A soft multi-axial force sensor to assess tissue properties in RealTime

Objective: This work presents a method for the use of a soft multi-axis force sensor to determine tissue trauma in Minimally Invasive Surgery. Despite recent developments, there is a lack of effective haptic sensing technology employed in instruments for Minimally Invasive Surgery (MIS). There is thus a clear clinical need to increase the provision of haptic feedback and to perform real-time analysis of haptic data to inform the surgical operator. This paper establishes a methodology for the capture of real-time data through use of an inexpensive prototype grasper. Fabricated using soft silicone and 3D printing, the sensor is able to precisely detect compressive and shear forces applied to the grasper face. The sensor is based upon a magnetic soft tactile sensor, using variations in the local magnetic field to determine force. The performance of the sensing element is assessed and a linear response was observed, with a max hysteresis error of 4.1% of the maximum range of the sensor. To assess the potential of the sensor for surgical sensing, a simulated grasping study was conducted using ex vivo porcine tissue. Two previously established metrics for prediction of tissue trauma were obtained and compared from recorded data. The normalized stress rate (kPa.mm⁻¹) of compression and the normalized stress relaxation (ΔσR) were analyzed across repeated grasps. The sensor was able to obtain measures in agreement with previous research, demonstrating future potential for this approach. In summary this work demonstrates that inexpensive soft sensing systems can be used to instrument surgical tools and thus assess properties such as tissue health. This could help reduce surgical error and thus benefit patients

DefCor-Net: Physics-Aware Ultrasound Deformation Correction

The recovery of morphologically accurate anatomical images from deformed ones is challenging in ultrasound (US) image acquisition, but crucial to accurate and consistent diagnosis, particularly in the emerging field of computer-assisted diagnosis. This article presents a novel anatomy-aware deformation correction approach based on a coarse-to-fine, multi-scale deep neural network (DefCor-Net). To achieve pixel-wise performance, DefCor-Net incorporates biomedical knowledge by estimating pixel-wise stiffness online using a U-shaped feature extractor. The deformation field is then computed using polynomial regression by integrating the measured force applied by the US probe. Based on real-time estimation of pixel-by-pixel tissue properties, the learning-based approach enables the potential for anatomy-aware deformation correction. To demonstrate the effectiveness of the proposed DefCor-Net, images recorded at multiple locations on forearms and upper arms of six volunteers are used to train and validate DefCor-Net. The results demonstrate that DefCor-Net can significantly improve the accuracy of deformation correction to recover the original geometry (Dice Coefficient: from $14.3\pm20.9$ to $82.6\pm12.1$ when the force is $6N$).Comment: Accepted by MedIA. code is availabl

Shear-promoted drug encapsulation into red blood cells: a CFD model and μ-PIV analysis

The present work focuses on the main parameters that influence shear-promoted encapsulation of drugs into erythrocytes. A CFD model was built to investigate the fluid dynamics of a suspension of particles flowing in a commercial micro channel. Micro Particle Image Velocimetry (μ-PIV) allowed to take into account for the real properties of the red blood cell (RBC), thus having a deeper understanding of the process. Coupling these results with an analytical diffusion model, suitable working conditions were defined for different values of haematocrit

Design, Development and Force Control of a Tendon-driven Steerable Catheter with a Learning-based Approach

In this research, a learning-based force control schema for tendon-driven steerable catheters with the application in robot-assisted tissue ablation procedures was proposed and validated. To this end, initially a displacement-based model for estimating the contact force between the catheter and tissue was developed. Afterward, a tendon-driven catheter was designed and developed. Next, a software-hardware-integrated robotic system for controlling and monitoring the pose of the catheter was designed and developed. Also, a force control schema was developed based on the developed contact force model as a priori knowledge. Furthermore, the position control of the tip of the catheter was performed using a learning-based inverse kinematic approach. By combining the position control and the contact model, the force control schema was developed and validated. Validation studies were performed on phantom tissue as well as excised porcine tissue. Results of the validation studies showed that the proposed displacement-based model was 91.5% accurate in contact force prediction. Also, the system was capable of following a set of desired trajectories with an average root-mean-square error of less than 5%. Further validation studies revealed that the system could fairly generate desired static and dynamic force profiles on the phantom tissue. In summary, the proposed force control system did not necessitate the utilization of force sensors and could fairly contribute in automatizing the ablation task for robotic tissue ablation procedures

OPTICAL-BASED TACTILE SENSORS FOR MINIMALLY INVASIVE SURGERIES: DESIGN, MODELING, FABRICATION AND VALIDATION

Loss of tactile perception is the most challenging limitation of state-of-the-art technology for minimally invasive surgery. In conventional open surgery, surgeons rely on their tactile sensation to perceive the tissue type, anatomical landmarks, and instrument-tissue interaction in the patient’s body. To compensate for the loss of tactile feedback in minimally invasive surgery, researchers have proposed various tactile sensors based on electrical and optical sensing principles. Optical-based sensors have shown the most compatibility with the functional and physical requirements of minimally invasive surgery applications. However, the proposed tactile sensors in the literature are typically bulky, expensive, cumbersome to integrate with surgical instruments and show nonlinearity in interaction with biological tissues. In this doctoral study, different optical tactile sensing principles were proposed, modeled, validated and various tactile sensors were fabricated, and experimentally studied to address the limitations of the state-of-the-art. The present thesis first provides a critical review of the proposed tactile sensors in the literature with a comparison of their advantages and limitations for surgical applications. Afterward, it compiles the results of the design, modeling, and validation of a hybrid optical-piezoresistive sensor, a distributed Bragg reflecting sensor, and two sensors based on the variable bending radius light intensity modulation principle. The performance of each sensor was verified experimentally for the required criteria of accuracy, resolution, range, repeatability, and hysteresis. Also, a novel image-based intensity estimation technique was proposed and its applicability for being used in surgical applications was verified experimentally. In the end, concluding remarks and recommendations for future studies are provided

jULIEs: nanostructured polytrodes for low traumatic extracellular recordings and stimulation in the mammalian brain

Objective.Extracellular microelectrode techniques are the most widely used approach to interrogate neuronal populations. However, regardless of the manufacturing method used, damage to the vasculature and circuit function during probe insertion remains a concern. This issue can be mitigated by minimising the footprint of the probe used. Reducing the size of probes typically requires either a reduction in the number of channels present in the probe, or a reduction in the individual channel area. Both lead to less effective coupling between the probe and extracellular signals of interest.Approach.Here, we show that continuously drawn SiO2-insulated ultra-microelectrode fibres offer an attractive substrate to address these challenges. Individual fibres can be fabricated to >10 m continuous stretches and a selection of diameters below 30µm with low resistance (<100 Ω mm-1) continuously conductive metal core of <10µm and atomically flat smooth shank surfaces. To optimize the properties of the miniaturised electrode-tissue interface, we electrodeposit rough Au structures followed by ∼20 nm IrOx film resulting in the reduction of the interfacial impedance to <500 kΩ at 1 kHz.Main results. We demonstrate that these ultra-low impedance electrodes can record and stimulate both single and multi-unit activity with minimal tissue disturbance and exceptional signal-to-noise ratio in both superficial (∼40µm) and deep (∼6 mm) structures of the mouse brain. Further, we show that sensor modifications are stable and probe manufacturing is reproducible.Significance.Minimally perturbing bidirectional neural interfacing can reveal circuit function in the mammalian brainin vivo

New Image Processing Methods for Ultrasound Musculoskeletal Applications

In the past few years, ultrasound (US) imaging modalities have received increasing interest as diagnostic tools for orthopedic applications. The goal for many of these novel ultrasonic methods is to be able to create three-dimensional (3D) bone visualization non-invasively, safely and with high accuracy and spatial resolution. Availability of accurate bone segmentation and 3D reconstruction methods would help correctly interpreting complex bone morphology as well as facilitate quantitative analysis. However, in vivo ultrasound images of bones may have poor quality due to uncontrollable motion, high ultrasonic attenuation and the presence of imaging artifacts, which can affect the quality of the bone segmentation and reconstruction results. In this study, we investigate the use of novel ultrasonic processing methods that can significantly improve bone visualization, segmentation and 3D reconstruction in ultrasound volumetric data acquired in applications in vivo. Specifically, in this study, we investigate the use of new elastography-based, Doppler-based and statistical shape model-based methods that can be applied to ultrasound bone imaging applications with the overall major goal of obtaining fast yet accurate 3D bone reconstructions. This study is composed to three projects, which all have the potential to significantly contribute to this major goal. The first project deals with the fast and accurate implementation of correlation-based elastography and poroelastography techniques for real-time assessment of the mechanical properties of musculoskeletal tissues. The rationale behind this project is that, iii in the future, elastography-based features can be used to reduce false positives in ultrasonic bone segmentation methods based on the differences between the mechanical properties of soft tissues and the mechanical properties of hard tissues. In this study, a hybrid computation model is designed, implemented and tested to achieve real time performance without compromise in elastographic image quality . In the second project, a Power Doppler-based signal enhancement method is designed and tested with the intent of increasing the contrast between soft tissue and bone while suppressing the contrast between soft tissue and connective tissue, which is often a cause of false positives in ultrasonic bone segmentation problems. Both in-vitro and in-vivo experiments are performed to statistically analyze the performance of this method. In the third project, a statistical shape model based bone surface segmentation method is proposed and investigated. This method uses statistical models to determine if a curve detected in a segmented ultrasound image belongs to a bone surface or not. Both in-vitro and in-vivo experiments are performed to statistically analyze the performance of this method. I conclude this Dissertation with a discussion on possible future work in the field of ultrasound bone imaging and assessment

Photoacoustic microscopy

Photoacoustic microscopy (PAM) is a hybrid in vivo imaging technique that acoustically detects optical contrast via the photoacoustic effect. Unlike pure optical microscopic techniques, PAM takes advantage of the weak acoustic scattering in tissue and thus breaks through the optical diffusion limit (∼1 mm in soft tissue). With its excellent scalability, PAM can provide high-resolution images at desired maximum imaging depths up to a few millimeters. Compared with backscattering-based confocal microscopy and optical coherence tomography, PAM provides absorption contrast instead of scattering contrast. Furthermore, PAM can image more molecules, endogenous or exogenous, at their absorbing wavelengths than fluorescence-based methods, such as wide-field, confocal, and multi-photon microscopy. Most importantly, PAM can simultaneously image anatomical, functional, molecular, flow dynamic and metabolic contrasts in vivo. Focusing on state-of-the-art developments in PAM, this Review discusses the key features of PAM implementations and their applications in biomedical studies

Biomechanical Modeling and Inverse Problem Based Elasticity Imaging for Prostate Cancer Diagnosis

Early detection of prostate cancer plays an important role in successful prostate cancer treatment. This requires screening the prostate periodically after the age of 50. If screening tests lead to prostate cancer suspicion, prostate needle biopsy is administered which is still considered as the clinical gold standard for prostate cancer diagnosis. Given that needle biopsy is invasive and is associated with issues including discomfort and infection, it is desirable to develop a prostate cancer diagnosis system that has high sensitivity and specificity for early detection with a potential to improve needle biopsy outcome. Given the complexity and variability of prostate cancer pathologies, many research groups have been pursuing multi-parametric imaging approach as no single modality imaging technique has proven to be adequate. While imaging additional tissue properties increases the chance of reliable prostate cancer detection and diagnosis, selecting an additional property needs to be done carefully by considering clinical acceptability and cost. Clinical acceptability entails ease with respect to both operating by the radiologist and patient comfort. In this work, effective tissue biomechanics based diagnostic techniques are proposed for prostate cancer assessment with the aim of early detection and minimizing the numbers of prostate biopsies. The techniques take advantage of the low cost, widely available and well established TRUS imaging method. The proposed techniques include novel elastography methods which were formulated based on an inverse finite element frame work. Conventional finite element analysis is known to have high computational complexity, hence computation time demanding. This renders the proposed elastography methods not suitable for real-time applications. To address this issue, an accelerated finite element method was proposed which proved to be suitable for prostate elasticity reconstruction. In this method, accurate finite element analysis of a large number of prostates undergoing TRUS probe loadings was performed. Geometry input and displacement and stress fields output obtained from the analysis were used to train a neural network mapping function to be used for elastopgraphy imaging of prostate cancer patients. The last part of the research presented in this thesis tackles an issue with the current 3D TRUS prostate needle biopsy. Current 3D TRUS prostate needle biopsy systems require registering preoperative 3D TRUS to intra-operative 2D TRUS images. Such image registration is time-consuming while its real-time implementation is yet to be developed. To bypass this registration step, concept of a robotic system was proposed which can reliably determine the preoperative TRUS probe position relative to the prostate to place at the same position relative to the prostate intra-operatively. For this purpose, a contact pressure feedback system is proposed to ensure similar prostate deformation during 3D and 2D image acquisition in order to bypass the registration step