Image guided constitutive modeling of the brain tissue

Issued as final reportUnited States. Department of Health and Human Service

Image-Guided Abdominal Surgery and Therapy Delivery

ABSTRACT Image-Guided Surgery has become the standard of care in intracranial neurosurgery providing more exact resections while minimizing damage to healthy tissue. Moving that process to abdominal organs presents additional challenges in the form of image segmentation, image to physical space registration, organ motion and deformation. In this paper, we present methodologies and results for addressing these challenges in two specific organs: the liver and the kidney

État de l'art des méthodes de correction des déformations cérébrales per-opératoires

L'utilisation croissante de systèmes de navigation pour l'aide à la chirurgie a permis de faciliter les interventions ainsi que la planification des gestes chirurgicaux. Néanmoins, dans le cas de la neurochirurgie où le geste opératoire doit être très précis, les systèmes actuels sont limités à cause de déformations per-opératoires nommées ``Brain Shift''. Le terme de 'Brain Shift' traduit le mouvement des structures cérébrales arrivant après ouverture de la boite crânienne (jusqu'à 25mm). Le recalage rigide réalisé par le système de neuronavigation entre les examens préopératoires et la position du patient en salle d'opération est donc entaché d'une imprécision. Ainsi, les informations fournies par le système de navigation deviennent partiellement obsolètes. Ce document propose une présentation des différentes techniques de mesure et de compensation du 'Brain Shift'. Les avantages et inconvénients de chaque approche seront soulignés avant de conclure par une brève présentation des méthodes de validation existantes. / Navigation systems become a very attractive tool in surgical planning and procedure. However, the accuracy and usefulness of such systems is limited in presence of soft-tissue deformations. In neurosurgery, this phenomenon is called ``Brain Shift''. The ``Brain shift'' is the motion of cerebral structures occurring after the craniotomy (up to 25mm). The neuronavigation system matches rigidly the pre-operative images with the surgical field. The hypothesis of a rigid registration is no longer valid because of deformations. This document presents a survey with classification of published methods to measure and compensate for the brain shift. The various validation framework are also presented

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

Advanced Endoscopic Navigation:Surgical Big Data,Methodology,and Applications

随着科学技术的飞速发展，健康与环境问题日益成为人类面临的最重大问题之一。信息科学、计算机技术、电子工程与生物医学工程等学科的综合应用交叉前沿课题，研究现代工程技术方法，探索肿瘤癌症等疾病早期诊断、治疗和康复手段。本论文综述了计算机辅助微创外科手术导航、多模态医疗大数据、方法论及其临床应用：从引入微创外科手术导航概念出发，介绍了医疗大数据的术前与术中多模态医学成像方法、阐述了先进微创外科手术导航的核心流程包括计算解剖模型、术中实时导航方案、三维可视化方法及交互式软件技术，归纳了各类微创外科手术方法的临床应用。同时，重点讨论了全球各种手术导航技术在临床应用中的优缺点，分析了目前手术导航领域内的最新技术方法。在此基础上，提出了微创外科手术方法正向数字化、个性化、精准化、诊疗一体化、机器人化以及高度智能化的发展趋势。【Abstract】Interventional endoscopy (e.g., bronchoscopy, colonoscopy, laparoscopy, cystoscopy) is a widely performed procedure that involves either diagnosis of suspicious lesions or guidance for minimally invasive surgery in a variety of organs within the body cavity. Endoscopy may also be used to guide the introduction of certain items (e.g., stents) into the body. Endoscopic navigation systems seek to integrate big data with multimodal information (e.g., computed tomography, magnetic resonance images, endoscopic video sequences, ultrasound images, external trackers) relative to the patient's anatomy, control the movement of medical endoscopes and surgical tools, and guide the surgeon's actions during endoscopic interventions. Nevertheless, it remains challenging to realize the next generation of context-aware navigated endoscopy. This review presents a broad survey of various aspects of endoscopic navigation, particularly with respect to the development of endoscopic navigation techniques. First, we investigate big data with multimodal information involved in endoscopic navigation. Next, we focus on numerous methodologies used for endoscopic navigation. We then review different endoscopic procedures in clinical applications. Finally, we discuss novel techniques and promising directions for the development of endoscopic navigation.X.L. acknowledges funding from the Fundamental Research Funds for the Central Universities. T.M.P. acknowledges funding from the Canadian Foundation for Innovation, the Canadian Institutes for Health Research, the National Sciences and Engineering Research Council of Canada, and a grant from Intuitive Surgical Inc

Multi-Modal Partial Surface Matching for Intra-Operative Registration

An important task for computer-assisted surgical interventions is the alignment of pre- and intra-operative spaces allowing the transfer of pre-operative information to the current patient situation, known as intra-operative registration. Registration is usually performed by using markers or image-based techniques. Another approach is the intra-operative acquisition of organ surfaces by 3D range scanners, which are then matched to pre-operatively generated surfaces. However, this approach is not trivial, as methods for intra-operative surface matching must be able to deal with noise, distortions, deformations, and the availability of only partially overlapping, nearly flat surfaces. For these reasons, surface matching for intra-operative registration has so far only been used to account for displacements that occur in local scales, while the actual alignment is still performed manually. The main contributions of this thesis are two different approaches for automatic surface matching in intra-operative environments. The focus here is the registration of surfaces acquired by different modalities, dealing with the aforementioned issues and without relying on unique landmarks. For the first approach, surfaces are converted to graph representations and correspondences between them are identified by means of graph matching. Graphs are obtained automatically by segmenting the surfaces into regions with similar properties. As the graph matching problem is known to be NP-hard, it was solved by iteratively computing node similarity scores, and converting it to a linear assignment problem. In the second approach, correspondences are identified by the selection of two spatial configurations of landmarks that can be better fitted to each other, according to an error metric. This error metric does not only incorporate a fitting error, but also a new measure for spatial configuration reliability. The optimization problem is solved by means of a greedy algorithm. Evaluation of the two approaches was performed with several experiments, simulating intra-operative conditions. While the graph matching approach proved to be robust for the registration of small partial data, the point-based approach proved to be more reliable for noisy surfaces. Apart from being a significant contribution to the field of feature-less partial surface matching, this work represents a great effort towards the achievement of a fully automatic, marker-less, registration system for computer-assisted surgery guidance