Data-guide for brain deformation in surgery: comparison of linear and nonlinear models

<p>Abstract</p> <p>Background</p> <p>Pre-operative imaging devices generate high-resolution images but intra-operative imaging devices generate low-resolution images. To use high-resolution pre-operative images during surgery, they must be deformed to reflect intra-operative geometry of brain.</p> <p>Methods</p> <p>We employ biomechanical models, guided by low resolution intra-operative images, to determine location of normal and abnormal regions of brain after craniotomy. We also employ finite element methods to discretize and solve the related differential equations. In the process, pre- and intra-operative images are utilized and corresponding points are determined and used to optimize parameters of the models. This paper develops a nonlinear model and compares it with linear models while our previous work developed and compared linear models (mechanical and elastic).</p> <p>Results</p> <p>Nonlinear model is evaluated and compared with linear models using simulated and real data. Partial validation using intra-operative images indicates that the proposed models reduce the localization error caused by brain deformation after craniotomy.</p> <p>Conclusions</p> <p>The proposed nonlinear model generates more accurate results than the linear models. When guided by limited intra-operative surface data, it predicts deformation of entire brain. Its execution time is however considerably more than those of linear models.</p

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Physical Constraint Finite Element Model for Medical Image Registration

Due to being derived from linear assumption, most elastic body based non-rigid image registration algorithms are facing challenges for soft tissues with complex nonlinear behavior and with large deformations. To take into account the geometric nonlinearity of soft tissues, we propose a registration algorithm on the basis of Newtonian differential equation. The material behavior of soft tissues is modeled as St. Venant-Kirchhoff elasticity, and the nonlinearity of the continuum represents the quadratic term of the deformation gradient under the Green- St.Venant strain. In our algorithm, the elastic force is formulated as the derivative of the deformation energy with respect to the nodal displacement vectors of the finite element; the external force is determined by the registration similarity gradient flow which drives the floating image deforming to the equilibrium condition. We compared our approach to three other models: 1) the conventional linear elastic finite element model (FEM); 2) the dynamic elastic FEM; 3) the robust block matching (RBM) method. The registration accuracy was measured using three similarities: MSD (Mean Square Difference), NC (Normalized Correlation) and NMI (Normalized Mutual Information), and was also measured using the mean and max distance between the ground seeds and corresponding ones after registration. We validated our method on 60 image pairs including 30 medical image pairs with artificial deformation and 30 clinical image pairs for both the chest chemotherapy treatment in different periods and brain MRI normalization. Our method achieved a distance error of 0.320±0.138 mm in x direction and 0.326±0.111 mm in y direction, MSD of 41.96±13.74, NC of 0.9958±0.0019, NMI of 1.2962±0.0114 for images with large artificial deformations; and average NC of 0.9622±0.008 and NMI of 1.2764±0.0089 for the real clinical cases. Student's t-test demonstrated that our model statistically outperformed the other methods in comparison (p-values <0.05)

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

Biomechanics applied to computer-aided diagnosis: examples of orbital and maxillofacial surgeries

This paper introduces the methodology proposed by our group to model the biological soft tissues deformations and to couple these models with Computer-Assisted Surgical (CAS) applications. After designing CAS protocols that mainly focused on bony structures, the Computer Aided Medical Imaging group of Laboratory TIMC (CNRS, France) now tries to take into account the behaviour of soft tissues in the CAS context. For this, a methodology, originally published under the name of the Mesh-Matching method, has been proposed to elaborate patient specific models. Starting from an elaborate manually-built "generic" Finite Element (FE) model of a given anatomical structure, models adapted to the geometries of each new patient ("patient specific" FE models) are automatically generated through a non-linear elastic registration algorithm. This paper presents the general methodology of the Mesh-Matching method and illustrates this process with two clinical applications, namely the orbital and the maxillofacial computer-assisted surgeries

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

On the Real-Time Performance, Robustness and Accuracy of Medical Image Non-Rigid Registration

Three critical issues about medical image non-rigid registration are performance, robustness and accuracy. A registration method, which is capable of responding timely with an accurate alignment, robust against the variation of the image intensity and the missing data, is desirable for its clinical use. This work addresses all three of these issues. Unacceptable execution time of Non-rigid registration (NRR) often presents a major obstacle to its routine clinical use. We present a hybrid data partitioning method to parallelize a NRR method on a cooperative architecture, which enables us to get closer to the goal: accelerating using architecture rather than designing a parallel algorithm from scratch. to further accelerate the performance for the GPU part, a GPU optimization tool is provided to automatically optimize GPU execution configuration.;Missing data and variation of the intensity are two severe challenges for the robustness of the registration method. A novel point-based NRR method is presented to resolve mapping function (deformation field) with the point correspondence missing. The novelty of this method lies in incorporating a finite element biomechanical model into an Expectation and Maximization (EM) framework to resolve the correspondence and mapping function simultaneously. This method is extended to deal with the deformation induced by tumor resection, which imposes another challenge, i.e. incomplete intra-operative MRI. The registration is formulated as a three variable (Correspondence, Deformation Field, and Resection Region) functional minimization problem and resolved by a Nested Expectation and Maximization framework. The experimental results show the effectiveness of this method in correcting the deformation in the vicinity of the tumor. to deal with the variation of the intensity, two different methods are developed depending on the specific application. For the mono-modality registration on delayed enhanced cardiac MRI and cine MRI, a hybrid registration method is designed by unifying both intensity- and feature point-based metrics into one cost function. The experiment on the moving propagation of suspicious myocardial infarction shows effectiveness of this hybrid method. For the multi-modality registration on MRI and CT, a Mutual Information (MI)-based NRR is developed by modeling the underlying deformation as a Free-Form Deformation (FFD). MI is sensitive to the variation of the intensity due to equidistant bins. We overcome this disadvantage by designing a Top-to-Down K-means clustering method to naturally group similar intensities into one bin. The experiment shows this method can increase the accuracy of the MI-based registration.;In image registration, a finite element biomechanical model is usually employed to simulate the underlying movement of the soft tissue. We develop a multi-tissue mesh generation method to build a heterogeneous biomechanical model to realistically simulate the underlying movement of the brain. We focus on the following four critical mesh properties: tissue-dependent resolution, fidelity to tissue boundaries, smoothness of mesh surfaces, and element quality. Each mesh property can be controlled on a tissue level. The experiments on comparing the homogeneous model with the heterogeneous model demonstrate the effectiveness of the heterogeneous model in improving the registration accuracy

Automatic finite elements mesh generation from planar contours of the brain: an image driven 'blobby' approach

In this paper, we address the problem of automatic mesh generation for finite elements modeling of anatomical organs for which a volumetric data set is available. In the first step a set of characteristic outlines of the organ is defined manually or automatically within the volume. The outlines define the "key frames" that will guide the procedure of surface reconstruction. Then, based on this information, and along with organ surface curvature information extracted from the volume data, a 3D scalar field is generated. This field allows a 3D reconstruction of the organ: as an iso-surface model, using a marching cubes algorithm; or as a 3D mesh, using a grid "immersion" technique, the field value being used as the outside/inside test. The final reconstruction respects the various topological changes that occur within the organ, such as holes and branching elements

Biomechanics

Biomechanics is a vast discipline within the field of Biomedical Engineering. It explores the underlying mechanics of how biological and physiological systems move. It encompasses important clinical applications to address questions related to medicine using engineering mechanics principles. Biomechanics includes interdisciplinary concepts from engineers, physicians, therapists, biologists, physicists, and mathematicians. Through their collaborative efforts, biomechanics research is ever changing and expanding, explaining new mechanisms and principles for dynamic human systems. Biomechanics is used to describe how the human body moves, walks, and breathes, in addition to how it responds to injury and rehabilitation. Advanced biomechanical modeling methods, such as inverse dynamics, finite element analysis, and musculoskeletal modeling are used to simulate and investigate human situations in regard to movement and injury. Biomechanical technologies are progressing to answer contemporary medical questions. The future of biomechanics is dependent on interdisciplinary research efforts and the education of tomorrow’s scientists