Biomechanical Modeling for Lung Tumor Motion Prediction during Brachytherapy and Radiotherapy

A novel technique is proposed to develop a biomechanical model for estimating lung’s tumor position as a function of respiration cycle time. Continuous tumor motion is a major challenge in lung cancer treatment techniques where the tumor needs to be targeted; e.g. in external beam radiotherapy and brachytherapy. If not accounted for, this motion leads to areas of radiation over and/or under dosage for normal tissue and tumors. In this thesis, biomechanical models were developed for lung tumor motion predication in two distinct cases of lung brachytherapy and lung external beam radiotherapy. The lung and other relevant surrounding organs geometries, loading, boundary conditions and mechanical properties were considered and incorporated properly for each case. While using material model with constant incompressibility is sufficient to model the lung tissue in the brachytherapy case, in external beam radiation therapy the tissue incompressibility varies significantly due to normal breathing. One of the main issues tackled in this research is characterizing lung tissue incompressibility variations and measuring its corresponding parameters as a function of respiration cycle time. Results obtained from an ex-vivo porcine deflated lung indicated feasibility and reliability of using the developed biomechanical model to predict tumor motion during brachytherapy. For external beam radiotherapy, in-silico studies indicated very significant impact of considering the lung tissue incompressibility on the accuracy of predicting tumor motion. Furthermore, ex-vivo porcine lung experiments demonstrated the capability and reliability of the proposed approach for predicting tumor motion as a function of cyclic time. As such, the proposed models have a good potential to be incorporated effectively in computer assisted lung radiotherapy treatment systems

Computational ultrasound tissue characterisation for brain tumour resection

In brain tumour resection, it is vital to know where critical neurovascular structuresand tumours are located to minimise surgical injuries and cancer recurrence. Theaim of this thesis was to improve intraoperative guidance during brain tumourresection by integrating both ultrasound standard imaging and elastography in thesurgical workflow. Brain tumour resection requires surgeons to identify the tumourboundaries to preserve healthy brain tissue and prevent cancer recurrence. Thisthesis proposes to use ultrasound elastography in combination with conventionalultrasound B-mode imaging to better characterise tumour tissue during surgery.Ultrasound elastography comprises a set of techniques that measure tissue stiffness,which is a known biomarker of brain tumours. The objectives of the researchreported in this thesis are to implement novel learning-based methods for ultrasoundelastography and to integrate them in an image-guided intervention framework.Accurate and real-time intraoperative estimation of tissue elasticity can guide towardsbetter delineation of brain tumours and improve the outcome of neurosurgery. We firstinvestigated current challenges in quasi-static elastography, which evaluates tissuedeformation (strain) by estimating the displacement between successive ultrasoundframes, acquired before and after applying manual compression. Recent approachesin ultrasound elastography have demonstrated that convolutional neural networkscan capture ultrasound high-frequency content and produce accurate strain estimates.We proposed a new unsupervised deep learning method for strain prediction, wherethe training of the network is driven by a regularised cost function, composed of asimilarity metric and a regularisation term that preserves displacement continuityby directly optimising the strain smoothness. We further improved the accuracy of our method by proposing a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. We then demonstrateinitial results towards extending our ultrasound displacement estimation method toshear wave elastography, which provides a quantitative estimation of tissue stiffness.Furthermore, this thesis describes the development of an open-source image-guidedintervention platform, specifically designed to combine intra-operative ultrasoundimaging with a neuronavigation system and perform real-time ultrasound tissuecharacterisation. The integration was conducted using commercial hardware andvalidated on an anatomical phantom. Finally, preliminary results on the feasibilityand safety of the use of a novel intraoperative ultrasound probe designed for pituitarysurgery are presented. Prior to the clinical assessment of our image-guided platform,the ability of the ultrasound probe to be used alongside standard surgical equipmentwas demonstrated in 5 pituitary cases

Towards markerless orthopaedic navigation with intuitive Optical See-through Head-mounted displays

The potential of image-guided orthopaedic navigation to improve surgical outcomes has been well-recognised during the last two decades. According to the tracked pose of target bone, the anatomical information and preoperative plans are updated and displayed to surgeons, so that they can follow the guidance to reach the goal with higher accuracy, efficiency and reproducibility. Despite their success, current orthopaedic navigation systems have two main limitations: for target tracking, artificial markers have to be drilled into the bone and calibrated manually to the bone, which introduces the risk of additional harm to patients and increases operating complexity; for guidance visualisation, surgeons have to shift their attention from the patient to an external 2D monitor, which is disruptive and can be mentally stressful. Motivated by these limitations, this thesis explores the development of an intuitive, compact and reliable navigation system for orthopaedic surgery. To this end, conventional marker-based tracking is replaced by a novel markerless tracking algorithm, and the 2D display is replaced by a 3D holographic Optical see-through (OST) Head-mounted display (HMD) precisely calibrated to a user's perspective. Our markerless tracking, facilitated by a commercial RGBD camera, is achieved through deep learning-based bone segmentation followed by real-time pose registration. For robust segmentation, a new network is designed and efficiently augmented by a synthetic dataset. Our segmentation network outperforms the state-of-the-art regarding occlusion-robustness, device-agnostic behaviour, and target generalisability. For reliable pose registration, a novel Bounded Iterative Closest Point (BICP) workflow is proposed. The improved markerless tracking can achieve a clinically acceptable error of 0.95 deg and 2.17 mm according to a phantom test. OST displays allow ubiquitous enrichment of perceived real world with contextually blended virtual aids through semi-transparent glasses. They have been recognised as a suitable visual tool for surgical assistance, since they do not hinder the surgeon's natural eyesight and require no attention shift or perspective conversion. The OST calibration is crucial to ensure locational-coherent surgical guidance. Current calibration methods are either human error-prone or hardly applicable to commercial devices. To this end, we propose an offline camera-based calibration method that is highly accurate yet easy to implement in commercial products, and an online alignment-based refinement that is user-centric and robust against user error. The proposed methods are proven to be superior to other similar State-of- the-art (SOTA)s regarding calibration convenience and display accuracy. Motivated by the ambition to develop the world's first markerless OST navigation system, we integrated the developed markerless tracking and calibration scheme into a complete navigation workflow designed for femur drilling tasks during knee replacement surgery. We verify the usability of our designed OST system with an experienced orthopaedic surgeon by a cadaver study. Our test validates the potential of the proposed markerless navigation system for surgical assistance, although further improvement is required for clinical acceptance.Open Acces

Non-Rigid Liver Registration for Laparoscopy using Data-Driven Biomechanical Models

During laparoscopic liver resection, the limited access to the organ, the small field of view and lack of palpation can obstruct a surgeon’s workflow. Automatic navigation systems could use the images from preoperative volumetric organ scans to help the surgeons find their target (tumors) and risk-structures (vessels) more efficiently. This requires the preoperative data to be fused (or registered) with the intraoperative scene in order to display information at the correct intraoperative position. One key challenge in this setting is the automatic estimation of the organ’s current intra-operative deformation, which is required in order to predict the position of internal structures. Parameterizing the many patient-specific unknowns (tissue properties, boundary conditions, interactions with other tissues, direction of gravity) is very difficult. Instead, this work explores how to employ deep neural networks to solve the registration problem in a data-driven manner. To this end, convolutional neural networks are trained on synthetic data to estimate an organ’s intraoperative displacement field and thus its current deformation. To drive this estimation, visible surface cues from the intraoperative camera view must be supplied to the networks. Since reliable surface features are very difficult to find, the networks are adapted to also find correspondences between the pre- and intraoperative liver geometry automatically. This combines the search for correspondences with the biomechanical behavior estimation and allows the networks to tackle the full non-rigid registration problem in one single step. The result is a model which can quickly predict the volume deformation of a liver, given only sparse surface information. The model combines the advantages of a physically accurate biomechanical simulation with the speed and powerful feature extraction capabilities of deep neural networks. To test the method intraoperatively, a registration pipeline is developed which constructs a map of the liver and its surroundings from the laparoscopic video and then uses the neural networks to fuse the preoperative volume data into this map. The deformed organ volume can then be rendered as an overlay directly onto the laparoscopic video stream. The focus of this pipeline is to be applicable to real surgery, where everything should be quick and non-intrusive. To meet these requirements, a SLAM system is used to localize the laparoscopic camera (avoiding setup of an external tracking system), various neural networks are used to quickly interpret the scene and semi-automatic tools let the surgeons guide the system. Beyond the concrete advantages of the data-driven approach for intraoperative registration, this work also demonstrates general benefits of training a registration system preoperatively on synthetic data. The method lets the engineer decide which values need to be known explicitly and which should be estimated implicitly by the networks, which opens the door to many new possibilities.:1 Introduction 1.1 Motivation 1.1.1 Navigated Liver Surgery 1.1.2 Laparoscopic Liver Registration 1.2 Challenges in Laparoscopic Liver Registration 1.2.1 Preoperative Model 1.2.2 Intraoperative Data 1.2.3 Fusion/Registration 1.2.4 Data 1.3 Scope and Goals of this Work 1.3.1 Data-Driven, Biomechanical Model 1.3.2 Data-Driven Non-Rigid Registration 1.3.3 Building a Working Prototype 2 State of the Art 2.1 Rigid Registration 2.2 Non-Rigid Liver Registration 2.3 Neural Networks for Simulation and Registration 3 Theoretical Background 3.1 Liver 3.2 Laparoscopic Liver Resection 3.2.1 Staging Procedure 3.3 Biomechanical Simulation 3.3.1 Physical Balance Principles 3.3.2 Material Models 3.3.3 Numerical Solver: The Finite Element Method (FEM) 3.3.4 The Lagrangian Specification 3.4 Variables and Data in Liver Registration 3.4.1 Observable 3.4.2 Unknowns 4 Generating Simulations of Deforming Organs 4.1 Organ Volume 4.2 Forces and Boundary Conditions 4.2.1 Surface Forces 4.2.2 Zero-Displacement Boundary Conditions 4.2.3 Surrounding Tissues and Ligaments 4.2.4 Gravity 4.2.5 Pressure 4.3 Simulation 4.3.1 Static Simulation 4.3.2 Dynamic Simulation 4.4 Surface Extraction 4.4.1 Partial Surface Extraction 4.4.2 Surface Noise 4.4.3 Partial Surface Displacement 4.5 Voxelization 4.5.1 Voxelizing the Liver Geometry 4.5.2 Voxelizing the Displacement Field 4.5.3 Voxelizing Boundary Conditions 4.6 Pruning Dataset - Removing Unwanted Results 4.7 Data Augmentation 5 Deep Neural Networks for Biomechanical Simulation 5.1 Training Data 5.2 Network Architecture 5.3 Loss Functions and Training 6 Deep Neural Networks for Non-Rigid Registration 6.1 Training Data 6.2 Architecture 6.3 Loss 6.4 Training 6.5 Mesh Deformation 6.6 Example Application 7 Intraoperative Prototype 7.1 Image Acquisition 7.2 Stereo Calibration 7.3 Image Rectification, Disparity- and Depth- estimation 7.4 Liver Segmentation 7.4.1 Synthetic Image Generation 7.4.2 Automatic Segmentation 7.4.3 Manual Segmentation Modifier 7.5 SLAM 7.6 Dense Reconstruction 7.7 Rigid Registration 7.8 Non-Rigid Registration 7.9 Rendering 7.10 Robotic Operating System 8 Evaluation 8.1 Evaluation Datasets 8.1.1 In-Silico 8.1.2 Phantom Torso and Liver 8.1.3 In-Vivo, Human, Breathing Motion 8.1.4 In-Vivo, Human, Laparoscopy 8.2 Metrics 8.2.1 Mean Displacement Error 8.2.2 Target Registration Error (TRE) 8.2.3 Champfer Distance 8.2.4 Volumetric Change 8.3 Evaluation of the Synthetic Training Data 8.4 Data-Driven Biomechanical Model (DDBM) 8.4.1 Amount of Intraoperative Surface 8.4.2 Dynamic Simulation 8.5 Volume to Surface Registration Network (V2S-Net) 8.5.1 Amount of Intraoperative Surface 8.5.2 Dependency on Initial Rigid Alignment 8.5.3 Registration Accuracy in Comparison to Surface Noise 8.5.4 Registration Accuracy in Comparison to Material Stiffness 8.5.5 Champfer-Distance vs. Mean Displacement Error 8.5.6 In-vivo, Human Breathing Motion 8.6 Full Intraoperative Pipeline 8.6.1 Intraoperative Reconstruction: SLAM and Intraoperative Map 8.6.2 Full Pipeline on Laparoscopic Human Data 8.7 Timing 9 Discussion 9.1 Intraoperative Model 9.2 Physical Accuracy 9.3 Limitations in Training Data 9.4 Limitations Caused by Difference in Pre- and Intraoperative Modalities 9.5 Ambiguity 9.6 Intraoperative Prototype 10 Conclusion 11 List of Publications List of Figures Bibliograph

Técnicas de coste reducido para el posicionamiento del paciente en radioterapia percutánea utilizando un sistema de imágenes ópticas

Patient positioning is an important part of radiation therapy which is one of the main solutions for the treatment of malignant tissue in the human body. Currently, the most common patient positioning methods expose healthy tissue of the patient's body to extra dangerous radiations. Other non-invasive positioning methods are either not very accurate or are very costly for an average hospital. In this thesis, we explore the possibility of developing a system comprised of affordable hardware and advanced computer vision algorithms that facilitates patient positioning. Our algorithms are based on the usage of affordable RGB-D sensors, image features, ArUco planar markers, and other geometry registration methods. Furthermore, we take advantage of consumer-level computing hardware to make our systems widely accessible. More specifically, we avoid the usage of approaches that need to take advantage of dedicated GPU hardware for general-purpose computing since they are more costly. In different publications, we explore the usage of the mentioned tools to increase the accuracy of reconstruction/localization of the patient in its pose. We also take into account the visualization of the patient's target position with respect to their current position in order to assist the person who performs patient positioning. Furthermore, we make usage of augmented reality in conjunction with a real-time 3D tracking algorithm for better interaction between the program and the operator. We also solve more fundamental problems about ArUco markers that could be used in the future to improve our systems. These include highquality multi-camera calibration and mapping using ArUco markers plus detection of these markers in event cameras which are very useful in the presence of fast camera movement. In the end, we conclude that it is possible to increase the accuracy of 3D reconstruction and localization by combining current computer vision algorithms with fiducial planar markers with RGB-D sensors. This is reflected in the low amount of error we have achieved in our experiments for patient positioning, pushing forward the state of the art for this application.En el tratamiento de tumores malignos en el cuerpo, el posicionamiento del paciente en las sesiones de radioterapia es una cuestión crucial. Actualmente, los métodos más comunes de posicionamiento del paciente exponen tejido sano del mismo a radiaciones peligrosas debido a que no es posible asegurar que la posición del paciente siempre sea la misma que la que tuvo cuando se planificó la zona a radiar. Los métodos que se usan actualmente, o no son precisos o tienen costes que los hacen inasequibles para ser usados en hospitales con financiación limitada. En esta Tesis hemos analizado la posibilidad de desarrollar un sistema compuesto por hardware de bajo coste y métodos avanzados de visión por ordenador que ayuden a que el posicionamiento del paciente sea el mismo en las diferentes sesiones de radioterapia, con respecto a su pose cuando fue se planificó la zona a radiar. La solución propuesta como resultado de la Tesis se basa en el uso de sensores RGB-D, características extraídas de la imagen, marcadores cuadrados denominados ArUco y métodos de registro de la geometría en la imagen. Además, en la solución propuesta, se aprovecha la existencia de hardware convencional de bajo coste para hacer nuestro sistema ampliamente accesible. Más específicamente, evitamos el uso de enfoques que necesitan aprovechar GPU, de mayores costes, para computación de propósito general. Se han obtenido diferentes publicaciones para conseguir el objetivo final. Las mismas describen métodos para aumentar la precisión de la reconstrucción y la localización del paciente en su pose, teniendo en cuenta la visualización de la posición ideal del paciente con respecto a su posición actual, para ayudar al profesional que realiza la colocación del paciente. También se han propuesto métodos de realidad aumentada junto con algoritmos para seguimiento 3D en tiempo real para conseguir una mejor interacción entre el sistema ideado y el profesional que debe realizar esa labor. De forma añadida, también se han propuesto soluciones para problemas fundamentales relacionados con el uso de marcadores cuadrados que han sido utilizados para conseguir el objetivo de la Tesis. Las soluciones propuestas pueden ser empleadas en el futuro para mejorar otros sistemas. Los problemas citados incluyen la calibración y el mapeo multicámara de alta calidad utilizando los marcadores y la detección de estos marcadores en cámaras de eventos, que son muy útiles en presencia de movimientos rápidos de la cámara. Al final, concluimos que es posible aumentar la precisión de la reconstrucción y localización en 3D combinando los actuales algoritmos de visión por ordenador, que usan marcadores cuadrados de referencia, con sensores RGB-D. Los resultados obtenidos con respecto al error que el sistema obtiene al reproducir el posicionamiento del paciente suponen un importante avance en el estado del arte de este tópico

Medical SLAM in an autonomous robotic system

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects

Medical SLAM in an autonomous robotic system

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects

Technologies for Biomechanically-Informed Image Guidance of Laparoscopic Liver Surgery

Laparoscopic surgery for liver resection has a number medical advantages over open surgery, but also comes with inherent technical challenges. The surgeon only has a very limited field of view through the imaging modalities routinely employed intra-operatively, laparoscopic video and ultrasound, and the pneumoperitoneum required to create the operating space and gaining access to the organ can significantly deform and displace the liver from its pre-operative configuration. This can make relating what is visible intra-operatively to the pre-operative plan and inferring the location of sub-surface anatomy a very challenging task. Image guidance systems can help overcome these challenges by updating the pre-operative plan to the situation in theatre and visualising it in relation to the position of surgical instruments. In this thesis, I present a series of contributions to a biomechanically-informed image-guidance system made during my PhD. The most recent one is work on a pipeline for the estimation of the post-insufflation configuration of the liver by means of an algorithm that uses a database of segmented training images of patient abdomens where the post-insufflation configuration of the liver is known. The pipeline comprises an algorithm for inter and intra-subject registration of liver meshes by means of non-rigid spectral point-correspondence finding. My other contributions are more fundamental and less application specific, and are all contained and made available to the public in the NiftySim open-source finite element modelling package. Two of my contributions to NiftySim are of particular interest with regards to image guidance of laparoscopic liver surgery: 1) a novel general purpose contact modelling algorithm that can be used to simulate contact interactions between, e.g., the liver and surrounding anatomy; 2) membrane and shell elements that can be used to, e.g., simulate the Glisson capsule that has been shown to significantly influence the organ’s measured stiffness