Hadrien Courtecuisse

GPU-based Real-Time Soft Tissue Deformation with Cutting and Haptic Feedback

This article describes a series of contributions in the field of real-time simulation of soft tissue biomechanics. These contributions address various requirements for interactive simulation of complex surgical procedures. In particular, this article presents results in the areas of soft tissue deformation, contact modelling, simulation of cutting, and haptic rendering, which are all relevant to a variety of medical interventions. The contributions described in this article share a common underlying model of deformation and rely on GPU implementations to significantly improve computation times. This consistency in the modelling technique and computational approach ensures coherent results as well as efficient, robust and flexible solutions.

Real-time simulation of contact and cutting of heterogeneous soft-tissues

This paper presents a numerical method for interactive (real-time) simulations, which considerably improves the accuracy of the response of heterogeneous soft-tissue models undergoing contact, cutting and other topological changes. We provide an integrated methodology able to deal both with the ill-conditioning issues associated with material heterogeneities, contact boundary conditions which are one of the main sources of inaccuracies, and cutting which is one of the most challenging issues in interactive simulations. Our approach is based on an implicit time integration of a non-linear finite element model. To enable real-time computations, we propose a new preconditioning technique, based on an asynchronous update at low frequency. The preconditioner is not only used to improve the computation of the deformation of the tissues, but also to simulate the contact response of homogeneous and heterogeneous bodies with the same accuracy. We also address the problem of cutting the heterogeneous structures and propose a method to update the preconditioner according to the topological modifications. Finally, we apply our approach to three challenging demonstrators: (i) a simulation of cataract surgery (ii) a simulation of laparoscopic hepatectomy (iii) a brain tumor surgery.

Parallel Dense Gauss-Seidel Algorithm on Many-Core Processors

The Gauss-Seidel method is very efficient for solving problems such as tightly-coupled constraints with possible redundancies. However, the underlying algorithm is inherently sequential. Previous works have exploited sparsity in the system matrix to extract parallelism. In this paper, we propose to study several parallelization schemes for fully-coupled systems, unable to be parallelized by existing methods, taking advantage of recent many-cores architectures offering fast synchronization primitives. Experimental results on both multi-core CPUs and recent GPUs show that our proposed method is able to fully exploit the available units, whereas trivial parallel algorithms often fail.This method is illustrated by an application in medical intervention planning, where it is used to solve a linear complementary problem (LCP) expressing the contacts applied to a deformable body.

Virtual cutting of deformable objects based on efficient topological operations

Virtual cutting of deformable objects is at the core of many applications in interactive simulation and especially in computational medicine. The ability to simulate surgical cuts, dissection, soft tissue tearing or micro-fractures is essential for augmenting the capabilities of existing or future simulation systems. To support such features, we combine a new remeshing algorithm with a fast finite element approach. The proposed method is generic enough to support a large variety of applications. We show the benefits of our approach evaluating the impact of cuts on the number of nodes and the numerical quality of the mesh. These points are crucial to ensure accurate and stable real-time simulations.

Preconditioner-based contact response and application to cataract surgery

In this paper we introduce a new method to compute, in real-time, the physical behavior of several colliding soft-tissues in a surgical simulation. The numerical approach is based on finite element modeling and allows for a fast update of a large number of tetrahedral elements. The speed-up is obtained by the use of a specific preconditioner that is updated at low frequency. The preconditioning enables an optimized computation of both large deformations and precise contact response. Moreover, homogeneous and inhomogeneous tissues are simulated with the same accuracy. Finally, we illustrate our method in a simulation of one step in a cataract surgery procedure, which require to handle contacts with non homogeneous objects precisely.

Controlling the error on target motion through real-time mesh adaptation: applications to Deep Brain Stimulation

An error-controlled mesh refinement procedure for needle insertion simulations is presented. As an example, the procedure is applied for simulations of electrode implantation for deep brain stimulation. We take into account the brain shift phenomena occurring when a craniotomy is performed. We observe that the error in the computation of the displacement and stress fields is localised around the needle tip and the needle shaft during needle insertion simulation. By suitably and adaptively refining the mesh in this region, our approach enables to control, and thus to reduce, the error whilst maintaining a coarser mesh in other parts of the domain. Through academic and practical examples we demonstrate that our adaptive approach, as compared with a uniform coarse mesh, increases the accuracy of the displacement and stress fields around the needle shaft and, while for a given accuracy, saves computational time with respect to a uniform finer mesh. This facilitates real-time simulations. The proposed methodology has direct implications in increasing the accuracy, and controlling the computational expense of the simulation of percutaneous procedures such as biopsy, brachytherapy, regional anaesthesia, or cryotherapy. Moreover, the proposed approach can be helpful in the development of robotic surgeries because the simulation taking place in the control loop of a robot needs to be accurate, and to occur in real time.

Towards real-time computation of cardiac electrophysiology for training simulator

This work aims at developing a training simulator for interventional radiology and thermo-ablation of cardiac arrhythmias. To achieve this, a real-time model of the cardiac electrophysiology is needed, which is very challenging due to the stiff equations involved. In this paper, we detail our contributions in order to obtain efficient cardiac electrophysiology simulations. First, an adaptive parametrisation of the Mitchell-Schaeffer model as well as numerical optimizations are proposed. An accurate computation of both conduction velocity and action potential is ensured, even with relatively coarse meshes. Second, a GPU implementation of the electrophysiology was realised in order to decrease the computation time. We evaluate our results by comparison with an accurate reference simulation using model parameters, personalized on patient data. We demonstrate that a fast simulation (close to real-time) can be obtained while keeping a precise description of the phenomena.

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

HighlightsA constraint‐based biomechanical simulation method is proposed to compensate for brain‐shift.Intraoperatively, a single ultrasound acquisition is used to account for the vessels and cortical deformations.Quantitative validation over synthetic data and five clinical cases is provided.Improvements over one of the closest existing methods are shown.This method is fully compatible with a surgical process. Graphical abstract Figure. No caption available. Purpose. During brain tumor surgery, planning and guidance are based on preoperative 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 biomechanical 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.

GPU-Based Interactive Simulation of Liver Resection

This GPU-based interactive simulation of laparoscopic liver resection is implemented using the open-source SOFA framework. While similar medical simulators have been developed in the past, this demo relies on advanced methods and the computational power of current GPUs to simulate multiple organs with high-resolution deformations and collisions in real time. It is based on recently proposed methods: high-resolution Finite Element Model (FEM) with implicit time-integration implemented on GPU, volume-contact constraints, an efficient numerical solver based on asynchronous preconditioning, and improvements in visual and haptic rendering. And it uses detailed meshes generated from segmented CT scans to facilitate reproduction of patient-specific scenarios, which is necessary for pre-operative rehearsal of complex or risky medical procedures. These methods allow real-time simulation of all organs in the abdominal cavity using an improved level of precision compared to previous systems. The FEM formulation enables reproduction of specific material properties. Contacts are handled by precise constraints with frictions on detailed surface meshes. Both methods efficiently support topological changes, as demonstrated by performing a resection of a portion of a liver, an important step in surgical procedures performed to remove cancerous tumors. Users can examine the mechanical and collision models, and the generated contacts while the simulated patient is breathing, and manipulate a laparoscopic instrument to navigate through the abdominal cavity, push on organs, and perform a thermal ablation. This project is a result of a collaboration between INRIA and IRCAD within the EU-funded PASSPORT project.

Fast elastic registration of soft tissues under large deformations

&NA; A fast and accurate fusion of intra‐operative images with a pre‐operative data is a key component of computer‐aided interventions which aim at improving the outcomes of the intervention while reducing the patients discomfort. In this paper, we focus on the problematic of the intra‐operative navigation during abdominal surgery, which requires an accurate registration of tissues undergoing large deformations. Such a scenario occurs in the case of partial hepatectomy: to facilitate the access to the pathology, e.g. a tumor located in the posterior part of the right lobe, the surgery is performed on a patient in lateral position. Due to the change in patients position, the resection plan based on the pre‐operative CT scan acquired in the supine position must be updated to account for the deformations. We suppose that an imaging modality, such as the cone‐beam CT, provides the information about the intra‐operative shape of an organ, however, due to the reduced radiation dose and contrast, the actual locations of the internal structures necessary to update the planning are not available. To this end, we propose a method allowing for fast registration of the pre‐operative data represented by a detailed 3D model of the liver and its internal structure and the actual configuration given by the organ surface extracted from the intra‐operative image. The algorithm behind the method combines the iterative closest point technique with a biomechanical model based on a co‐rotational formulation of linear elasticity which accounts for large deformations of the tissue. The performance, robustness and accuracy of the method is quantitatively assessed on a control semi‐synthetic dataset with known ground truth and a real dataset composed of nine pairs of abdominal CT scans acquired in supine and flank positions. It is shown that the proposed surface‐matching method is capable of reducing the target registration error evaluated of the internal structures of the organ from more than 40 mm to less then 10 mm. Moreover, the control data is used to demonstrate the compatibility of the method with intra‐operative clinical scenario, while the real datasets are utilized to study the impact of parametrization on the accuracy of the method. The method is also compared to a state‐of‐the art intensity‐based registration technique in terms of accuracy and performance.