Tag: medical imaging
We present a parallel implementation of a new deformable image registration algorithm using the Computer Unified Device Architecture (CUDA). The algorithm co-registers preoperative and intraoperative 3-dimensional magnetic resonance (MR) images of a deforming organ.
“NVIDIA Application Lab” at Jülich Supercomputing Centre: Advancing Research in Neuroscience and Astrophysics
NVIDIA today announced that its GPUs will be used by scientists at Germany’s Forschungszentrum Jülich, which hosts the Jülich Supercomputing Centre, one of Europe’s largest and most powerful supercomputing resources, to accelerate advanced neurological research targeted at unlocking secrets of the human brain. NVIDIA also announced a new, multiyear collaboration with the center to drive…
This paper presents optimization strategies for compute- and memory-bound algorithms of neuroimaging for the CUDA architecture
We describe how to perform preprocessing and statistical analysis of fMRI data on the GPU. Non-parametric tests of fMRI data become practically feasible by using the GPU. GPUs are required to handle the future increase in spatial and temporal resolution. GPUs enable more advanced real-time analysis.
In this study multidimensional adaptive filtering of 4D echocardiography was performed using GPUs. Filtering was done using multiple kernels implemented in OpenCL working on multiple subsets of the data.
Computer tomography (CT) has wide application in medical imaging and reverse engineering. Due to the limited number of projections used in reconstructing the volume, the resulting 3D data is typically noisy. Contouring such data, for surface extraction, yields surfaces with localised artifacts of complex topology.
In this paper, we review volumetric image visualization pipelines, algorithms, and medical applications. We also illustrate our algorithm implementation and evaluation results, and address the advantages and drawbacks of each algorithm in terms of image quality and efficiency.
Fast and efficient statistically based image reconstruction is highly demanded for state-of-art high-resolution PET scanners. The system matrix that defines the mapping from the image space to the data space is the key to high-resolution image reconstruction.
The x-ray imaging dose from serial cone-beam computed tomography (CBCT) scans raises a clinical concern in most image-guided radiation therapy procedures. It is the goal of this paper to develop a fast graphic processing unit (GPU)-based algorithm to reconstruct high-quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose.