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

Phenomenological model of diffuse global and regional atrophy using finite-element methods

The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques' ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural magnetic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy is simulated in different tissue compartments or in different neuroanatomical structures with a phenomenological model. This model of diffuse global and regional atrophy is based on volumetric measurements such as the brain or the hippocampus, from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions and tissues. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on biomechanical tissue properties and simulating plausible tissue deformations with finite-element methods. A thermoelastic model of tissue deformation is employed, controlling the rate of progression of atrophy by means of a set of thermal coefficients, each one corresponding to a different type of tissue. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh that will be introduced to a finite-element solver to create the simulated deformations. Preliminary work on the simulation of acquisition artefa- - cts is also presented. Cross-sectional and

Finite element modeling of soft tissue deformation.

Computer-aided minimally invasive surgery (MIS) has progressed significantly in the last decade and it has great potential in surgical planning and operations. To limit the damage to nearby healthy tissue, accurate modeling is required of the mechanical behavior of a target soft tissue subject to surgical manipulations. Therefore, the study of soft tissue deformations is important for computer-aided (MIS) in surgical planning and operation, or in developing surgical simulation tools or systems. The image acquisition facilities are also important for prediction accuracy. This dissertation addresses partial differential and integral equations (PDIE) based biomechanical modeling of soft tissue deformations incorporating the specific material properties to characterize the soft tissue responses for certain human interface behaviors. To achieve accurate simulation of real tissue deformations, several biomechanical finite element (FE) models are proposed to characterize liver tissue. The contribution of this work is in theoretical and practical aspects of tissue modeling. High resolution imaging techniques of Micro Computed Tomography (Micro-CT) and Cone Beam Computed Tomography (CBCT) imaging are first proposed to study soft tissue deformation in this dissertation. These high resolution imaging techniques can detect the tissue deformation details in the contact region between the tissue and the probe for small force loads which would be applied to a surgical probe used. Traditional imaging techniques in clinics can only achieve low image resolutions. Very small force loads seen in these procedures can only yield tissue deformation on the few millimeters to submillimeter scale. Small variations are hardly to detect. Furthermore, if a model is validated using high resolution images, it implies that the model is true in using the same model for low resolution imaging facilities. The reverse cannot be true since the small variations at the sub-millimeter level cannot be detected. In this dissertation, liver tissue deformations, surface morphological changes, and volume variations are explored and compared from simulations and experiments. The contributions of the dissertation are as follows. For liver tissue, for small force loads (5 grams to tens of grams), the linear elastic model and the neo-Hooke\u27s hyperelastic model are applied and shown to yield some discrepancies among them in simulations and discrepancies between simulations and experiments. The proposed finite element models are verified for liver tissue. A general FE modeling validation system is proposed to verify the applicability of FE models to the soft tissue deformation study. The validation of some FE models is performed visually and quantitatively in several ways in comparison with the actual experimental results. Comparisons among these models are also performed to show their advantages and disadvantages. The method or verification system can be applied for other soft tissues for the finite element analysis of the soft tissue deformation. For brain tissue, an elasticity based model was proposed previously employing local elasticity and Poisson\u27s ratio. It is validated by intraoperative images to show more accurate prediction of brain deformation than the linear elastic model. FE analysis of brain ventricle shape changes was also performed to capture the dynamic variation of the ventricles in author\u27s other works. There, for the safety reasons, the images for brain deformation modeling were from Magnetic Resonance Imaging (MRI) scanning which have been used for brain scanning. The measurement process of material properties involves the tissue desiccation, machine limits, human operation errors, and time factors. The acquired material parameters from measurement devices may have some difference from the tissue used in real state of experiments. Therefore, an experimental and simulation based method to inversely evaluate the material parameters is proposed and compare

Microscope Embedded Neurosurgical Training and Intraoperative System

In the recent years, neurosurgery has been strongly influenced by new technologies. Computer Aided Surgery (CAS) offers several benefits for patients\u27 safety but fine techniques targeted to obtain minimally invasive and traumatic treatments are required, since intra-operative false movements can be devastating, resulting in patients deaths. The precision of the surgical gesture is related both to accuracy of the available technological instruments and surgeon\u27s experience. In this frame, medical training is particularly important. From a technological point of view, the use of Virtual Reality (VR) for surgeon training and Augmented Reality (AR) for intra-operative treatments offer the best results. In addition, traditional techniques for training in surgery include the use of animals, phantoms and cadavers. The main limitation of these approaches is that live tissue has different properties from dead tissue and that animal anatomy is significantly different from the human. From the medical point of view, Low-Grade Gliomas (LGGs) are intrinsic brain tumours that typically occur in younger adults. The objective of related treatment is to remove as much of the tumour as possible while minimizing damage to the healthy brain. Pathological tissue may closely resemble normal brain parenchyma when looked at through the neurosurgical microscope. The tactile appreciation of the different consistency of the tumour compared to normal brain requires considerable experience on the part of the neurosurgeon and it is a vital point. The first part of this PhD thesis presents a system for realistic simulation (visual and haptic) of the spatula palpation of the LGG. This is the first prototype of a training system using VR, haptics and a real microscope for neurosurgery. This architecture can be also adapted for intra-operative purposes. In this instance, a surgeon needs the basic setup for the Image Guided Therapy (IGT) interventions: microscope, monitors and navigated surgical instruments. The same virtual environment can be AR rendered onto the microscope optics. The objective is to enhance the surgeon\u27s ability for a better intra-operative orientation by giving him a three-dimensional view and other information necessary for a safe navigation inside the patient. The last considerations have served as motivation for the second part of this work which has been devoted to improving a prototype of an AR stereoscopic microscope for neurosurgical interventions, developed in our institute in a previous work. A completely new software has been developed in order to reuse the microscope hardware, enhancing both rendering performances and usability. Since both AR and VR share the same platform, the system can be referred to as Mixed Reality System for neurosurgery. All the components are open source or at least based on a GPL license

SIMBIO-M 2014, SIMulation technologies in the fields of BIO-Sciences and Multiphysics: BioMechanics, BioMaterials and BioMedicine, Marseille, France, june 2014

Proceedings de la 3ème édition de la conférence internationale Simbio-M (2014). Organisée conjointement par l'IFSTTAR, Aix-Marseille Université, l'université de Coventry et CADLM, cette conférence se concentre sur les progrès des technologies de simulation dans les domaines des sciences du vivant et multiphysiques: Biomécanique, Biomatériaux et Biomédical. L'objectif de cette conférence est de partager et d'explorer les résultats dans les techniques d'analyse numérique et les outils de modélisation mathématique. Cette approche numérique permet des études prévisionnelles ou exploratoires dans les différents domaines des biosciences

Characterising and Modelling Calvarial Growth and Bone Formation in Wild Type and Craniosynostotic Type Mice

The newborn mammalian cranial vault consists of five flat bones that are joined together along their edges by soft tissues called sutures. The sutures give flexibility for birth, and accommodate the growth of the brain. They also act as shock absorber in childhood. Early fusion of the cranial sutures is a medical condition called craniosynostosis, and may affect only one suture (non-syndromic) or multiple sutures (syndromic). Correction of this condition is complex and usually involves multiple surgical interventions during infancy. The aim of this study was to characterise the skull growth in normal and craniosynostotic mice and to use this data to develop a validated computational model of skull growth. Two oncogenic series of normal and craniosynostosis (Crouzon) mice were microCT scanned and various morphological features of their skulls was characterised at postnatal days (P) 3, 7 and 10. Finite element model of a normal mouse at P3 was developed and used to predict the radial expansion of the skull and the pattern of bone formation at the sutures at P7 and P10. A series of sensitivity tests were carried out. Note the specific ages used in this study correspond to the age that this condition is diagnosed and treated in human. Results highlighted a good agreement between the finite element results and the ex vivo data both in terms of the radial expansion of the skull and the pattern of bone formation at the sutures. Nonetheless, the FE results were sensitive to the choice of input parameters. The modelling approach and the platform that was developed and validated here has huge potentials to be applied to human skull and to optimise the management of various forms of this condition

Predicting and optimising the postoperative outcomes of sagittal craniosynostosis correction

The neonate skull consists of several flat bones, connected by fibrous joints called sutures. Sutures regulate the bone formation along their adjoining edges, while providing mailability to assist with the early phases of rapid brain growth and passing through the birth canal with minimal restriction. By adolescents, these sutures fuse into solid bone, protecting the brain from impacts. The premature fusion of one or more of these sutures is a medical condition known as craniosynostosis, with its most common form being sagittal craniosynostosis (fusion of the midline suture). The condition results in compensatory overgrowth perpendicular to the fused suture, leading to calvarial deformation and possible neurofunctional defects. Surgeons have developed several surgical techniques to restore the normative shape. This has led to debates as to which surgical option provides the most beneficial long term outcome. The overall aim of this thesis was to develop a computational approach using the finite element (FE) method capable of predicting and optimising the long term outcomes for treating sagittal craniosynostosis. A generic 3D pre-operative FE model was developed using patient specific CT data. The FE model was parameterised to predict the long term calvarial growth, the pattern of suture and bone formation, the pattern of bone healing across the replicated surgical techniques, and the changes in contact pressure levels across the modelled brain. All techniques underwent simulated growth up to the maximum age of 76 months. Morphological results were compared against the patient specific CT data at the same age. Where absent, technique specific follow up CT data were used instead. Results highlighted a good morphological agreement between the predicted models and their comparative CT data. The FE model was highly sensitive to the choice of input parameters. Based on the findings of this thesis, the *** approach proved the most optimal across the predicted outcomes. The novel methodology and platform developed here has huge potential to better inform surgeons of the impact various techniques could have on long term outcomes and continue to improve the quality of care for patients undergoing corrective surgery