Registration of ultrasound and computed tomography for guidance of laparoscopic liver surgery

Laparoscopic Ultrasound (LUS) imaging is a standard tool used for image-guidance during laparoscopic liver resection, as it provides real-time information on the internal structure of the liver. However, LUS probes are di cult to handle and their resulting images hard to interpret. Additionally, some anatomical targets such as tumours are not always visible, making the LUS guidance less e ective. To solve this problem, registration between the LUS images and a pre-operative Computed Tomography (CT) scan using information from blood vessels has been previously proposed. By merging these two modalities, the relative position between the LUS images and the anatomy of CT is obtained and both can be used to guide the surgeon. The problem of LUS to CT registration is specially challenging, as besides being a multi-modal registration, the eld of view of LUS is signi cantly smaller than that of CT. Therefore, this problem becomes poorly constrained and typically an accurate initialisation is needed. Also, the liver is highly deformed during laparoscopy, complicating the problem further. So far, the methods presented in the literature are not clinically feasible as they depend on manually set correspondences between both images. In this thesis, a solution for this registration problem that may be more transferable to the clinic is proposed. Firstly, traditional registration approaches comprised of manual initialisation and optimisation of a cost function are studied. Secondly, it is demonstrated that a globally optimal registration without a manual initialisation is possible. Finally, a new globally optimal solution that does not require commonly used tracking technologies is proposed and validated. The resulting approach provides clinical value as it does not require manual interaction in the operating room or tracking devices. Furthermore, the proposed method could potentially be applied to other image-guidance problems that require registration between ultrasound and a pre-operative scan

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

A Low Cost and Computationally Efficient Approach for Occlusion Handling in Video Surveillance Systems

In the development of intelligent video surveillance systems for tracking a vehicle, occlusions are one of the major challenges. It becomes difficult to retain features during occlusion especially in case of complete occlusion. In this paper, a target vehicle tracking algorithm for Smart Video Surveillance (SVS) is proposed to track an unidentified target vehicle even in case of occlusions. This paper proposes a computationally efficient approach for handling occlusions named as Kalman Filter Assisted Occlusion Handling (KFAOH) technique. The algorithm works through two periods namely tracking period when no occlusion is seen and detection period when occlusion occurs, thus depicting its hybrid nature. Kanade-Lucas-Tomasi (KLT) feature tracker governs the operation of algorithm during the tracking period, whereas, a Cascaded Object Detector (COD) of weak classifiers, specially trained on a large database of cars governs the operation during detection period or occlusion with the assistance of Kalman Filter (KF). The algorithm’s tracking efficiency has been tested on six different tracking scenarios with increasing complexity in real-time. Performance evaluation under different noise variances and illumination levels shows that the tracking algorithm has good robustness against high noise and low illumination. All tests have been conducted on the MATLAB platform. The validity and practicality of the algorithm are also verified by success plots and precision plots for the test cases

Patient-specific simulation environment for surgical planning and preoperative rehearsal

Surgical simulation is common practice in the fields of surgical education and training. Numerous surgical simulators are available from commercial and academic organisations for the generic modelling of surgical tasks. However, a simulation platform is still yet to be found that fulfils the key requirements expected for patient-specific surgical simulation of soft tissue, with an effective translation into clinical practice. Patient-specific modelling is possible, but to date has been time-consuming, and consequently costly, because data preparation can be technically demanding. This motivated the research developed herein, which addresses the main challenges of biomechanical modelling for patient-specific surgical simulation. A novel implementation of soft tissue deformation and estimation of the patient-specific intraoperative environment is achieved using a position-based dynamics approach. This modelling approach overcomes the limitations derived from traditional physically-based approaches, by providing a simulation for patient-specific models with visual and physical accuracy, stability and real-time interaction. As a geometrically- based method, a calibration of the simulation parameters is performed and the simulation framework is successfully validated through experimental studies. The capabilities of the simulation platform are demonstrated by the integration of different surgical planning applications that are found relevant in the context of kidney cancer surgery. The simulation of pneumoperitoneum facilitates trocar placement planning and intraoperative surgical navigation. The implementation of deformable ultrasound simulation can assist surgeons in improving their scanning technique and definition of an optimal procedural strategy. Furthermore, the simulation framework has the potential to support the development and assessment of hypotheses that cannot be tested in vivo. Specifically, the evaluation of feedback modalities, as a response to user-model interaction, demonstrates improved performance and justifies the need to integrate a feedback framework in the robot-assisted surgical setting.Open Acces

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

The impact of Joint Hypermobility Syndrome in adults: A quantitative exploration of neuromuscular impairments, activity limitations and participation restrictions

Abstract:Introduction: Joint hypermobility syndrome (JHS) is a heritable connective tissue disorder associated with multiple joint laxity and pain. JHS is severe and disabling condition with a prevalence reaching 55% of patients attended physiotherapy with musculoskeletal symptoms. However, the literature is limited in quantity and quality to support the assessment and management strategies for people with JHS. Therefore, impairment, activity and participation were explored to identify the underlying problems. Methods: A cross-section design was employed to compare a group of adults with JHS against a matched control group. Neuromuscular impairments were explored through five domains: 1) pain intensity in the lower-limb joints was measured using visual analogue scales. 2) Achilles tendon stiffness was measured using the diagnostic ultrasound with strain-gauge myometer. 3) the plantar flexors strength was measured using the strain-gauge myometer. 4) knee proprioception was explored using the angle reproduction test. 5) gastrocnemius medius (GM) elasticity was quantified using the sonoelastography (SEG). Regarding the activity domain, both gait and vertical jump were analysed in terms of spatiotemporal, kinematics and kinetics using the Qualisys motion capture system, synchronised with the Kistler platform. The participation domain was assessed using the 12-item Short Form Health Survey (SF-12) and the Bristol Impact of Hypermobility questionnaire (BIoH). Additionally, the feasibility of the SEG was explored, and the intra-rater reliabilities for examining the Achilles tendon stiffness and gait kinematics were determined. Statistical Package for Social Sciences (SPSS) was used to conduct the statistical analysis. Results: The JHS group included 29 women and two men aged 38.52 ± 14.14 years (mean ± SD), while the control group included 29 women and two men aged 39.06 ± 12.43 years (mean ± SD). Various statistically significant differences were identified in the JHS group when compared to the control group, including increased pain intensity (all p ≤ 0.001), reduced Achilles tendon stiffness (p = 0.03), reduced plantar flexors strength (p = 0.01) and reduced non-dominant knee proprioception (p range 0.001 – 0.04). The gait and jump kinematics in the JHS group were mostly comparable to the control (p ≥ 0.05), with statistically significant reductions in moments (p ranged from 0.001 - 0.04) and power generation and absorption (p ranged from 0.001- 0.04) in the JHS group. Significant reductions in the participation level were evidenced in the JHS group, obtained from SF-12 (p ranged from 0.001 - 0.002), with significant impact from JHS (211.51 ± 39.28)/360 (mean ± standard deviation) obtained from the BIoH. Sonoelastography seems a feasible tool in terms of training, examination time, patient tolerance, and image analysis. High intra-rater reliability was demonstrated for examining the Achilles tendon stiffness (ICC ranged from 0.981 – 0.984), and moderate-high intra-rater reliability was demonstrated for examining gait kinematics (ICC ranged from 0.625 – 0.996). Conclusion: JHS has a multi-dimensional impact, causing neuromuscular impairment, activity limitations and participation restrictions. Assessment strategies should consider this multi-dimensional impact of the condition, and management strategies should be multi-disciplinary, aiming to target the problems identified in the current study.Key words: Joint hypermobility syndrome, impairment, activity, participation

Patient-specific simulation for autonomous surgery

An Autonomous Robotic Surgical System (ARSS) has to interact with the complex anatomical environment, which is deforming and whose properties are often uncertain. Within this context, an ARSS can benefit from the availability of patient-specific simulation of the anatomy. For example, simulation can provide a safe and controlled environment for the design, test and validation of the autonomous capabilities. Moreover, it can be used to generate large amounts of patient-specific data that can be exploited to learn models and/or tasks. The aim of this Thesis is to investigate the different ways in which simulation can support an ARSS and to propose solutions to favor its employability in robotic surgery. We first address all the phases needed to create such a simulation, from model choice in the pre-operative phase based on the available knowledge to its intra-operative update to compensate for inaccurate parametrization. We propose to rely on deep neural networks trained with synthetic data both to generate a patient-specific model and to design a strategy to update model parametrization starting directly from intra-operative sensor data. Afterwards, we test how simulation can assist the ARSS, both for task learning and during task execution. We show that simulation can be used to efficiently train approaches that require multiple interactions with the environment, compensating for the riskiness to acquire data from real surgical robotic systems. Finally, we propose a modular framework for autonomous surgery that includes deliberative functions to handle real anatomical environments with uncertain parameters. The integration of a personalized simulation proves fundamental both for optimal task planning and to enhance and monitor real execution. The contributions presented in this Thesis have the potential to introduce significant step changes in the development and actual performance of autonomous robotic surgical systems, making them closer to applicability to real clinical conditions