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

Controlling the Error on Target Motion through Real-time Mesh Adaptation: Applications to Deep Brain Stimulation

We present an error-controlled mesh refinement procedure for needle insertion simulation and apply it to the simulation of electrode implantation for deep brain stimulation, including brain shift. Our approach enables to control the error in the computation of the displacement and stress fields around the needle tip and needle shaft by suitably refining the mesh, whilst maintaining a coarser mesh in other parts of the domain. We demonstrate through academic and practical examples that our approach increases the accuracy of the displacement and stress fields around the needle without increasing the computational expense. This enables real-time simulations. The proposed methodology has direct implications to increase the accuracy and control the computational expense of the simulation of percutaneous procedures such as biopsy, brachytherapy, regional anesthesia, or cryotherapy and can be essential to the development of robotic guidance.Comment: 21 pages, 14 figure

Finite element surface registration incorporating curvature, volume preservation, and statistical model information

We present a novel method for nonrigid registration of 3D surfaces and images. The method can be used to register surfaces by means of their distance images, or to register medical images directly. It is formulated as a minimization problem of a sum of several terms representing the desired properties of a registration result: smoothness, volume preservation, matching of the surface, its curvature, and possible other feature images, as well as consistency with previous registration results of similar objects, represented by a statistical deformation model. While most of these concepts are already known, we present a coherent continuous formulation of these constraints, including the statistical deformation model. This continuous formulation renders the registration method independent of its discretization. The finite element discretization we present is, while independent of the registration functional, the second main contribution of this paper. The local discontinuous Galerkin method has not previously been used in image registration, and it provides an efficient and general framework to discretize each of the terms of our functional. Computational efficiency and modest memory consumption are achieved thanks to parallelization and locally adaptive mesh refinement. This allows for the first time the use of otherwise prohibitively large 3D statistical deformation models

New Mechanics of Traumatic Brain Injury

The prediction and prevention of traumatic brain injury is a very important aspect of preventive medical science. This paper proposes a new coupled loading-rate hypothesis for the traumatic brain injury (TBI), which states that the main cause of the TBI is an external Euclidean jolt, or SE(3)-jolt, an impulsive loading that strikes the head in several coupled degrees-of-freedom simultaneously. To show this, based on the previously defined covariant force law, we formulate the coupled Newton-Euler dynamics of brain's micro-motions within the cerebrospinal fluid and derive from it the coupled SE(3)-jolt dynamics. The SE(3)-jolt is a cause of the TBI in two forms of brain's rapid discontinuous deformations: translational dislocations and rotational disclinations. Brain's dislocations and disclinations, caused by the SE(3)-jolt, are described using the Cosserat multipolar viscoelastic continuum brain model. Keywords: Traumatic brain injuries, coupled loading-rate hypothesis, Euclidean jolt, coupled Newton-Euler dynamics, brain's dislocations and disclinationsComment: 18 pages, 1 figure, Late

Brain Response of a Computational Head Model for Prescribed Skull Kinematics and Simulated 3 Football Helmet Impact Boundary Conditions

This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this article is published in the Journal of the Mechanical Behavior of Biomedical Materials, and is available online at https://doi.org/10.1016/j.jmbbm.2020.104299Computational human body models (HBM) present a novel approach to predict brain response 3 in football impact scenarios, with prescribed kinematic boundary conditions for the HBM skull 4 typically used at present. However, computational optimization of helmets requires simulation 5 of the coupled helmet and HBM model; which is much more complex and has not been assessed 6 in the context of brain deformation and existing simplified approaches. In the current study, two 7 boundary conditions and the resulting brain deformations were compared using a HBM head 8 model: (1) a prescribed skull kinematics (PK) boundary condition using measured head kinematics 9 from experimental impacts; and (2) a novel detailed simulation of a HBM head and neck, helmet 10 and linear impactor (HBM‐S). While lateral and rear impacts exhibited similar levels of maximum 11 principal strain (MPS) in the brain tissue using both boundary conditions, differences were noted 12 in the frontal orientation (at 9.3 m/s, MPS was 0.39 for PK, 0.54 for HBM‐S). Importantly, both PK 13 and HBM‐S boundary conditions produced a similar distribution of MPS throughout the brain for 14 each impact orientation considered. Within the corpus callosum and thalamus, high MPS was 15 associated with lateral impacts and lower values with frontal and rear impacts. The good 16 correspondence of both boundary conditions is encouraging for future optimization of helmet 17 designs. A limitation of the PK approach is the need for experimental head kinematics data, while 18 the HBM‐S can predict brain response for varying impact conditions and helmet configurations, 19 with potential as a tool to improve helmet protection performance.The research presented was made possible by a grant from Football Research, Inc. (FRI), the National Football League (NFL), and Biomechanical Consulting and Research, LLC (Biocore) in the USA

Bulging brains

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.Brain swelling is a serious condition associated with an accumulation of fluid inside the brain that can be caused by trauma, stroke, infection, or tumors. It increases the pressure inside the skull and reduces blood and oxygen supply. To relieve the intracranial pressure, neurosurgeons remove part of the skull and allow the swollen brain to bulge outward, a procedure known as decompressive craniectomy. Decompressive craniectomy has been preformed for more than a century; yet, its effects on the swollen brain remain poorly understood. Here we characterize the deformation, strain, and stretch in bulging brains using the nonlinear field theories of mechanics. Our study shows that even small swelling volumes of 28 to 56 ml induce maximum principal strains in excess of 30 %. For radially outward-pointing axons, we observe maximal normal stretches of 1.3 deep inside the bulge and maximal tangential stretches of 1.3 around the craniectomy edge. While the stretch magnitude varies with opening site and swelling region, our study suggests that the locations of maximum stretch are universally shared amongst all bulging brains. Our model has the potential to inform neurosurgeons and rationalize the shape and position of the skull opening, with the ultimate goal to reduce brain damage and improve the structural and functional outcomes of decompressive craniectomy in trauma patients.We thank Allan L. Reiss and his group for providing the MRI scans. This work was supported by the Timoshenko Scholar Award to Alain Goriely and by the Humboldt Research Award and the National Institutes of Health grant U01 HL119578 to Ellen Kuhl

The mechanics of decompressive craniectomy: Personalized simulations

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordDecompressive craniectomy is a traditional but controversial surgical procedure that removes part of the skull to allow an injured and swollen brain to expand outward. Recent studies suggest that mechanical strain is associated with its undesired, high failure rates. However, the precise strain fields induced by the craniectomy are unknown. Here we create a personalized craniectomy model from magnetic resonance images to quantify the strains during a decompressive craniectomy using finite element analysis. We swell selected regions of the brain and remove part of the skull to allow the brain to bulge outward and release the intracranical swelling pressure. Our simulations reveal three potential failure mechanisms associated with the procedure: axonal stretch in the center of the bulge, axonal compression at the edge of the craniectomy, and axonal shear around the opening. Strikingly, for a swelling of only 10%, axonal strain, compression, and shear reach local maxima of up to 30%, and exceed the reported functional and morphological damage thresholds of 18% and 21%. Our simulations suggest that a collateral craniectomy with the skull opening at the side of swelling is less invasive than a contralateral craniectomy with the skull opening at the opposite side: It induces less deformation, less rotation, smaller strains, and a markedly smaller midline shift. Our computational craniectomy model can help quantify brain deformation, tissue strain, axonal stretch, and shear with the goal to identify high-risk regions for brain damage on a personalized basis. While computational modeling is beyond clinical practice in neurosurgery today, simulations of neurosurgical procedures have the potential to rationalize surgical process parameters including timing, location, and size, and provide standardized guidelines for clinical decision making and neurosurgical planning.This work was supported by the Wolfson/Royal Society Merit Award to Alain Goriely and by the National Institutes of Health grant U01 HL119578 to Ellen Kuhl

Grid simulation services for the medical community

The first part of this paper presents a selection of medical simulation applications, including image reconstruction, near real-time registration for neuro-surgery, enhanced dose distribution calculation for radio-therapy, inhaled drug delivery prediction, plastic surgery planning and cardio-vascular system simulation. The latter two topics are discussed in some detail. In the second part, we show how such services can be made available to the clinical practitioner using Grid technology. We discuss the developments and experience made during the EU project GEMSS, which provides reliable, efficient, secure and lawful medical Grid services

A mixed finite element method for nearly incompressible multiple-network poroelasticity

In this paper, we present and analyze a new mixed finite element formulation of a general family of quasi-static multiple-network poroelasticity (MPET) equations. The MPET equations describe flow and deformation in an elastic porous medium that is permeated by multiple fluid networks of differing characteristics. As such, the MPET equations represent a generalization of Biot's equations, and numerical discretizations of the MPET equations face similar challenges. Here, we focus on the nearly incompressible case for which standard mixed finite element discretizations of the MPET equations perform poorly. Instead, we propose a new mixed finite element formulation based on introducing an additional total pressure variable. By presenting energy estimates for the continuous solutions and a priori error estimates for a family of compatible semi-discretizations, we show that this formulation is robust in the limits of incompressibility, vanishing storage coefficients, and vanishing transfer between networks. These theoretical results are corroborated by numerical experiments. Our primary interest in the MPET equations stems from the use of these equations in modelling interactions between biological fluids and tissues in physiological settings. So, we additionally present physiologically realistic numerical results for blood and tissue fluid flow interactions in the human brain

A CFD and FEM Approach to a Multicompartmental Poroelastic Model for CSF Production and Circulation with Applicationsin Hydrocephalus Treatment and Cerebral Oedema

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.This study introduces a Multiple-Network Poroelastic Theory (MPET) model, coupled with finite-volume based Computational fluid dynamics (CFD) for the purpose of studying, in detail, the effects of obstructing Cerebrospinal fluid (CSF) transport within an image-derived cerebral environment. The MPET representation allows the investigation of fluid transport between CSF, brain parenchyma and cerebral blood, in an integral and comprehensive manner. Key novelties of this model are the casting of multidimensional MPET in a Finite Element Method (FEM) framework, the amalgamation of anatomically accurate choroid plexuses with their feeding arteries and a simple relationship relaxing the constraint of a unique permeability for the CSF compartment. This model is used to demonstrate the impact of fourth ventricle outlet obstruction (FVOO). The implications of treating such a clinical condition with the aid of endoscopic third (ETV) and endoscopic fourth (EFV) ventriculostomy are considered. Finally, we outline the impact of the FEM based MPET framework in understanding oedema, and its ongoing evolution