plant’s displacement output and no force sensor is used. The precision and accuracy of the measurements of the proposed instrument for soft tissues is noticeably higher than similar instruments, which are not optimized to work with soft tissues. The proposed dynamic instrument is designed to detect the frequency shifts caused by contacting a soft tissue using an improved phase-locked loop feedback system (closed loop). These frequency shifts can then be used to evaluate the mechanical properties of the tissue. The closed-loop method works fast (with an approximate resonance-frequency-shift rate of 15 Hz per second), and is capable of measuring dy­ namic mechanical properties of viscoelastic tissues, while previous focus was mostly on static/quasi-static elastic modulus. The instrument is used to evaluate the equivalent stiffness of several springs and cantilever beams, mass of reference samples, and also the frequency shifts of several phantoms with injected tumors, noting that these frequency shifts can be used to measure the viscoelasticity of the tissues. It is also shown that the instrument can be used for tumor localization in these phantoms

Haptic assessment of tissue stiffness in locating and identifying gynaecological cancer in human tissue

Gynaecological surgeons are not able to gather adequate tissue feedback during minimal access surgery for cancer treatment. This can result in failure to locate tumour boundaries and to ensure these are completely resected within tumour-free resection margins. Surgeons achieve significantly better surgical and oncological outcomes if they can identify the precise location of a gynaecological tumour. Indeed, the true nature of tumour, whether benign or cancerous, is often not known prior to surgery. If more details were available in relation to the characteristics that differentiate gynaecological cancer in tumours, this would enable more accurate diagnosis and help in the planning of surgery. HYPOTHESIS: Haptic technology has the potential to enhance the surgeon’s degree of perception during minimal access surgery. Alteration in tissue stiffness in gynaecological tumours, thought to be associated with the accelerated multiplication of cancer cells, should allow their location to be identified and help in determining the likelihood of malignancy. METHOD: Setting: (i) Guy's & St Thomas' Hospital (ii) Dept of Informatics (King's College London).Permission from the National Research Ethics Committee and Research & Development (R&D) approval were sought from the National Health Service. The Phantom Omni, capable of 3D motion tracking, attached to a nano-17 force sensor, was used to capture real-time position data and force data. Uniaxial indentation palpation behaviour was used. The indentation depth was calculated using the displacement of the probe from the surface to the deepest point for each contact. The tissue stiffness (TS) was then calculated.The haptic probe was tested first on silicone models with embedded nodules mimicking tumour(s). This was followed by assessing TS ex-vivo using a haptic probe on fresh human gynaecological organs that had been removed in surgery. Tissue stiffness maps were generated in real time using the haptic device by converting stiffness values into RGB values. Surgeons also manually palpated and recorded the site of the tumour. Histology was used as the gold standard for location and cancer diagnosis. Manual palpation and haptic data were compared for accuracy on tumour location. The tissue stiffness calculated by the haptic probe was compared in cancer and control specimens. Several data analysis techniques were applied to derive results.CONTRIBUTIONS: Haptic indentation probe was tested for the first time on fresh human gynaecological organs to locate cancer in a clinical setting. We are the first one to evaluate the accuracy of cancer diagnosis in human gynaecological organs with a force sensing haptic indentation probe measuring tissue stiffness

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

Recursive Least Squares Filtering Algorithms for On-Line Viscoelastic Characterization of Biosamples

The mechanical characterization of biological samples is a fundamental issue in biology and related fields, such as tissue and cell mechanics, regenerative medicine and diagnosis of diseases. In this paper, a novel approach for the identification of the stiffness and damping coefficients of biosamples is introduced. According to the proposed method, a MEMS-based microgripper in operational condition is used as a measurement tool. The mechanical model describing the dynamics of the gripper-sample system considers the pseudo-rigid body model for the microgripper, and the Kelvin–Voigt constitutive law of viscoelasticity for the sample. Then, two algorithms based on recursive least square (RLS) methods are implemented for the estimation of the mechanical coefficients, that are the forgetting factor based RLS and the normalised gradient based RLS algorithms. Numerical simulations are performed to verify the effectiveness of the proposed approach. Results confirm the feasibility of the method that enables the ability to perform simultaneously two tasks: sample manipulation and parameters identification

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