Abstract:
Molecular dynamics simulations of proteins play a growing role in various fields such as pharmaceutical, biochemical and medical research. Accordingly, the need for high quality visualization of these protein systems raises. Highly interactive visualization techniques are especially needed for the analysis of time-dependent molecular simulations. Beside various other molecular representations the surface representations are of high importance for these applications. So far, users had to accept a trade-off between rendering quality and performance—particularly when visualizing trajectories of time-dependent protein data. We present a new approach for visualizing the Solvent Excluded Surface of proteins using a GPU ray casting technique and thus achieving interactive frame rates even for long protein trajectories where conventional methods based on precomputation are not applicable. Furthermore, we propose a semantic simplification of the raw protein data to reduce the visual complexity of the surface and thereby accelerate the rendering without impeding perception of the protein’s basic shape. We also demonstrate the application of our Solvent Excluded Surface method to visualize the spatial probability density for the protein atoms over the whole period of the trajectory in one frame, providing a qualitative analysis of the protein flexibility.

Paper available at ACM.

Abstract

The iterative reconstruction algorithms for X-ray CT image reconstruction suffer from their high computational cost. Recently Nvidia releases common unified device architecture (CUDA), allowing developers to access to the processing power of Nvidia graphical processing units (GPUs), in order to perform general purpose computations. The use of the GPU, as an alternative computation platform, allows decreasing processing times, for parallel algorithms. This paper aims to demonstrate the feasibility of such an implementation for the iterative image reconstruction. The ordered subsets convex (OSC) algorithm, an iterative reconstruction algorithm for transmission tomography, has been developed with CUDA. The performances have been evaluated and compared with another implementation using a single CPU node. The result shows that speed-ups of two orders of magnitude, with a negligible impact on image accuracy, have been observed.

Paper available at IEEE.

   Due to the very long timescales involved (μs−s), theoretical modeling of fundamental biological processes including folding, misfolding, and mechanical unraveling of biomolecules, under physiologically relevant conditions, is challenging even for distributed computing systems. Graphics Processing Units (GPUs) are emerging as an alternative programming platform to the more traditional CPUs as they provide high raw computational power that can be utilized in a wide range of scientific applications.
   Using a coarse-grained Self Organized Polymer (SOP) model, we have developed and tested the GPU-based implementation of Langevin simulations for proteins (SOP-GPU program). Simultaneous calculation of forces for all particles is implemented using either the particle based or the interacting pair based parallelization, which leads to a ~30-fold acceleration compared to an optimized CPU version of the program. We assess the computational performance of an end-to-end application of the SOP-GPU program, where all steps of the algorithm are running on the GPU, by profiling the associated simulation time and memory usage for a number of small proteins, long protein fibers, and large-size protein assemblies. The SOP-GPU package can now be used in the theoretical exploration of the mechanical properties of large-size protein systems to generate the force-extension and forceindentation profiles under the experimental conditions of force application, and to relate the results of single-molecule experiments in vitro and in silico. 

Abstract:
We present a novel approach to fluid simulation over complex dynamic geometry designed for the specific context of virtual surgery simulation. The method combines a surface-based fluid simulation model with a multi-layer depth peeling representation to allow realistic yet efficient simulation of bleeding on complex surfaces undergoing geometry and topology modifications. Our implementation allows for fast fluid propagation and accumulation over the entire scene, and runs on the GPU at a constant low cost that is independent of the amount of blood in the scene. The proposed bleeding simulation is integrated in a complete simulator for brain tumor resection, where trainees have to manage blood aspiration and tissue/vessel cauterization while they perform virtual surgery tasks.

Paper available at ACM.

Abstract:

We propose a system for thoracic aortic stent-graft deployment that employs a magnetic tracking system (MTS) and intra-operative ultrasound (US). A preoperative plan is first performed using a GPU-accelerated cardiac modeling method to determine the target position of the stent-graft. During the surgery, a MTS is employed to track sensors embedded in the catheter, cannula and the US probe, while a fiducial landmark based registration is used to map the patients coordinate to the image coordinate. The surgical target is tracked in real time via a calibrated intra-operative US image. Under the guidance of the MTS integrated with the real-time US images, the stent-graft can be deployed to the target position without the use of ionizing radiation. This navigation approach was validated using both phantom and animal studies. In the phantom study we demonstrate a US calibration accuracy of 1.5±0.47 mm, and a deployment error of 1.4 ± 0.16mm. In the animal study we performed experiments on five porcine subjects and recorded fiducial, target and deployment errors of 2.5 ± 0.32mm, 4.2 ± 0.78mm, and 2.43 ± 0.69mm, respectively. These results demonstrate that delivery and deployment of thoracic stent-graft under MTS-guided navigation using US imaging is feasible and appropriate for clinical application.

Paper available at IEEE.