Registration of ultrasound volumes based on Euclidean distance transform

During neurosurgical operations, surgeons can decide to acquire intraoperative data to better proceed with the removal of a tumor. A valid option is given by ultrasound (US) imaging, which can be easily obtained at subsequent surgical stages, giving therefore multiple updates of the resection cavity. To improve the efficacy of the intraoperative guidance, neurosurgeons may benefit from having a direct correspondence between anatomical structures identified at different US acquisitions. In this context, the commonly available neuronavigation systems already provide registration methods, which however are not enough accurate to overcome the anatomical changes happening during resection. Therefore, our aim with this work is to improve the registration of intraoperative US volumes. In the proposed methodology, first a distance mapping of automatically segmented anatomical structures is computed and then the transformed images are utilized in the registration step. Our solution is tested on a public dataset of 17 cases, where the average landmark registration error between volumes acquired at the beginning and at the end of neurosurgical procedures is reduced from 3.55mm to 1.27mm

A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation

A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Developing such a registration method is very challenging, due to factors such as missing correspondence in images, the complexity of brain pathology and the demand for fast computation. We propose a novel feature-driven active framework. Here, landmarks and their displacement are first estimated from a pair of US images using corresponding local image features. Subsequently, a Gaussian Process (GP) model is used to interpolate a dense deformation field from the sparse landmarks. Kernels of the GP are estimated by using variograms and a discrete grid search method. If necessary, the user can actively add new landmarks based on the image context and visualization of the uncertainty measure provided by the GP to further improve the result. We retrospectively demonstrate our registration framework as a robust and accurate brain shift compensation solution on clinical data acquired during neurosurgery

The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation

With the recent developments in machine learning and modern graphics processing units (GPUs), there is a marked shift in the way intra-operative ultrasound (iUS) images can be processed and presented during surgery. Real-time processing of images to highlight important anatomical structures combined with in-situ display, has the potential to greatly facilitate the acquisition and interpretation of iUS images when guiding an operation. In order to take full advantage of the recent advances in machine learning, large amounts of high-quality annotated training data are necessary to develop and validate the algorithms. To ensure efficient collection of a sufficient number of patient images and external validity of the models, training data should be collected at several centers by different neurosurgeons, and stored in a standard format directly compatible with the most commonly used machine learning toolkits and libraries. In this paper, we argue that such effort to collect and organize large-scale multi-center datasets should be based on common open source software and databases. We first describe the development of existing open-source ultrasound based neuronavigation systems and how these systems have contributed to enhanced neurosurgical guidance over the last 15 years. We review the impact of the large number of projects worldwide that have benefited from the publicly available datasets “Brain Images of Tumors for Evaluation” (BITE) and “Retrospective evaluation of Cerebral Tumors” (RESECT) that include MR and US data from brain tumor cases. We also describe the need for continuous data collection and how this effort can be organized through the use of a well-adapted and user-friendly open-source software platform that integrates both continually improved guidance and automated data collection functionalities.publishedVersio

Neurosurgical Ultrasound Pose Estimation Using Image-Based Registration and Sensor Fusion - A Feasibility Study

Modern neurosurgical procedures often rely on computer-assisted real-time guidance using multiple medical imaging modalities. State-of-the-art commercial products enable the fusion of pre-operative with intra-operative images (e.g., magnetic resonance [MR] with ultrasound [US] images), as well as the on-screen visualization of procedures in progress. In so doing, US images can be employed as a template to which pre-operative images can be registered, to correct for anatomical changes, to provide live-image feedback, and consequently to improve confidence when making resection margin decisions near eloquent regions during tumour surgery. In spite of the potential for tracked ultrasound to improve many neurosurgical procedures, it is not widely used. State-of-the-art systems are handicapped by optical tracking’s need for consistent line-of-sight, keeping tracked rigid bodies clean and rigidly fixed, and requiring a calibration workflow. The goal of this work is to improve the value offered by co-registered ultrasound images without the workflow drawbacks of conventional systems. The novel work in this thesis includes: the exploration and development of a GPU-enabled 2D-3D multi-modal registration algorithm based on the existing LC2 metric; and the use of this registration algorithm in the context of a sensor and image-fusion algorithm. The work presented here is a motivating step in a vision towards a heterogeneous tracking framework for image-guided interventions where the knowledge from intraoperative imaging, pre-operative imaging, and (potentially disjoint) wireless sensors in the surgical field are seamlessly integrated for the benefit of the surgeon. The technology described in this thesis, inspired by advances in robot localization demonstrate how inaccurate pose data from disjoint sources can produce a localization system greater than the sum of its parts

Intraoperative Navigation Systems for Image-Guided Surgery

Recent technological advancements in medical imaging equipment have resulted in a dramatic improvement of image accuracy, now capable of providing useful information previously not available to clinicians. In the surgical context, intraoperative imaging provides a crucial value for the success of the operation. Many nontrivial scientific and technical problems need to be addressed in order to efficiently exploit the different information sources nowadays available in advanced operating rooms. In particular, it is necessary to provide: (i) accurate tracking of surgical instruments, (ii) real-time matching of images from different modalities, and (iii) reliable guidance toward the surgical target. Satisfying all of these requisites is needed to realize effective intraoperative navigation systems for image-guided surgery. Various solutions have been proposed and successfully tested in the field of image navigation systems in the last ten years; nevertheless several problems still arise in most of the applications regarding precision, usability and capabilities of the existing systems. Identifying and solving these issues represents an urgent scientific challenge. This thesis investigates the current state of the art in the field of intraoperative navigation systems, focusing in particular on the challenges related to efficient and effective usage of ultrasound imaging during surgery. The main contribution of this thesis to the state of the art are related to: Techniques for automatic motion compensation and therapy monitoring applied to a novel ultrasound-guided surgical robotic platform in the context of abdominal tumor thermoablation. Novel image-fusion based navigation systems for ultrasound-guided neurosurgery in the context of brain tumor resection, highlighting their applicability as off-line surgical training instruments. The proposed systems, which were designed and developed in the framework of two international research projects, have been tested in real or simulated surgical scenarios, showing promising results toward their application in clinical practice

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

Validation of a semi-automatic co-registration of MRI scans in patients with brain tumors during treatment follow-up.

There is an expanding research interest in high-grade gliomas because of their significant population burden and poor survival despite the extensive standard multimodal treatment. One of the obstacles is the lack of individualized monitoring of tumor characteristics and treatment response before, during and after treatment. We have developed a two-stage semi-automatic method to co-register MRI scans at different time points before and after surgical and adjuvant treatment of high-grade gliomas. This two-stage co-registration includes a linear co-registration of the semi-automatically derived mask of the preoperative contrast-enhancing area or postoperative resection cavity, brain contour and ventricles between different time points. The resulting transformation matrix was then applied in a non-linear manner to co-register conventional contrast-enhanced T1 -weighted images. Targeted registration errors were calculated and compared with linear and non-linear co-registered images. Targeted registration errors were smaller for the semi-automatic non-linear co-registration compared with both the non-linear and linear co-registered images. This was further visualized using a three-dimensional structural similarity method. The semi-automatic non-linear co-registration allowed for optimal correction of the variable brain shift at different time points as evaluated by the minimal targeted registration error. This proposed method allows for the accurate evaluation of the treatment response, essential for the growing research area of brain tumor imaging and treatment response evaluation in large sets of patients. Copyright © 2016 John Wiley & Sons, Ltd.This research was funded by a National Institute of Health Clinician Scientist Fellowship [SJP], a Remmert Adriaan Laan Fund [AH], a René Vogels Fund [AH] and a grant from the Chang Gung Medical Foundation and Chang Gung Memorial Hospital, Keelung [JLY]. None of the authors have financial of other conflict of interest related to the work presented in this paper. This paper presents independent research funded by the UK National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the UK NHS, the UK NIHR or the UK Department of Health.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Wiley

Serial FEM/XFEM-Based Update of Preoperative Brain Images Using Intraoperative MRI

Current neuronavigation systems cannot adapt to changing intraoperative conditions over time. To overcome this limitation, we present an experimental end-to-end system capable of updating 3D preoperative images in the presence of brain shift and successive resections. The heart of our system is a nonrigid registration technique using a biomechanical model, driven by the deformations of key surfaces tracked in successive intraoperative images. The biomechanical model is deformed using FEM or XFEM, depending on the type of deformation under consideration, namely, brain shift or resection. We describe the operation of our system on two patient cases, each comprising five intraoperative MR images, and we demonstrate that our approach significantly improves the alignment of nonrigidly registered images