Accurate biomechanical modelling of soft tissue is a key aspect for achieving realistic surgical simulations. However, because medical simulation is a multi-disciplinary area, researchers do not always have sufficient resources to develop an efficient and physically rigorous model for organ deformation. We address this issue by implementing a CUDA-based nonlinear finite element model into the SOFA open source framework. The proposed model is an anisotropic visco-hyperelastic constitutive formulation implemented on a graphical processor unit (GPU). After presenting results on the model‘s performance we illustrate the benefits of its integration within the SOFA framework on a simulation of cataract surgery.

Paper available at ACM.

Abstract:
Electrical Impedance Tomography (EIT) is an imaging technology based on impedance measurements. To retrieve meaningful insights from these measurements, EIT relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of current flows therein. The non-homogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeoff between physical accuracy and technical feasibility, which at present severely limits the capabilities of EIT. This work presents a complete algorithmic flow for an accurate EIT modeling environment featuring high anatomical fidelity with a spatial resolution equal to that provided by an MRI and a novel realistic complete electrode model implementation. At the same time we demonstrate that current Graphics Processing Unit (GPU)-based platforms provide enough computational power that a domain discretized with 5 million voxels can be numerically modeled in about 30 seconds.

Paper available at IEEE.

Abstract:
Manipulating small objects such as needles, screws or plates inside the human body during minimally invasive surgery can be very difficult for less experienced surgeons, due to the loss of 3D depth perception. This paper presents an approach for tracking a suturing needle using a standard endoscope. The resulting pose information of the needle is then used to generate artificial 3D cues on the 2D screen to optimally support surgeons during tissue suturing. Additionally, if an external tracking device is provided to report the endoscope‘s position, the suturing needle can be tracked in a hybrid fashion with submillimeter accuracy. Finally, a visual navigation aid can be incorporated, if a 3D surface is intraoperatively reconstructed from video or registered from preoperative imaging.

Paper available at ACM.

Abstract:

Most small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. Image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp et al. Currently there are a strong need to speed-up the reconstruction of XRay CT data in order to extend its clinical applications. We present an efficient modular implementation of an FDK-based reconstruction algorithm that takes advantage of the parallel computing capabilities and the efficient bilinear interpolation provided by general purpose graphic processing units (GPGPU). The proposed implementation of the algorithm is evaluated for a high-resolution micro-CT and achieves a speed-up of 46, while preserving the reconstructed image quality.

Paper available at IEEE.

Abstract:

Time resolved three-dimensional (3D) echocardiography generates four-dimensional (3D+time) data sets that bring new possibilities in clinical practice. Image quality of four-dimensional (4D) echocardiography is however regarded as poorer compared to conventional echocardiography where timeresolved 2D imaging is used. Advanced image processing filtering methods can be used to achieve image improvements but to the cost of heavy data processing. The recent development of GPUs (graphics processing unit) enables highly parallel general purpose computations, that considerably reduces the computational time of advanced image filtering methods. In this study multidimensional adaptive filtering of 4D echocardiography was performed using GPUs. Filtering was done using multiple kernels implemented in OpenCL (Open Computing Language) working on multiple subsets of the data. Our results show a substantial speed increase of up to 74 times, resulting in a total filtering time less than 30 seconds on a common desktop. This implies that advanced adaptive image processing can be accomplished in conjunction with a clinical examination. Since the presented GPU processor method scales linearly with the number of processing elements, we expect it to continue scaling with the expected future increases in number of processing elements. This should be contrasted with the increases in data set sizes in the near future following the further improvements in ultrasound probes and measuring devices. It is concluded that GPUs facilitate the use of demanding adaptive image filtering techniques that in turn enhance 4D echocardiographic data sets. The presented general methodology of implementing parallelism using GPUs is also applicable for other medical modalities that generate multidimensional data.

Paper available at IEEE.