Using CamiTK for rapid prototyping of interactive Computer Assisted Medical Intervention applications

Computer Assisted Medical Intervention (CAMI hereafter) is a complex multi-disciplinary field. CAMI research requires the collaboration of experts in several fields as diverse as medicine, computer science, mathematics, instrumentation, signal processing, mechanics, modeling, automatics, optics, etc

Determining the Biomechanical Behavior of the Liver Using Medical Image Analysis and Evolutionary Computation

Modeling the liver deformation forms the basis for the development of new clinical applications that improve the diagnosis, planning and guidance in liver surgery. However, the patient-specific modeling of this organ and its validation are still a challenge in Biomechanics. The reason is the difficulty to measure the mechanical response of the in vivo liver tissue. The current approach consist of performing minimally invasive or open surgery aimed at estimating the elastic constant of the proposed biomechanical models. This dissertation presents how the use of medical image analysis and evolutionary computation allows the characterization of the biomechanical behavior of the liver, avoiding the use of these minimally invasive techniques. In particular, the use of similarity coefficients commonly used in medical image analysis has permitted, on one hand, to estimate the patient-specific biomechanical model of the liver avoiding the invasive measurement of its mechanical response. On the other hand, these coefficients have also permitted to validate the proposed biomechanical models. Jaccard coefficient and Hausdorff distance have been used to validate the models proposed to simulate the behavior of ex vivo lamb livers, calculating the error between the volume of the experimentally deformed samples of the livers and the volume from biomechanical simulations of these deformations. These coefficients has provided information, such as the shape of the samples and the error distribution along their volume. For this reason, both coefficients have also been used to formulate a novel function, the Geometric Similarity Function (GSF). This function has permitted to establish a methodology to estimate the elastic constants of the models proposed for the human liver using evolutionary computation. Several optimization strategies, using GSF as cost function, have been developed aimed at estimating the patient-specific elastic constants of the biomechanical models proposed for the human liver. Finally, this methodology has been used to define and validate a biomechanical model proposed for an in vitro human liver.Martínez Martínez, F. (2014). Determining the Biomechanical Behavior of the Liver Using Medical Image Analysis and Evolutionary Computation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/39337TESI

Essential techniques for improving visual realism of laparoscopic surgery simulation.

With the prevalence of laparoscopic surgery, the request for reliable training and assessment is becoming increasingly important. The traditional way of training is both time consuming and cost intensive, and may cause ethical or moral issues. With the development of computer technologies, virtual reality has entered the world of consumer electronics as a new way to enhance tactile and visual sensory experiences. Virtual reality based surgical skill training gradually becomes an effective supplementary to the traditional laparoscopic skill training in many surgical theatres. To provide high fidelity virtual surgery training experiences, the presentation of the virtual world should have the same level of realism as what surgeons see and feel during real operations. However, the weak computing power limits the potential level of details on the graphics presentation and physical behaviour of virtual objects, which will further influence the fidelity of tactile interaction. Achieving visual realism (realistic graphics presentation and accurate physical behaviour) and good user experience using limited computing resources is the main challenge for laparoscopic surgery simulation. The topic of visual realism in laparoscopic surgery simulation has not been well researched. This topic mainly relates to the area of 3D anatomy modeling, soft body simulation and rendering. Current researches in computer graphics and game communities are not tailored for laparoscopic surgery simulation. The direct use of those techniques in developing surgery simulators will often result in poor quality anatomy model, inaccurate simulation, low fidelity visual effect, poor user experience and inefficient production pipeline, which significantly influence the visual realism of the virtual world. The development of laparoscopic surgery simulator is an interdiscipline of computer graphics, computational physics and haptics. However, current researches barely focus on the study of tailored techniques and efficient production pipeline which often result in the long term research cycle and daunting cost for simulator development. This research is aiming at improving the visual realism of laparoscopic surgery simulation from the perspective of computer graphics. In this research, a set of tailor techniques have been proposed to improve the visual realism for laparoscopic surgery simulation. For anatomy modeling, an automatic and efficient 3D anatomy conversion pipeline is proposed which can convert bad quality 3D anatomy into simulation ready state while preserving the original model’s surface parameterization property. For simulation, a soft tissue simulation pipeline is pro- posed which can provide multi-layer heterogeneous soft tissue modeling and intuitive physically editable simulation based on uniform polynomial based hyperelastic material representation. For interaction, a collision detection and interaction system based on adaptive circumphere structure is proposed which supports robust and efficient sliding con- tact, energized dissection and clip. For rendering, a multi-layer soft tissue rendering pipeline is proposed which decomposed the multi-layer structure of soft tissue into corresponding material asset required by state-of-art rendering techniques. Based on this research, a system framework for building a laparoscopic surgery simulator is also proposed to test the feasibility of those tailored techniques

Finite element modeling of soft tissue deformation.

Computer-aided minimally invasive surgery (MIS) has progressed significantly in the last decade and it has great potential in surgical planning and operations. To limit the damage to nearby healthy tissue, accurate modeling is required of the mechanical behavior of a target soft tissue subject to surgical manipulations. Therefore, the study of soft tissue deformations is important for computer-aided (MIS) in surgical planning and operation, or in developing surgical simulation tools or systems. The image acquisition facilities are also important for prediction accuracy. This dissertation addresses partial differential and integral equations (PDIE) based biomechanical modeling of soft tissue deformations incorporating the specific material properties to characterize the soft tissue responses for certain human interface behaviors. To achieve accurate simulation of real tissue deformations, several biomechanical finite element (FE) models are proposed to characterize liver tissue. The contribution of this work is in theoretical and practical aspects of tissue modeling. High resolution imaging techniques of Micro Computed Tomography (Micro-CT) and Cone Beam Computed Tomography (CBCT) imaging are first proposed to study soft tissue deformation in this dissertation. These high resolution imaging techniques can detect the tissue deformation details in the contact region between the tissue and the probe for small force loads which would be applied to a surgical probe used. Traditional imaging techniques in clinics can only achieve low image resolutions. Very small force loads seen in these procedures can only yield tissue deformation on the few millimeters to submillimeter scale. Small variations are hardly to detect. Furthermore, if a model is validated using high resolution images, it implies that the model is true in using the same model for low resolution imaging facilities. The reverse cannot be true since the small variations at the sub-millimeter level cannot be detected. In this dissertation, liver tissue deformations, surface morphological changes, and volume variations are explored and compared from simulations and experiments. The contributions of the dissertation are as follows. For liver tissue, for small force loads (5 grams to tens of grams), the linear elastic model and the neo-Hooke\u27s hyperelastic model are applied and shown to yield some discrepancies among them in simulations and discrepancies between simulations and experiments. The proposed finite element models are verified for liver tissue. A general FE modeling validation system is proposed to verify the applicability of FE models to the soft tissue deformation study. The validation of some FE models is performed visually and quantitatively in several ways in comparison with the actual experimental results. Comparisons among these models are also performed to show their advantages and disadvantages. The method or verification system can be applied for other soft tissues for the finite element analysis of the soft tissue deformation. For brain tissue, an elasticity based model was proposed previously employing local elasticity and Poisson\u27s ratio. It is validated by intraoperative images to show more accurate prediction of brain deformation than the linear elastic model. FE analysis of brain ventricle shape changes was also performed to capture the dynamic variation of the ventricles in author\u27s other works. There, for the safety reasons, the images for brain deformation modeling were from Magnetic Resonance Imaging (MRI) scanning which have been used for brain scanning. The measurement process of material properties involves the tissue desiccation, machine limits, human operation errors, and time factors. The acquired material parameters from measurement devices may have some difference from the tissue used in real state of experiments. Therefore, an experimental and simulation based method to inversely evaluate the material parameters is proposed and compare

A surrogate model based on a finite element model of abdomen for real-time visualisation of tissue stress during physical examination training

Robotic patients show great potential to improve medical palpation training as they can provide feedback that cannot be obtained in a real patient. Providing information about internal organs deformation can significantly enhance palpation training by giving medical trainees visual insight based on their finger behaviours. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, thus able to provide real-time predictions. In this work, we proposed an innovative surrogate model of abdomen mechanics using machine learning (ML) and finite element (FE) modelling to virtually render internal tissue deformation in real-time. We first developed a new high-fidelity FE model of the abdomen mechanics from computerized tomography (CT) images. We performed palpation simulations to produce a large database of stress distribution on the liver edge, an area of interest in most examinations. We then used artificial neural networks (ANN) to develop the surrogate model and demonstrated its application in an experimental palpation platform. Our FE simulations took 1.5 hrs to predict stress distribution for each palpation while this only took a fraction of a second for the surrogate model. Our results show that the ANN has a 92.6% accuracy. We also show that the surrogate model is able to use the experimental input of palpation location and force to provide real-time projections onto the robotics platform. This enhanced robotics platform has potential to be used as a training simulator for trainees to hone their palpation skills

NOVEL STRATEGIES FOR THE MORPHOLOGICAL AND BIOMECHANICAL ANALYSIS OF THE CARDIAC VALVES BASED ON VOLUMETRIC CLINICAL IMAGES

This work was focused on the morphological and biomechanical analysis of the heart valves exploiting the volumetric data. Novel methods were implemented to perform cardiac valve structure and sub-structure segmentation by defining long axis planes evenly rotated around the long axis of the valve. These methods were exploited to successfully reconstruct the 3D geometry of the mitral, tricuspid and aortic valve structures. Firstly, the reconstructed models were used for the morphological analysis providing a detailed description of the geometry of the valve structures, also computing novel indexes that could improve the description of the valvular apparatus and help their clinical assessment. Additionally, the models obtained for the mitral valve complex were adopted for the development of a novel biomechanical approach to simulate the systolic closure of the valve, relying on highly-efficient mass-spring models thus obtaining a good trade-off between the accuracy and the computational cost of the numerical simulations. In specific: \u2022 First, an innovative and semi-automated method was implemented to generate the 3D model of the aortic valve and of its calcifications, to quantitively describe its 3D morphology and to compute the anatomical aortic valve area (AVA) based on multi-detector computed tomography images. The comparison of the obtained results vs. effective AVA measurements showed a good correlation. Additionally, these methods accounted for asymmetries or anatomical derangements, which would be difficult to correctly capture through either effective AVA or planimetric AVA. \u2022 Second, a tool to quantitively assess the geometry of the tricuspid valve during the cardiac cycle using multidetector CT was developed, in particular focusing on the 3D spatial relationship between the tricuspid annulus and the right coronary artery. The morphological analysis of the annulus and leaflets confirmed data reported in literature. The qualitative and quantitative analysis of the spatial relationship could standardize the analysis protocol and be pivotal in the procedure planning of the percutaneous device implantation that interact with the tricuspid annulus. \u2022 Third, we simulated the systolic closure of three patient specific mitral valve models, derived from CMR datasets, by means of the mass spring model approach. The comparison of the obtained results vs. finite element analyses (considered as the gold-standard) was performed tuning the parameters of the mass spring model, so to obtain the best trade-off between computational expense and accuracy of the results. A configuration mismatch between the two models lower than two times the in-plane resolution of starting imaging data was yielded using a mass spring model set-up that requires, on average, only ten minutes to simulate the valve closure. \u2022 Finally, in the last chapter, we performed a comprehensive analysis which aimed at exploring the morphological and mechanical changes induced by the myxomatous pathologies in the mitral valve tissue. The analysis of mitral valve thickness confirmed the data and patterns reported in literature, while the mechanical test accurately described the behavior of the pathological tissue. A preliminary implementation of this data into finite element simulations suggested that the use of more reliable patient-specific and pathology-specific characterization of the model could improve the realism and the accuracy of the biomechanical simulations

Real-time simulation of surgery by Proper Generalized Decomposition techniques

La simulación quirúrgica por ordenador en tiempo real se ha convertido en una alternativa muy atractiva a los simuladores quirúrgicos tradicionales. Entre otras ventajas, los simuladores por ordenador consiguen ahorros importantes de tiempo y de costes de mantenimiento, y permiten que los estudiantes practiquen sus habilidades quirúrgicas en un entorno seguro tantas veces como sea necesario. Sin embargo, a pesar de las capacidades de los ordenadores actuales, la cirugía computacional sigue siendo un campo de investigación exigente. Uno de sus mayores retos es la alta velocidad a la que se tienen que resolver complejos problemas de mecánica de medios continuos para que los interfaces hápticos puedan proporcionar un sentido del tacto realista (en general, se necesitan velocidades de respuesta de 500-1000 Hz).Esta tesis presenta algunos métodos numéricos novedosos para la simulación interactiva de dos procedimientos quirúrgicos habituales: el corte y el rasgado (o desgarro) de tejidos blandos. El marco común de los métodos presentados es el uso de la Descomposición Propia Generalizada (PGD en inglés) para la generación de vademécums computacionales, esto es, metasoluciones generales de problemas paramétricos de altas dimensiones que se pueden evaluar a velocidades de respuesta compatibles con entornos hápticos.En el caso del corte, los vademécums computacionales se utilizan de forma conjunta con técnicas basadas en XFEM, mientras que la carga de cálculo se distribuye entre una etapa off-line (previa a la ejecución interactiva) y otra on-line (en tiempo de ejecución). Durante la fase off-line, para el órgano en cuestión se precalculan tanto un vademécum computacional para cualquier posición de una carga, como los desplazamientos producidos por un conjunto de cortes. Así, durante la etapa on-line, los resultados precalculados se combinan de la forma más adecuada para obtener en tiempo real la respuesta a las acciones dirigidas por el usuario. En cuanto al rasgado, a partir de una ecuación paramétrica basada en mecánica del daño continuo, se obtiene un vademécum computacional. La complejidad del modelo se reduce mediante técnicas de Descomposición Ortogonal Propia (POD en inglés), y el vademécum se incorpora a una formulación incremental explícita que se puede interpretar como una especie de integrador temporal.A modo de ejemplo, el método para el corte se aplica a la simulación de un procedimiento quirúrgico refractivo de la córnea conocido como queratotomía radial, mientras que el método para el rasgado se centra en la simulación de la colecistectomía laparoscópica (la extirpación de la vesícula biliar mediante laparoscopia). En ambos casos, los métodos implementados ofrecen excelentes resultados en términos de velocidades de respuesta y producen simulaciones muy realistas desde los puntos de vista visual y háptico.The real-time computer-based simulation of surgery has proven to be an appealing alternative to traditional surgical simulators. Amongst other advantages, computer-based simulators provide considerable savings on time and maintenance costs, and allow trainees to practice their surgical skills in a safe environment as often as necessary. However, in spite of the current computer capabilities, computational surgery continues to be a challenging field of research. One of its major issues is the high speed at which complex problems in continuum mechanics have to be solved so that haptic interfaces can render a realistic sense of touch (generally, feedback rates of 500–1 000 Hz are required). This thesis introduces some novel numerical methods for the interactive simulation of two usual surgical procedures: cutting and tearing of soft tissues. The common framework of the presented methods is the use of the Proper Generalised Decomposition (PGD) for the generation of computational vademecums, i. e. general meta-solutions of parametric high-dimensional problems that can be evaluated at feedback rates compatible with haptic environments. In the case of cutting, computational vademecums are used jointly with XFEM-based techniques, and the computing workload is distributed into an off-line and an on-line stage. During the off-line stage, both a computational vademecum for any position of a load and the displacements produced by a set of cuts are pre-computed for the organ under consideration. Thus, during the on-line stage, the pre-computed results are properly combined together to obtain in real-time the response to the actions driven by the user. Concerning tearing, a computational vademecum is obtained from a parametric equation based on continuum damage mechanics. The complexity of the model is reduced by Proper Orthogonal Decomposition (POD) techniques, and the vademecum is incorporated into an explicit incremental formulation that can be viewed as a sort of time integrator. By way of example, the cutting method is applied to the simulation of a corneal refractive surgical procedure known as radial keratotomy, whereas the tearing method focuses on the simulation of laparoscopic cholecystectomy (i. e. the removal of the gallbladder). In both cases, the implemented methods offer excellent performances in terms of feedback rates, and produce.<br /

Performance Factors in Neurosurgical Simulation and Augmented Reality Image Guidance

Virtual reality surgical simulators have seen widespread adoption in an effort to provide safe, cost-effective and realistic practice of surgical skills. However, the majority of these simulators focus on training low-level technical skills, providing only prototypical surgical cases. For many complex procedures, this approach is deficient in representing anatomical variations that present clinically, failing to challenge users’ higher-level cognitive skills important for navigation and targeting. Surgical simulators offer the means to not only simulate any case conceivable, but to test novel approaches and examine factors that influence performance. Unfortunately, there is a void in the literature surrounding these questions. This thesis was motivated by the need to expand the role of surgical simulators to provide users with clinically relevant scenarios and evaluate human performance in relation to image guidance technologies, patient-specific anatomy, and cognitive abilities. To this end, various tools and methodologies were developed to examine cognitive abilities and knowledge, simulate procedures, and guide complex interventions all within a neurosurgical context. The first chapter provides an introduction to the material. The second chapter describes the development and evaluation of a virtual anatomical training and examination tool. The results suggest that learning occurs and that spatial reasoning ability is an important performance predictor, but subordinate to anatomical knowledge. The third chapter outlines development of automation tools to enable efficient simulation studies and data management. In the fourth chapter, subjects perform abstract targeting tasks on ellipsoid targets with and without augmented reality guidance. While the guidance tool improved accuracy, performance with the tool was strongly tied to target depth estimation – an important consideration for implementation and training with similar guidance tools. In the fifth chapter, neurosurgically experienced subjects were recruited to perform simulated ventriculostomies. Results showed anatomical variations influence performance and could impact outcome. Augmented reality guidance showed no marked improvement in performance, but exhibited a mild learning curve, indicating that additional training may be warranted. The final chapter summarizes the work presented. Our results and novel evaluative methodologies lay the groundwork for further investigation into simulators as versatile research tools to explore performance factors in simulated surgical procedures

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