Stian Flage Johnsen

NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics

PurposeNiftySim, an open-source finite element toolkit, has been designed to allow incorporation of high-performance soft tissue simulation capabilities into biomedical applications. The toolkit provides the option of execution on fast graphics processing unit (GPU) hardware, numerous constitutive models and solid-element options, membrane and shell elements, and contact modelling facilities, in a simple to use library.MethodsThe toolkit is founded on the total Lagrangian explicit dynamics (TLEDs) algorithm, which has been shown to be efficient and accurate for simulation of soft tissues. The base code is written in C

Accuracy validation of an image guided laparoscopy system for liver resection

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Prefrontal Cortical Folding of the Preterm Brain: A Longitudinal Analysis of Preterm-Born Neonates

++, and GPU execution is achieved using the nVidia CUDA framework. In most cases, interaction with the underlying solvers can be achieved through a single Simulator class, which may be embedded directly in third-party applications such as, surgical guidance systems. Advanced capabilities such as contact modelling and nonlinear constitutive models are also provided, as are more experimental technologies like reduced order modelling. A consistent description of the underlying solution algorithm, its implementation with a focus on GPU execution, and examples of the toolkit’s usage in biomedical applications are provided.ResultsEfficient mapping of the TLED algorithm to parallel hardware results in very high computational performance, far exceeding that available in commercial packages.ConclusionThe NiftySim toolkit provides high-performance soft tissue simulation capabilities using GPU technology for biomechanical simulation research applications in medical image computing, surgical simulation, and surgical guidance applications.

Explicit contact modeling for surgical computer guidance and simulation

Determining corresponding regions between an MRI and an X-ray mammogram is a clinically useful task that is challenging for radiologists due to the large deformation that the breast undergoes between the two image acquisitions. In this work we propose an intensity-based image registration framework, where the biomechanical transformation model parameters and the rigid-body transformation parameters are optimised simultaneously. Patient-specific biomechanical modelling of the breast derived from diagnostic, prone MRI has been previously used for this task. However, the high computational time associated with breast compression simulation using commercial packages, did not allow the optimisation of both pose and FEM parameters in the same framework. We use a fast explicit Finite Element (FE) solver that runs on a graphics card, enabling the FEM-based transformation model to be fully integrated into the optimisation scheme. The transformation model has seven degrees of freedom, which include parameters for both the initial rigid-body pose of the breast prior to mammographic compression, and those of the biomechanical model. The framework was tested on ten clinical cases and the results were compared against an affine transformation model, previously proposed for the same task. The mean registration error was 11.6±3.8mm for the CC and 11±5.4mm for the MLO view registrations, indicating that this could be a useful clinical tool.

The NifTK software platform for image-guided interventions: platform overview and NiftyLink messaging

We present an analysis of the registration component of a proposed image guidance system for image guided liver surgery, using contrast enhanced CT. The analysis is performed on a visually realistic liver phantom and in-vivo porcine data. A robust registration process that can be deployed clinically is a key component of any image guided surgery system. It is also essential that the accuracy of the registration can be quantified and communicated to the surgeon. We summarise the proposed guidance system and discuss its clinical feasibility. The registration combines an intuitive manual alignment stage, surface reconstruction from a tracked stereo laparoscope and a rigid iterative closest point registration to register the intra-operative liver surface to the liver surface derived from CT. Testing of the system on a liver phantom shows that subsurface landmarks can be localised to an accuracy of 2.9 mm RMS. Testing during five porcine liver surgeries demonstrated that registration can be performed during surgery, with an error of less than 10 mm RMS for multiple surface landmarks.

Locally rigid, vessel-based registration for laparoscopic liver surgery

The insufflation of the abdomen in laparoscopic liver surgery leads to significant deformation of the liver. The estimation of the shape and position of the liver after insufflation has many important applications, such as providing surface-based registration algorithms used in image guidance with an initial guess and realistic patient-specific surgical simulation.

Detection and modelling of contacts in explicit finite-element simulation of soft tissue biomechanics

Very preterm birth (less than 32 weeks completed gestation) coincides with a rapid period of brain growth and development. Investigating the changes of certain brain regions may allow the development of biomarkers for predicting neurological outcome. The prefrontal cortex, associated with the executive function, undergoes major changes during the last 10 weeks of pregnancy, and therefore its development may be altered by very-preterm birth. In this paper we use surface-based spectral matching techniques to analyse how the prefrontal cortex develops between 30 weeks and 40 weeks equivalent gestational age in 5 infants born preterm. Using this method, we can accurately map the regions where the secondary and tertiary sulci and gyri of the prefrontal cortex will form. Additionally, measurements of cortical curvature can be used to estimate the local bending energy required to generate the observed pattern of cortical folding. Longitudinal measurement of the cortical folding change can provide information about the mechanical properties of the underlying tissue and may be useful in discriminating mechanical changes during growth in this vulnerable period of development.

SmartLiver Image Guidance System for Laparoscopic Liver Resection

Realistic modelling of mechanical interactions between tissues is an important part of surgical simulation, and may become a valuable asset in surgical computer guidance. Unfortunately, it is also computationally very demanding. Explicit matrix-free FEM solvers have been shown to be a good choice for fast tissue simulation, however little work has been done on contact algorithms for such FEM solvers. This work introduces such an algorithm that is capable of handling both deformable-deformable (soft-tissue interacting with soft-tissue) and deformable-rigid (e.g. soft-tissue interacting with surgical instruments) contacts. The proposed algorithm employs responses computed with a fully matrix-free, virtual node-based version of the model first used by Taylor and Flanagan in PRONTO3D. For contact detection, a bounding-volume hierarchy (BVH) capable of identifying self collisions is introduced. The proposed BVH generation and update strategies comprise novel heuristics to minimise the number of bounding volumes visited in hierarchy update and collision detection. Aside from speed, stability was a major objective in the development of the algorithm, hence a novel method for computation of response forces from C0-continuous normals, and a gradual application of response forces from rate constraints has been devised and incorporated in the scheme. The continuity of the surface normals has advantages particularly in applications such as sliding over irregular surfaces, which occurs, e.g., in simulated breathing. The effectiveness of the scheme is demonstrated on a number of meshes derived from medical image data and artificial test cases.