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

Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

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

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

Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks

In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image. Our motion model allows for sampling from the conditional distribution of dense displacement fields, is encoded by a generative neural network conditioned on a medical image, and accepts random noise as additional input. The generative network is trained by a minimax optimisation with a second discriminative neural network, tasked to distinguish generated samples from training motion data. In this work, we propose that 1) jointly optimising a third conditioning neural network that pre-processes the input image, can effectively extract patient-specific features for conditioning; and 2) combining multiple generative models trained separately with heuristically pre-disjointed training data sets can adequately mitigate the problem of mode collapse. Trained with diagnostic T2-weighted MR images from 143 real patients and 73,216 3D dense displacement fields from finite element simulations of intraoperative prostate motion due to transrectal ultrasound probe pressure, the proposed models produced physically-plausible patient-specific motion of prostate glands. The ability to capture biomechanically simulated motion was evaluated using two errors representing generalisability and specificity of the model. The median values, calculated from a 10-fold cross-validation, were 2.8+/-0.3 mm and 1.7+/-0.1 mm, respectively. We conclude that the introduced approach demonstrates the feasibility of applying state-of-the-art machine learning algorithms to generate organ motion models from patient images, and shows significant promise for future research.Comment: Accepted to MICCAI 201

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

A Sparse Intraoperative Data-Driven Biomechanical Model to Compensate for Brain Shift during Neuronavigation

BACKGROUND AND PURPOSE: Intraoperative brain deformation is an important factor compromising the accuracy of image-guided neurosurgery. The purpose of this study was to elucidate the role of a model-updated image in the compensation of intraoperative brain shift. MATERIALS AND METHODS: An FE linear elastic model was built and evaluated in 11 patients with craniotomies. To build this model, we provided a novel model-guided segmentation algorithm. After craniotomy, the sparse intraoperative data (the deformed cortical surface) were tracked by a 3D LRS. The surface deformation, calculated by an extended RPM algorithm, was applied on the FE model as a boundary condition to estimate the entire brain shift. The compensation accuracy of this model was validated by the real-time image data of brain deformation acquired by intraoperative MR imaging. RESULTS: The prediction error of this model ranged from 1.29 to 1.91 mm (mean, 1.62 +/- 0.22 mm), and the compensation accuracy ranged from 62.8% to 81.4% (mean, 69.2 +/- 5.3%). The compensation accuracy on the displacement of subcortical structures was higher than that of deep structures (71.3 +/- 6.1%; 66.8 +/- 5.0%, P \u3c .01). In addition, the compensation accuracy in the group with a horizontal bone window was higher than that in the group with a nonhorizontal bone window (72.0 +/- 5.3%; 65.7 +/- 2.9%, P \u3c .05). CONCLUSIONS: Combined with our novel model-guided segmentation and extended RPM algorithms, this sparse data-driven biomechanical model is expected to be a reliable, efficient, and convenient approach for compensation of intraoperative brain shift in image-guided surgery

Finite Element Modeling Driven by Health Care and Aerospace Applications

This thesis concerns the development, analysis, and computer implementation of mesh generation algorithms encountered in finite element modeling in health care and aerospace. The finite element method can reduce a continuous system to a discrete idealization that can be solved in the same manner as a discrete system, provided the continuum is discretized into a finite number of simple geometric shapes (e.g., triangles in two dimensions or tetrahedrons in three dimensions). In health care, namely anatomic modeling, a discretization of the biological object is essential to compute tissue deformation for physics-based simulations. This thesis proposes an efficient procedure to convert 3-dimensional imaging data into adaptive lattice-based discretizations of well-shaped tetrahedra or mixed elements (i.e., tetrahedra, pentahedra and hexahedra). This method operates directly on segmented images, thus skipping a surface reconstruction that is required by traditional Computer-Aided Design (CAD)-based meshing techniques and is convoluted, especially in complex anatomic geometries. Our approach utilizes proper mesh gradation and tissue-specific multi-resolution, without sacrificing the fidelity and while maintaining a smooth surface to reflect a certain degree of visual reality. Image-to-mesh conversion can facilitate accurate computational modeling for biomechanical registration of Magnetic Resonance Imaging (MRI) in image-guided neurosurgery. Neuronavigation with deformable registration of preoperative MRI to intraoperative MRI allows the surgeon to view the location of surgical tools relative to the preoperative anatomical (MRI) or functional data (DT-MRI, fMRI), thereby avoiding damage to eloquent areas during tumor resection. This thesis presents a deformable registration framework that utilizes multi-tissue mesh adaptation to map preoperative MRI to intraoperative MRI of patients who have undergone a brain tumor resection. Our enhancements with mesh adaptation improve the accuracy of the registration by more than 5 times compared to rigid and traditional physics-based non-rigid registration, and by more than 4 times compared to publicly available B-Spline interpolation methods. The adaptive framework is parallelized for shared memory multiprocessor architectures. Performance analysis shows that this method could be applied, on average, in less than two minutes, achieving desirable speed for use in a clinical setting. The last part of this thesis focuses on finite element modeling of CAD data. This is an integral part of the design and optimization of components and assemblies in industry. We propose a new parallel mesh generator for efficient tetrahedralization of piecewise linear complex domains in aerospace. CAD-based meshing algorithms typically improve the shape of the elements in a post-processing step due to high complexity and cost of the operations involved. On the contrary, our method optimizes the shape of the elements throughout the generation process to obtain a maximum quality and utilizes high performance computing to reduce the overheads and improve end-user productivity. The proposed mesh generation technique is a combination of Advancing Front type point placement, direct point insertion, and parallel multi-threaded connectivity optimization schemes. The mesh optimization is based on a speculative (optimistic) approach that has been proven to perform well on hardware-shared memory. The experimental evaluation indicates that the high quality and performance attributes of this method see substantial improvement over existing state-of-the-art unstructured grid technology currently incorporated in several commercial systems. The proposed mesh generator will be part of an Extreme-Scale Anisotropic Mesh Generation Environment to meet industries expectations and NASA\u27s CFD visio

Automatic Alignment of pre and intraoperative Data using Anatomical Landmarks for Augmented Laparoscopic Liver Surgery

International audienceEach year in Europe 50,000 new liver cancer cases are diagnosed for which hepatic surgery combined to chemotherapy is the most common treatment. In particular the number of laparoscopic liver surgeries has increased significantly over the past years. This type of minimally invasive procedure which presents many benefits for the patient is challenging for the surgeons due to the limited field of view. Recently new augmented reality techniques which merge preoperative data and intraoperative images and permit to visualize internal structures have been proposed to help surgeons during this type of surgery. One of the difficulties is to align preoperative data with the intraoperative images. We propose in this paper a semi-automatic approach for solving the ill-posed problem of initial alignment for Augmented Reality systems during liver surgery. Our registration method relies on anatomical landmarks extracted from both the laparoscopic images and three-dimensional model, using an image-based soft-tissue reconstruction technique and an atlas-based approach, respectively. The registration evolves automatically from a quasi-rigid to a non-rigid registration. Furthermore, the surface-driven deformation is induced in the volume via a patient specific biomechanical model. The experiments conducted on both synthetic and in vivo data show promising results with a registration error of 2 mm when dealing with a visible surface of 30% of the whole liver