Brain-shift compensation using intraoperative ultrasound and constraint-based biomechanical simulation

International audiencePurpose. During brain tumor surgery, planning and guidance are based on pre-operative images which do not account for brain-shift. However, this deformation is a major source of error in image-guided neurosurgery and affects the accuracy of the procedure. In this paper, we present a constraint-based biome-chanical simulation method to compensate for craniotomy-induced brain-shift that integrates the deformations of the blood vessels and cortical surface, using a single intraoperative ultrasound acquisition. Methods. Prior to surgery, a patient-specific biomechanical model is built from preoperative images, accounting for the vascular tree in the tumor region and brain soft tissues. Intraoperatively, a navigated ultrasound acquisition is performed directly in contact with the organ. Doppler and B-mode images are recorded simultaneously, enabling the extraction of the blood vessels and probe footprint respectively. A constraint-based simulation is then executed to register the pre-and intraoperative vascular trees as well as the cortical surface with the probe footprint. Finally, preoperative images are updated to provide the surgeon with images corresponding to the current brain shape for navigation. Results. The robustness of our method is first assessed using sparse and noisy synthetic data. In addition, quantitative results for five clinical cases are provided , first using landmarks set on blood vessels, then based on anatomical structures delineated in medical images. The average distances between paired vessels landmarks ranged from 3.51 to 7.32 (in mm) before compensation. With our method, on average 67% of the brain-shift is corrected (range [1.26; 2.33]) against 57% using one of the closest existing works (range [1.71; 2.84]). Finally, our method is proven to be fully compatible with a surgical workflow in terms of execution times and user interactions. Conclusion. In this paper, a new constraint-based biomechanical simulation method is proposed to compensate for craniotomy-induced brain-shift. While being efficient to correct this deformation, the method is fully integrable in a clinical process

In vivo measurement of human brain elasticity using a light aspiration device

The brain deformation that occurs during neurosurgery is a serious issue impacting the patient "safety" as well as the invasiveness of the brain surgery. Model-driven compensation is a realistic and efficient solution to solve this problem. However, a vital issue is the lack of reliable and easily obtainable patient-specific mechanical characteristics of the brain which, according to clinicians' experience, can vary considerably. We designed an aspiration device that is able to meet the very rigorous sterilization and handling process imposed during surgery, and especially neurosurgery. The device, which has no electronic component, is simple, light and can be considered as an ancillary instrument. The deformation of the aspirated tissue is imaged via a mirror using an external camera. This paper describes the experimental setup as well as its use during a specific neurosurgery. The experimental data was used to calibrate a continuous model. We show that we were able to extract an in vivo constitutive law of the brain elasticity: thus for the first time, measurements are carried out per-operatively on the patient, just before the resection of the brain parenchyma. This paper discloses the results of a difficult experiment and provide for the first time in-vivo data on human brain elasticity. The results point out the softness as well as the highly non-linear behavior of the brain tissue.Comment: Medical Image Analysis (2009) accept\'

Framework for a low-cost intra-operative image-guided neuronavigator including brain shift compensation

In this paper we present a methodology to address the problem of brain tissue deformation referred to as 'brain-shift'. This deformation occurs throughout a neurosurgery intervention and strongly alters the accuracy of the neuronavigation systems used to date in clinical routine which rely solely on pre-operative patient imaging to locate the surgical target, such as a tumour or a functional area. After a general description of the framework of our intra-operative image-guided system, we describe a procedure to generate patient specific finite element meshes of the brain and propose a biomechanical model which can take into account tissue deformations and surgical procedures that modify the brain structure, like tumour or tissue resection

Framework and Bio-Mechanical Model for a Per-Operative Image-Guided Neuronavigator Including 'Brain-Shift' Compensation

In this paper we present a methodology to adress the problem of brain tissue deformation referred to as "brainshift". This deformation occurs throughout a neurosurgery intervention and strongly alters the accuracy of the neuronavigation systems used to date in clinical routine which rely solely on preoperative patient imaging to locate the surgical target, such as a tumour or a functional area. After a general description of the framework of our intraoperative image-guided system, we propose a biomechanical model of the brain which can take into account interactively such deformations as well as surgical procedures that modify the brain structure, like tumour or tissue resection

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

Vessel-based brain-shift compensation using elastic registration driven by a patient-specific finite element model

International audienceDuring brain tumor surgery, planning and guidance are based on pre-operative images which do not account for brain-shift.However, this shift is a major source of error in neuro-navigation systems and affects the accuracy of the procedure. The vascular tree is extracted from pre-operative Magnetic Resonance Angiography and from intra-operative Doppler ultrasound images, which provides sparse information on brain deformations.The pre-operative images are then updated based on an elastic registration of the blood vessels, driven by a patient-specific biomechanical model.This biomechanical model is used to extrapolate the deformation to the surrounding soft tissues.Quantitative results on a single surgical case are provided, with an evaluation of the execution time for each processing step.Our method is proved to be efficient to compensate for brain deformation while being compatible with a surgical process

Alignment of Cortical Vessels viewed through the Surgical Microscope with Preoperative Imaging to Compensate for Brain Shift

International audienceBrain shift is a non-rigid deformation of brain tissue that is affected by loss of cerebrospinal fluid, tissue manipulation and gravity among other phenomena. This deformation can negatively influence the outcome of a surgical procedure since surgical planning based on pre-operative image becomes less valid. We present a novel method to compensate for brain shift that maps preoperative image data to the deformed brain during intra-operative neurosurgical procedures and thus increases the likelihood of achieving a gross total resection while decreasing the risk to healthy tissue surrounding the tumor. Through a 3D/2D non-rigid registration process, a 3D articulated model derived from pre-operative imaging is aligned onto 2D images of the vessels viewed through the surgical miscroscopic intra-operatively. The articulated 3D vessels constrain a volumetric biomechanical model of the brain to propagate cortical vessel deformation to the parenchyma and in turn to the tumor. The 3D/2D non-rigid registration is performed using an energy minimization approach that satisfies both projective and physical constraints. Our method is evaluated on real and synthetic data of human brain showing both quantitative and qualitative results and exhibiting its particular suitability for real-time surgical guidance

Patient-specific simulation environment for surgical planning and preoperative rehearsal

Surgical simulation is common practice in the fields of surgical education and training. Numerous surgical simulators are available from commercial and academic organisations for the generic modelling of surgical tasks. However, a simulation platform is still yet to be found that fulfils the key requirements expected for patient-specific surgical simulation of soft tissue, with an effective translation into clinical practice. Patient-specific modelling is possible, but to date has been time-consuming, and consequently costly, because data preparation can be technically demanding. This motivated the research developed herein, which addresses the main challenges of biomechanical modelling for patient-specific surgical simulation. A novel implementation of soft tissue deformation and estimation of the patient-specific intraoperative environment is achieved using a position-based dynamics approach. This modelling approach overcomes the limitations derived from traditional physically-based approaches, by providing a simulation for patient-specific models with visual and physical accuracy, stability and real-time interaction. As a geometrically- based method, a calibration of the simulation parameters is performed and the simulation framework is successfully validated through experimental studies. The capabilities of the simulation platform are demonstrated by the integration of different surgical planning applications that are found relevant in the context of kidney cancer surgery. The simulation of pneumoperitoneum facilitates trocar placement planning and intraoperative surgical navigation. The implementation of deformable ultrasound simulation can assist surgeons in improving their scanning technique and definition of an optimal procedural strategy. Furthermore, the simulation framework has the potential to support the development and assessment of hypotheses that cannot be tested in vivo. Specifically, the evaluation of feedback modalities, as a response to user-model interaction, demonstrates improved performance and justifies the need to integrate a feedback framework in the robot-assisted surgical setting.Open Acces