Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive 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 capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Neurosurgery and brain shift: review of the state of the art and main contributions of robotics

Este artículo presenta una revisión acerca de la neurocirugía, los asistentes robóticos en este tipo de procedimiento, y el tratamiento que se le da al problema del desplazamiento que sufre el tejido cerebral, incluyendo las técnicas para la obtención de imágenes médicas. Se abarca de manera especial el fenómeno del desplazamiento cerebral, comúnmente conocido como brain shift, el cual causa pérdida de referencia entre las imágenes preoperatorias y los volúmenes a tratar durante la cirugía guiada por imágenes médicas. Hipotéticamente, con la predicción y corrección del brain shift sobre el sistema de neuronavegación, se podrían planear y seguir trayectorias de mínima invasión, lo que conllevaría a minimizar el daño a los tejidos funcionales y posiblemente a reducir la morbilidad y mortalidad en estos delicados y exigentes procedimientos médicos, como por ejemplo, en la extirpación de un tumor cerebral. Se mencionan también otros inconvenientes asociados a la neurocirugía y se muestra cómo los sistemas robotizados han ayudado a solventar esta problemática. Finalmente se ponen en relieve las perspectivas futuras de esta rama de la medicina, la cual desde muchas disciplinas busca tratar las dolencias del principal órgano del ser humano.This paper presents a review about neurosurgery, robotic assistants in this type of procedure, and the approach to the problem of brain tissue displacement, including techniques for obtaining medical images. It is especially focused on the phenomenon of brain displacement, commonly known as brain shift, which causes a loss of reference between the preoperative images and the volumes to be treated during image-guided surgery. Hypothetically, with brain shift prediction and correction for the neuronavigation system, minimal invasion trajectories could be planned and shortened. This would reduce damage to functional tissues and possibly lower the morbidity and mortality in delicate and demanding medical procedures such as the removal of a brain tumor. This paper also mentions other issues associated with neurosurgery and shows the way robotized systems have helped solve these problems. Finally, it highlights the future perspectives of neurosurgery, a branch of medicine that seeks to treat the ailments of the main organ of the human body from the perspective of many disciplines

Brain Shift Correction Based on a Boundary Element Biomechanical Model with Different Material Properties

Neuronavigation systems are usually subject to inaccuracy due to intraoperative changes like brain shift or tumor resection. In order to correct for these deformations a biomechanical model of the brain is proposed. Not only elastic tissues, but also fluids are modeled, since an important volume of the head contains cerebrospinal fluid, which does not behave like soft tissues. Unlike other approaches, we propose to solve the differential equations of the model by means of the boundary element method, which has the advantage of only considering the boundaries of the different biomechanically homogeneous regions. The size of the matrix to invert is therefore drastically reduced. Finally, our method is assessed with sequences of intraoperative MR images, showing better performances for the elastic/fluid model than for the purely elastic one

Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery

Intraoperative brain shift during neurosurgical procedures is a well-known phenomenon caused by gravity, tissue manipulation, tumor size, loss of cerebrospinal fluid (CSF), and use of medication. For the use of image-guided systems, this phenomenon greatly affects the accuracy of the guidance. During the last several decades, researchers have investigated how to overcome this problem. The purpose of this paper is to present a review of publications concerning different aspects of intraoperative brain shift especially in a tumor resection surgery such as intraoperative imaging systems, quantification, measurement, modeling, and registration techniques. Clinical experience of using intraoperative imaging modalities, details about registration, and modeling methods in connection with brain shift in tumor resection surgery are the focuses of this review. In total, 126 papers regarding this topic are analyzed in a comprehensive summary and are categorized according to fourteen criteria. The result of the categorization is presented in an interactive web tool. The consequences from the categorization and trends in the future are discussed at the end of this work

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

Real-time Biomechanical Modeling for Intraoperative Soft Tissue Registration

Computer assisted surgery systems intraoperatively support the surgeon by providing information on the location of hidden risk and target structures during surgery. However, soft tissue deformations make intraoperative registration (and thus intraoperative navigation) difficult. In this work, a novel, biomechanics based approach for real-time soft tissue registration from sparse intraoperative sensor data such as stereo endoscopic images is presented to overcome this problem

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