Compensating the Influence of Tremors on Impedance Measurements Through Fourier Analysis.

Electrical mpedance measurements are a promising method for detecting structural changes in tissue and can be used in oncology to differentiate between healthy and tumorous tissue areas. The impedance measurements are so sensitive that they are not only affected by changes in the tissue itself, but also by a fluctuating contact force between sensor and tissue. In this work, the correlation between impedance measurements and movements during the measuring process, such as physiological tremors, are analyzed. To do this, impedance measurements are taken on pig bladders and the sensor-tissue contact force is simultaneously recorded. The tremor frequencies are directly visible in the Fourier transform of the impedance measurement. To counteract these effects, a Butterworth filter is used to filter out tremor frequencies and remove unwanted artefacts. Additionally, placing an spring on top of the impedance sensor helped to achieve a steadier contact force between sensor and tissue to also remove low frequency disturbances in the impedance measurements.Clinical relevance- This approach can help to obtain more reliable impedance measurements on tissue both for ex vivo and in vivo applications.

An Enhanced Synthetic Cystoscopic Environment for Use in Monocular Depth Estimation.

As technology advances and sensing devices improve, it is becoming more and more pertinent to ensure accurate positioning of these devices, especially within the human body. This task remains particularly difficult during manual, minimally invasive surgeries such as cystoscopies where only a monocular, endoscopic camera image is available and driven by hand. Tracking relies on optical localization methods, however, existing classical options do not function well in such a dynamic, non-rigid environment. This work builds on recent works using neural networks to learn a supervised depth estimation from synthetically generated images and, in a second training step, use adversarial training to then apply the network on real images. The improvements made to a synthetic cystoscopic environment are done in such a way to reduce the domain gap between the synthetic images and the real ones. Training with the proposed enhanced environment shows distinct improvements over previously published work when applied to real test images.

In-plane Strain Analysis by Correlating Geometry and Visual Data Through a Gradient-Based Surface Reconstruction.

Abnormalities in tissue can be detected and analyzed by evaluating mechanical properties, such as strain and stiffness. While current sensor systems are effective in measuring longitudinal properties perpendicular to the measurement sensor, identifying in-plane deformation remains a significant challenge. To address this issue, this paper presents a novel method for reconstructing in-plane deformation of observed tissue surfaces using a fringe projection sensor specifically designed for measuring tissue deformations. The method employs the latest techniques from computer vision, such as differentiable rendering, to formulate the in-plane reconstruction as a differentiable optimization problem. This enables the use of gradient-based solvers for an efficient and effective optimization of the problem optimum. Depth information and image information are combined using landmark correspondences between the respective image observations of the undeformed and deformed scenes. By comparing the reconstructed pre- and post-deformation geometry, the in-plane deformation can be revealed through the analysis of relative variations between the corresponding models' geometries. The proposed reconstruction pipeline is validated on an experimental setup, and the potential for intraoperative applications is discussed.

Cystoscopic depth estimation using gated adversarial domain adaptation.

Monocular depth estimation from camera images is very important for surrounding scene evaluation in many technical fields from automotive to medicine. However, traditional triangulation methods using stereo cameras or multiple views with the assumption of a rigid environment are not applicable for endoscopic domains. Particularly in cystoscopies it is not possible to produce ground truth depth information to directly train machine learning algorithms for using a monocular image directly for depth prediction. This work considers first creating a synthetic cystoscopic environment for initial encoding of depth information from synthetically rendered images. Next, the task of predicting pixel-wise depth values for real images is constrained to a domain adaption between the synthetic and real image domains. This adaptation is done through added gated residual blocks in order to simplify the network task and maintain training stability during adversarial training. Training is done on an internally collected cystoscopy dataset from human patients. The results after training demonstrate the ability to predict reasonable depth estimations from actual cystoscopic videos and added stability from using gated residual blocks is shown to prevent mode collapse during adversarial training.

Enhancing Tissue Impedance Measurements Through Modeling of Fluid Flow During Viscoelastic Relaxation.

OBJECTIVE: Bladder cancer recurrence is an important issue after endoscopic urological surgeries. Additional sensor information such as electrical impedance measurements aim to support surgeons to ensure that the entirety of the tumor is removed. The foundation for differentiating lies in the altered sodium contents and cell structures within tumors that change their conductivity and permittivity. Mechanical deformations in the tissue expel fluid from the compressed area and pose a great difficulty, as they also lead to impedance changes. It is crucial to determine if this effect outweighs the alterations due to the tumorous tissue properties. METHODS: Impedance measurements under ongoing viscoelastic relaxation are taken on healthy and tumorous tissue samples from human bladders and breasts. A fluid model to account for extra- and intracellular fluid flow under compression is derived. It is based on the fluid content within the individual tissue compartments and their outflow via diffusion. RESULTS: After an initial deformation, the tissue relaxes and the impedance increases. The proposed model accurately represents these effects and validates the link between fluid flow under mechanical deformation and its impact on tissue impedance. A method to compensate for these undesired effects of fluid flow is proposed and the measurements are assessed in terms of differentiability between tumorous and healthy tissue samples. CONCLUSION: The electrical parameters are found to be promising for differentiation even under varying mechanical deformation, and the distinction is additionally improved by the proposed compensation approach. SIGNIFICANCE: Electrical impedance measurements show great potential to support urologist during endoscopic surgeries.

Geometric Mapping Evaluation for Real-Time Local Sensor Simulation.

Medical augmented reality and simulated test environments struggle in accurately simulating local sensor measurements across large spatial domains while maintaining the proper resolution of information required and real time capability. Here, a simple method for real-time simulation of intraoperative sensors is presented to aid with medical sensor development and professional training. During a surgical intervention, the interaction between medical sensor systems and tissue leads to mechanical deformation of the tissue. Through the inclusion of detailed finite element simulations in a real-time augmented reality system the method presented will allow for more accurate simulation of intraoperative sensor measurements that are independent of the mechanical state of the tissue. This concept uses a coarse, macro-level deformation mesh to maintain both computational speed and the illusion of reality and a simple geometric point mapping method to include detailed fine mesh information. The resulting system allows for flexible simulation of different types of localized sensor measurement techniques. Preliminary simulation results are provided using a real-time capable simulation environment and prove the feasibility of the method.

Detection Limits of Tetrapolar Impedance Sensors for Tissue Differentiation.

Cancer recurrence is an important issue in bladder tumor resections, because tissue cannot generously be removed from the thin bladder wall without impacting its functionality. Electrical impedance measurements during an operation aim to support the surgeon in making the decision which tissue areas to preserve, because physiological changes in tissue due to cancerous mutations can be detected by their altered electrical characteristics. This work investigates the detection limits of tetrapolar sensors when the impedance of heterogeneous tissue is measured. To do this, a finite element analysis is carried out where the sensors are placed on a dielectric medium with inclusions of different sizes, conductivity, and locations relative to the sensor. It is shown that a sensor with four electrodes in a square performs poorly in comparison to a sensor where the electrodes are symmetrically shaped as rings around one center electrode. This is mainly due to its enlarged regions of negative sensitivity. Based on the results, a third, optimized sensor geometry is proposed that shows superior performance to the other sensors in terms of geometry factor, sensitivities, and tumor detection. In simulation, it can reliably detect tumors with only half the radius of the sensor surface. Smaller tumor fractions cannot be detected by either sensor.

Geometry Factor Determination for Tetrapolar Impedance Sensor Probes.

Even after successful tumor resection, cancer recurrence remains an important issue for bladder tumors. Intra-operative tissue differentiation can help for diagnostic purposes as well as for ensuring that all cancerous cells are completely removed, therefore, decreasing the risk of recurrence. It has been shown that the electrical properties of tumors differ from healthy tissue due to an altered physiology. This work investigates three sensor configurations to measure the impedance of tissue. Each relies on a four terminal measurement and has a distinct electrode arrangement either inline or as a square. Analytical expressions to calculate the geometry factor of each sensor based on Laplace's equation are derived. The results are verified experimentally and in a finite element simulation. Furthermore, several measurements on pig bladders, both fresh and from frozen storage, are carried out with each sensor.It is shown that the calculated and simulated geometry factors yield the same results and are suitable and uncomplicated methods to determine the geometry factor without an experimental setup. These methods also allow for sensor optimization by knowing the measured potentials before the actual fabrication of the sensor. Moreover, conductivity values close to listed data are obtained for pig bladders, which validates the sensors. Ultimately, the square electrode configuration turns out to be a valid option for minimally invasive sensors, which are necessary for the envisaged application of transurethral bladder cancer diagnostics and surgery. This arrangement both assures reliable data and allows for easier miniaturization than the inline electrode placement.

2D to 3D Segmentation: Inclusion of Prior Information using Random Walk Kalman Filters.

Augmented reality is a quickly advancing field that has the potential to provide surgeons with computer generated diagnostic results during surgery. Visual classification of diseased tissue generated during a diagnostic procedure, for example, trans-urethral cystoscopy of the urinary bladder, can aid a surgeon during the following resection to ensure no tissue is inadvertently missed. Work with 2D segmentation of camera images is well developed and frameworks already exist to fuse this data real-time in a 3D reconstruction. These existing frame-works, however, maintain only the most recent segmentation information when building the 3D reconstruction. This work proposes a method to build a 3D point cloud classification using random walk Kalman filters. The method enables retention of prior classification information and additionally provides a framework to include additional sensor classifications contributing to a single, final 3D segmentation result. The method is demonstrated using a simulated environment intended to emulate the inside of a human bladder.

Multi-Physical Tissue Modeling of a Human Urinary Bladder.

A multi-physical model of a human urinary bladder is an essential element for the potential application of electrical impedance spectroscopy during transurethral resection surgery, where measurements are taken at different fill levels inside the bladder. This work derives a multi-physical bladder tissue model that incorporates the electrical impedance properties with dependence on mechanical deformation due to filling of the bladder. The volume and ratio of the intracellular to extracellular tissue fluid heavily influence the electrical impedance characteristics and thus provide the connection between the mechanical and electrical domains. Modeling the fluid within the tissue links both the physical and histological processes and enables useful inferences of the properties from empiric observations. This is demonstrated by taking impedance measurements at different fill volumes. The resulting model provides a tool to analyze impedance measurements during surgery at different stress levels. In addition, this model can be used to determine patient-specific tissue parameters.