Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡.

While modern low-power microcontrollers are a cornerstone of wearable physiological sensors, their limited on-chip storage typically makes peripheral storage devices a requirement for long-term physiological sensing-significantly increasing both size and power consumption. Here, a wearable biosensor system capable of long-term recording of physiological signals using a single, 64 kB microcontroller to minimize sensor size and improve energy performance is described. Electrodermal (EDA) signals were sampled and compressed using a multiresolution wavelet transformation to achieve long-term storage within the limited memory of a 16-bit microcontroller. The distortion of the compressed signal and errors in extracting common EDA features is evaluated across 253 independent EDA signals acquired from human volunteers. At a compression ratio (CR) of 23.3×, the root mean square error (RMSErr) is below 0.016 µ S and the percent root-mean-square difference (PRD) is below 1%. Tonic EDA features are preserved at a CR = 23.3× while phasic EDA features are more prone to reconstruction errors at CRs > 8.8×. This compression method is shown to be competitive with other compressive sensing-based approaches for EDA measurement while enabling on-board access to raw EDA data and efficient signal reconstructions. The system and compression method provided improves the functionality of low-resource microcontrollers by limiting the need for external memory devices and wireless connectivity to advance the miniaturization of wearable biosensors for mobile applications.

An Imaging-Compatible Oral Retractor System for Transoral Robotic Surgery.

This study aimed to develop and validate a Computed Tomography (CT)/Magnetic Resonance Imaging (MRI)-compatible polymer oral retractor system to enable intraoperative image guidance for transoral robotic surgery (TORS). The retractor was designed based on standard-of-care metallic retractors and 3D (three-dimensional) printed with carbon fiber composite and nylon. The system was comprehensively evaluated in bench-top and cadaveric experiments in terms of its ability to enable intraoperative CT/MR images during TORS, functionality including surgical exposure and working volume, usability, compatibility with da Vinci surgical systems, feasibility for disinfection or sterilization, and robustness over an extended period of time. The polymer retractor system enabled the acquisition of high-resolution and artifact-free intraoperative CT/MR images during TORS. With an inter-incisive distance of 42.55 mm and a working volume of 200.09 cm3, it provided surgical exposure comparable to standard-of-care metallic retractors. The system proved intuitive and compatible with da Vinci S, Xi, and Single Port systems, enabling successful mock surgical tasks performed by surgeons and residents. The retractor components could be effectively disinfected or sterilized for clinical use without significant compromise in material strength, with STERRAD considered the optimal method. Throughout a 2 h mock procedure, the retractor system showed minimal displacements (<1.5 mm) due to surrounding tissue deformation, with insignificant device deformation. The 3D-printed polymer retractor system successfully enabled artifact-free intraoperative CT/MR imaging in TORS for the first time and demonstrated feasibility for clinical use. This breakthrough opens the door to surgical navigation with intraoperative image guidance in TORS, offering the potential to significantly improve surgical outcomes and patients' quality of life.

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.

Prediction of Occult Hemorrhage in the Lower Body Negative Pressure Model: Initial Validation of Machine Learning Approaches.

INTRODUCTION: Detection of occult hemorrhage (OH) before progression to clinically apparent changes in vital signs remains an important clinical problem in managing trauma patients. The resource-intensiveness associated with continuous clinical patient monitoring and rescue from frank shock makes accurate early detection and prediction with noninvasive measurement technology a desirable innovation. Despite significant efforts directed toward the development of innovative noninvasive diagnostics, the implementation and performance of the newest bedside technologies remain inadequate. This poor performance may reflect the limitations of univariate systems based on one sensor in one anatomic location. It is possible that when signals are measured with multiple modalities in multiple locations, the resulting multivariate anatomic and temporal patterns of measured signals may provide additional discriminative power over single technology univariate measurements. We evaluated the potential superiority of multivariate methods over univariate methods. Additionally, we utilized machine learning-based models to compare the performance of noninvasive-only to noninvasive-plus-invasive measurements in predicting the onset of OH. MATERIALS AND METHODS: We applied machine learning methods to preexisting datasets derived using the lower body negative pressure human model of simulated hemorrhage. Employing multivariate measured physiological signals, we investigated the extent to which machine learning methods can effectively predict the onset of OH. In particular, we applied 2 ensemble learning methods, namely, random forest and gradient boosting. RESULTS: Analysis of precision, recall, and area under the receiver operating characteristic curve showed a superior performance of multivariate approach to that of the univariate ones. In addition, when using both invasive and noninvasive features, random forest classifier had a recall 95% confidence interval (CI) of 0.81 to 0.86 with a precision 95% CI of 0.65 to 0.72. Interestingly, when only noninvasive features were employed, the results worsened only slightly to a recall 95% CI of 0.80 to 0.85 and a precision 95% CI of 0.61 to 0.73. CONCLUSIONS: Multivariate ensemble machine learning-based approaches for the prediction of hemodynamic instability appear to hold promise for the development of effective solutions. In the lower body negative pressure multivariate hemorrhage model, predictions based only on noninvasive measurements performed comparably to those using both invasive and noninvasive measurements.

A Clinically Feasible Electrode Array for 3D Intraoperative Electrical Impedance Tomography-Based Surgical Margin Assessment in Robot-Assisted Radical Prostatectomy.

OBJECTIVE: A novel small form factor circular electrode array was designed specifically for electrical impedance tomography (EIT) based assessment of surgical margins during robot assisted radical prostatectomy (RARP). METHODS: The electrode array consists of 33 gold-plated electrodes arranged within a 9.5 mm diameter circular footprint on the end of a surgical probe that can be introduced through a standard 12 mm laparoscopic port used during RARP. The electrode array contains 8 larger, low-contact impedance outer electrodes dedicated for current drive and an internal grid of 25 smaller electrodes for simultaneous voltage measurement. Separating electrode geometry by function is designed to improve current delivery, speed, and resolution while reducing hardware requirements. RESULTS: Simulations demonstrated that 1 mm diameter hemispherical prostate cancer inclusions could be localized within regions of adipose and benign prostate tissue; 1.5 mm diameter inclusions were required for localization within muscle tissue. A 2.38 mm diameter aluminum rod in 0.2 S/m saline could be localized throughout the imaging domain with a position error of less than 2.5 mm for depths from the electrode array surface of up to 1.7 mm. Ex vivo tissue experiments with a bovine model demonstrate visual congruence of muscle and adipose tissue locations between the sample and reconstructed images. CONCLUSION: Simulation and experimental results indicate good detection and location of inclusions. SIGNIFICANCE: These results suggest the proposed electrode array design can provide sufficient accuracy in the detection and localization of prostate cancer against clinically relevant background tissues for use during RARP.

Independent effects of transcranial direct current stimulation and social influence on pain.

ABSTRACT: Transcranial direct current stimulation (tDCS) is a noninvasive neuromodulatory technique with the potential to provide pain relief. However, tDCS effects on pain are variable across existing studies, possibly related to differences in stimulation protocols and expectancy effects. We investigated the independent and joint effects of contralateral motor cortex tDCS (anodal vs cathodal) and socially induced expectations (analgesia vs hyperalgesia) about tDCS on thermal pain. We employed a double-blind, randomized 2 × 2 factorial cross-over design, with 5 sessions per participant on separate days. After calibration in Session 1, Sessions 2 to 5 crossed anodal or cathodal tDCS (20 minutes 2 mA) with socially induced analgesic or hyperalgesic expectations, with 6 to 7 days between the sessions. The social manipulation involved videos of previous "participants" (confederates) describing tDCS as inducing a low-pain state ("analgesic expectancy") or hypersensitivity to sensation ("hyperalgesic expectancy"). Anodal tDCS reduced pain compared with cathodal stimulation (F(1,19.9) = 19.53, P < 0.001, Cohen d = 0.86) and analgesic expectancy reduced pain compared with hyperalgesic expectancy (F(1,19.8) = 5.62, P = 0.027, Cohen d = 0.56). There was no significant interaction between tDCS and social expectations. Effects of social suggestions were related to expectations, whereas tDCS effects were unrelated to expectancies. The observed additive effects provide novel evidence that tDCS and socially induced expectations operate through independent processes. They extend clinical tDCS studies by showing tDCS effects on controlled nociceptive pain independent of expectancy effects. In addition, they show that social suggestions about neurostimulation effects can elicit potent placebo effects.

Rapid patient-specific FEM meshes from 3D smart-phone based scans.

Objective.The objective of this study was to describe and evaluate a smart-phone based method to rapidly generate subject-specific finite element method (FEM) meshes. More accurate FEM meshes should lead to more accurate thoracic electrical impedance tomography (EIT) images.Approach.The method was evaluated on an iPhone®that utilized an app called Heges, to obtain 3D scans (colored, surface triangulations), a custom belt, and custom open-source software developed to produce the subject-specific meshes. The approach was quantitatively validated via mannequin and volunteer tests using an infrared tracker as the gold standard, and qualitatively assessed in a series of tidal-breathing EIT images recorded from 9 subjects.Main results.The subject-specific meshes can be generated in as little as 6.3 min, which requires on average 3.4 min of user interaction. The mannequin tests yielded high levels of precision and accuracy at 3.2 ± 0.4 mm and 4.0 ± 0.3 mm root mean square error (RMSE), respectively. Errors on volunteers were only slightly larger (5.2 ± 2.1 mm RMSE precision and 7.7 ± 2.9 mm RMSE accuracy), illustrating the practical RMSE of the method.Significance.Easy-to-generate, subject-specific meshes could be utilized in the thoracic EIT community, potentially reducing geometric-based artifacts and improving the clinical utility of EIT.

Estimating tongue deformation during laryngoscopy using a hybrid FEM-multibody model and intraoperative tracking - a cadaver study.

Throat tumour margin control remains difficult due to the tight, enclosed space of the oral and throat regions and the tissue deformation resulting from placement of retractors and scopes during surgery. Intraoperative imaging can help with better localization but is hindered by non-image-compatible surgical instruments, cost, and unavailability. We propose a novel method of using instrument tracking and FEM-multibody modelling to simulate soft tissue deformation in the intraoperative setting, without requiring intraoperative imaging, to improve surgical guidance accuracy. We report our first empirical study, based on four trials of a cadaveric head specimen with full neck anatomy, yields a mean TLE of 10.8 ± 5.5 mm, demonstrating methodological feasibility.

Assessing pulmonary function in ALS using electrical impedance tomography.

OBJECTIVE: We sought to determine whether thoracic electrical impedance tomography (EIT) could characterize pulmonary function in amyotrophic lateral sclerosis (ALS) patients, including those with facial weakness. Thoracic EIT is a noninvasive, technology in which a multi-electrode belt is placed across the chest, producing real-time impedance imaging of the chest during breathing. METHODS: We enrolled 32 ALS patients and 32 age- and sex-matched healthy controls (HCs) without underlying lung disease. All participants had EIT measurements performed simultaneously with standard pulmonary function tests (PFTs), including slow and forced vital capacity (SVC and FVC) in upright and supine positions and maximal inspiratory and expiratory pressures (MIPs and MEPs, respectively). Intraclass correlation coefficients (ICCs) were calculated to assess the immediate reproducibility of EIT measurements and Pearson's correlations were used to explore the relationships between EIT and PFT values. RESULTS: Data from 30 ALS patients and 27 HCs were analyzed. Immediate upright SVC reproducibility was very high (ICC 0.98). Correlations were generally strongest between EIT and spirometry measures, with R values ranging from 0.64 to 0.82 (p < 0.001) in the ALS cohort. There were less robust correlations between EIT values and both MIPs and MEPs in the ALS patients, with R values ranging from 0.33 to 0.44. There was no significant difference for patients with and without facial weakness. There were no reported adverse events. CONCLUSION: EIT-based pulmonary measures hold the promise of providing an alternative approach for lung function assessment in ALS patients. Based on these early results, further development and study of this technology are warranted.

Onward to Better Surgery - the Critical Need for Improved Ex Vivo Testing and Training Methods.

Guided surgery has demonstrated significant improvements in patient outcomes in some disease processes. Interest in this field has led to substantial growth in the technologies under investigation. Most likely no single technology will prove to be "best," and combinations of macro- and microscale guidance-using radiological imaging navigation, probes (activatable, perfusion, and molecular-targeted; large- and small-molecule), autofluorescence, tissue intrinsic optical properties, bioimpedance, and other characteristics-will offer patients and surgeons the greatest opportunity for high-success/low-morbidity medical interventions. Problems are arising, however, from the lack of valid testing formats; surgical training simulators suffer the same problems. Small animal models do not accurately recreate human anatomy, especially in terms of tissue volume. Large animal models are expensive and have difficulty replicating many pathological states, particularly when molecular specificity for individual cancers is required. Furthermore, the sheer number of technologies and the potential for synergistic combination leads to exponential growth of testing requirements that is unrealistic for in vivo testing. Therefore, critical need exists to expand the ex vivo/in vitro testing platforms available to investigators and, once validated, a need to increase the acceptance of these methods for funding and regulatory endpoints. Herein is a review of the available ex vivo/in vitro testing formats for guided surgery, a review of their advantages/disadvantages, and consideration for how our field may safely and more swiftly move forward through stronger adoption of these testing and validation methods.

Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability in a lower body negative pressure model.

Occult hemorrhages after trauma can be present insidiously, and if not detected early enough can result in patient death. This study evaluated a hemorrhage model on 18 human subjects, comparing the performance of traditional vital signs to multiple off-the-shelf non-invasive biomarkers. A validated lower body negative pressure (LBNP) model was used to induce progression towards hypovolemic cardiovascular instability. Traditional vital signs included mean arterial pressure (MAP), electrocardiography (ECG), plethysmography (Pleth), and the test systems utilized electrical impedance via commercial electrical impedance tomography (EIT) and multifrequency electrical impedance spectroscopy (EIS) devices. Absolute and relative metrics were used to evaluate the performance in addition to machine learning-based modeling. Relative EIT-based metrics measured on the thorax outperformed vital sign metrics (MAP, ECG, and Pleth) achieving an area-under-the-curve (AUC) of 0.99 (CI 0.95-1.00, 100% sensitivity, 87.5% specificity) at the smallest LBNP change (0-15 mmHg). The best vital sign metric (MAP) at this LBNP change yielded an AUC of 0.6 (CI 0.38-0.79, 100% sensitivity, 25% specificity). Out-of-sample predictive performance from machine learning models were strong, especially when combining signals from multiple technologies simultaneously. EIT, alone or in machine learning-based combination, appears promising as a technology for early detection of progression toward hemodynamic instability.

Intracranial electrical impedance tomography: a method of continuous monitoring in an animal model of head trauma.

BACKGROUND: Electrical impedance tomography (EIT) is a method that can render continuous graphical cross-sectional images of the brain's electrical properties. Because these properties can be altered by variations in water content, shifts in sodium concentration, bleeding, and mass deformation, EIT has promise as a sensitive instrument for head injury monitoring to improve early recognition of deterioration and to observe the benefits of therapeutic intervention. This study presents a swine model of head injury used to determine the detection capabilities of an inexpensive bedside EIT monitoring system with a novel intracranial pressure (ICP)/EIT electrode combination sensor on induced intraparenchymal mass effect, intraparenchymal hemorrhage, and cessation of brain blood flow. Conductivity difference images are shown in conjunction with ICP data, confirming the effects. METHODS: Eight domestic piglets (3-4 weeks of age, mean 10 kg), under general anesthesia, were subjected to 4 injuries: induced intraparenchymal mass effect using an inflated, and later, deflated 0.15-mL Fogarty catheter; hemorrhage by intraparenchymal injection of 1-mL arterial blood; and ischemia/infarction by euthanasia. EIT and ICP data were recorded 10 minutes before inducing the injury until 10 minutes after injury. Continuous EIT and ICP monitoring were facilitated by a ring of circumferentially disposed cranial Ag/AgCl electrodes and 1 intraparenchymal ICP/EIT sensor electrode combination. Data were recorded at 100 Hz. Two-dimensional tomographic conductivity difference (Δσ) images, rendered using data before and after an injury, were displayed in real time on an axial circular mesh. Regions of interest (ROI) within the images were automatically selected as the upper or lower 5% of conductivity data depending on the nature of the injury. Mean Δσ within the ROIs and background were statistically analyzed. ROI Δσ was compared with the background Δσ after an injury event using an unpaired, unequal variance t test. Conductivity change within an ROI after injury was likewise compared with the same ROI before the injury making use of unpaired t tests with unequal variance. RESULTS: Eight animal subjects were studied, each undergoing 4 injury events including euthanasia. Changes in conductivity due to injury showed expected pathophysiologic effects in an ROI identified within the middle of the left hemisphere; this localization is reasonable given the actual site of injury (left hemisphere) and spatial warping associated with estimating a 3-dimensional conductivity distribution in 2-dimensional space. Results are shown as mean ± 1 SD. When averaged across all 8 animals, balloon inflation caused the mean Δσ within the ROI to shift by -11.4 ± 10.9 mS/m; balloon deflation by +9.4 ± 8.8 mS/m; blood injection by +19.5 ± 11.5 mS/m; death by -12.6 ± 13.2 mS/m. All induced injuries were detectable to statistical significance (P < 0.0001). CONCLUSION: This study confirms that the bedside EIT system with ICP/EIT combination sensor can detect induced trauma. Such a technique may hold promise for further research in the monitoring and management of traumatically brain-injured individuals.

FPGA Based High Speed Data Acquisition System for Electrical Impedance Tomography.

Electrical Impedance Tomography (EIT) systems are used to image tissue bio-impedance. EIT provides a number of features making it attractive for use as a medical imaging device including the ability to image fast physiological processes (>60 Hz), to meet a range of clinical imaging needs through varying electrode geometries and configurations, to impart only non-ionizing radiation to a patient, and to map the significant electrical property contrasts present between numerous benign and pathological tissues. To leverage these potential advantages for medical imaging, we developed a modular 32 channel data acquisition (DAQ) system using National Instruments' PXI chassis, along with FPGA, ADC, Signal Generator and Timing and Synchronization modules. To achieve high frame rates, signal demodulation and spectral characteristics of higher order harmonics were computed using dedicated FFT-hardware built into the FPGA module. By offloading the computing onto FPGA, we were able to achieve a reduction in throughput required between the FPGA and PC by a factor of 32:1. A custom designed analog front end (AFE) was used to interface electrodes with our system. Our system is wideband, and capable of acquiring data for input signal frequencies ranging from 100 Hz to 12 MHz. The modular design of both the hardware and software will allow this system to be flexibly configured for the particular clinical application.

Towards intraoperative surgical margin assessment: Validation of an electrical impedance-based probe with ex vivo bovine tissue.

Radical prostatectomy (RP) is a common surgical therapy to treat prostate cancer. The procedure has a high positive surgical margin (PSM) rate ranging from 4-48%. Patients with PSMs have a higher rate of cancer recurrence and often undergo noxious adjuvant therapy. Intraoperative surgical margin assessment (SMA) with an electrical impedance-based probe can potentially identify PSMs in real-time. This would enable surgeons to make data-based decisions in the operating room to improve patient outcomes. This paper focuses on characterizing an impedance sensing SMA probe with specialized electrodes to improve speed and bandwidth while maintaining accuracy. 3D electrical impedance tomography (EIT) reconstructions were generated from ex vivo bovine tissue to characterize probe imaging and to determine an optimal applied pressure range (15 Pa to 38 Pa). Classification accuracy of adipose and muscle tissue was evaluated by comparing the experimental data set to simulated data based on a ground truth binary map of the tissue. Experimental AUCs ≥0.83 were maintained up to 50 kHz. The developed impedance sensing probe successfully classified between muscle and adipose tissue in an ex vivo bovine model. Future work includes improving performance of the SMA probe with custom hardware and collecting data from ex vivo and in vivo prostatic tissues.Clinical Relevance-This technology is expected to reduce the rate of PSMs in RP and decrease the use of post-surgical adjuvant therapies. It is also anticipated that intraoperative impedance measurements will increase efficacy of nerve sparing procedures and reduce complications such as incontinence and erectile dysfunction.

EEG electrode localization with 3D iPhone scanning using point-cloud electrode selection (PC-ES).

Objective.Electroencephalography source imaging (ESI) is a valuable tool in clinical evaluation for epilepsy patients but is underutilized in part due to sensitivity to anatomical modeling errors. Accurate localization of scalp electrodes is instrumental to ESI, but existing localization devices are expensive and not portable. As a result, electrode localization challenges further impede access to ESI, particularly in inpatient and intensive care settings.Approach.To address this challenge, we present a portable and affordable electrode digitization method using the 3D scanning feature in modern iPhone models. This technique combines iPhone scanning with semi-automated image processing using point-cloud electrode selection (PC-ES), a custom MATLAB desktop application. We compare iPhone electrode localization to state-of-the-art photogrammetry technology in a human study with over 6000 electrodes labeled using each method. We also characterize the performance of PC-ES with respect to head location and examine the relative impact of different algorithm parameters.Main Results.The median electrode position variation across reviewers was 1.50 mm for PC-ES scanning and 0.53 mm for photogrammetry, and the average median distance between PC-ES and photogrammetry electrodes was 3.4 mm. These metrics demonstrate comparable performance of iPhone/PC-ES scanning to currently available technology and sufficient accuracy for ESI.Significance.Low cost, portable electrode localization using iPhone scanning removes barriers to ESI in inpatient, outpatient, and remote care settings. While PC-ES has current limitations in user bias and processing time, we anticipate these will improve with software automation techniques as well as future developments in iPhone 3D scanning technology.

Brain responses to food viewing in women during pregnancy and post partum and their relationship with metabolic health: study protocol for the FOODY Brain Study, a prospective observational study.

INTRODUCTION: Food cravings are common in pregnancy and along with emotional eating and eating in the absence of hunger, they are associated with excessive weight gain and adverse effects on metabolic health including gestational diabetes mellitus (GDM). Women with GDM also show poorer mental health, which further can contribute to dysregulated eating behaviour. Food cravings can lead to greater activity in brain centres known to be involved in food 'wanting' and reward valuation as well as emotional eating. They are also related to gestational weight gain. Thus, there is a great need to link implicit brain responses to food with explicit measures of food intake behaviour, especially in the perinatal period. The aim of this study is to investigate the spatiotemporal brain dynamics to visual presentations of food in women during pregnancy and in the post partum, and link these brain responses to the eating behaviour and metabolic health outcomes in women with and without GDM. METHODS AND ANALYSIS: This prospective observational study will include 20 women with and 20 without GDM, that have valid data for the primary outcomes. Data will be assessed at 24-36 weeks gestational age and at 6 months post partum. The primary outcomes are brain responses to food pictures of varying carbohydrate and fat content during pregnancy and in the post partum using electroencephalography. Secondary outcomes including depressive symptoms, current mood and eating behaviours will be assessed with questionnaires, objective eating behaviours will be measured using Auracle and stress will be measured with heart rate and heart rate variability (Actiheart). Other secondary outcome measures include body composition and glycaemic control parameters. ETHICS AND DISSEMINATION: The Human Research Ethics Committee of the Canton de Vaud approved the study protocol (2021-01976). Study results will be presented at public and scientific conferences and in peer-reviewed journals.

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.

Novel Electrode Placement in Electrical Bioimpedance-Based Stroke Detection: Effects on Current Penetration and Injury Characterization in a Finite Element Model.

OBJECTIVE: Reducing time-to-treatment and providing acute management in stroke are essential for patient recovery. Electrical bioimpedance (EBI) is an inexpensive and non-invasive tissue measurement approach that has the potential to provide novel continuous intracranial monitoring-something not possible in current standard-of-care. While extensive previous work has evaluated the feasibility of EBI in diagnosing stroke, high-impedance anatomical features in the head have limited clinical translation. METHODS: The present study introduces novel electrode placements near highly-conductive cerebral spinal fluid (CSF) pathways to enhance electrical current penetration through the skull and increase detection accuracy of neurologic damage. Simulations were conducted on a realistic finite element model (FEM). Novel electrode placements at the tear ducts, soft palate and base of neck were evaluated. Classification accuracy was assessed in the presence of signal noise, patient variability, and electrode positioning. RESULTS: Algorithms were developed to successfully determine stroke etiology, location, and size relative to impedance measurements from a baseline scan. Novel electrode placements significantly increased stroke classification accuracy at various levels of signal noise (e.g., p < 0.001 at 40 dB). Novel electrodes also amplified current penetration, with up to 30% increase in current density and 57% increased sensitivity in central intracranial regions (p < 0.001). CONCLUSION: These findings support the use of novel electrode placements in EBI to overcome prior limitations, indicating a potential approach to increasing the technology's clinical utility in stroke identification. SIGNIFICANCE: A non-invasive EBI monitor for stroke could provide essential timely intervention and care of stroke patients.

A particle filter approach to dynamic kidney pose estimation in robotic surgical exposure.

PURPOSE: Traditional soft tissue registration methods require direct intraoperative visualization of a significant portion of the target anatomy in order to produce acceptable surface alignment. Image guidance is therefore generally not available during the robotic exposure of structures like the kidneys which are not immediately visualized upon entry into the abdomen. This paper proposes guiding surgical exposure with an iterative state estimator that assimilates small visual cues into an a priori anatomical model as exposure progresses, thereby evolving pose estimates for the occluded structures of interest. METHODS: Intraoperative surface observations of a right kidney are simulated using endoscope tracking and preoperative tomography from a representative robotic partial nephrectomy case. Clinically relevant random perturbations of the true kidney pose are corrected using this sequence of observations in a particle filter framework to estimate an optimal similarity transform for fitting a patient-specific kidney model at each step. The temporal response of registration error is compared against that of serial rigid coherent point drift (CPD) in both static and simulated dynamic surgical fields, and for varying levels of observation persistence. RESULTS: In the static case, both particle filtering and persistent CPD achieved sub-5 mm accuracy, with CPD processing observations 75% faster. Particle filtering outperformed CPD in the dynamic case under equivalent computation times due to the former requiring only minimal persistence. CONCLUSION: This proof-of-concept simulation study suggests that Bayesian state estimation may provide a viable pathway to image guidance for surgical exposure in the abdomen, especially in the presence of dynamic intraoperative tissue displacement and deformation.