Tag: medical imaging
GPU Acceleration of Functional Neuroimaging
GPUs accelerate functional neuroimaging, by using three GPUs, 850 TB of data can be analyzed in 10 days, compared to 100 years with conventional software!
fMRI analysis on the GPU – Possibilities and challenges
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.
Fast GPU Based Adaptive Filtering of 4D Echocardiography
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.
Multiscale feature-preserving smoothing of tomographic 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.
Volume visualization: A technical overview with a focus on medical applications
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 fully 3D positron emission tomography (PET) image reconstruction
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.
GPU-based iterative cone-beam CT 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.
TED Video: Visualizing the medical data explosion
Today medical scans produce thousands of images and terabytes of data for a single patient in mere seconds, but how do doctors parse this information and determine what’s useful? At TEDxGöteborg, scientific visualization expert Anders Ynnerman shows us sophisticated new tools — like virtual autopsies — for analyzing this myriad data, and a glimpse at some sci-fi-sounding medical technologies in development. This talk contains some graphic medical imagery.
GPU-accelerated elastic 3D image registration for intra-surgical applications
The graphics processing unit (GPU) can be used to accelerate the calculation of such elastic registrations by using its parallel processing power, and by employing the hardwired tri-linear interpolation capabilities in order to efficiently perform the cubic B-spline evaluation. In this article it is shown that the similarity measure and its derivatives also can be calculated on the GPU, using a two pass approach.
Novel electron tomography software using multi-GPUs
Shortly after its commercial release in May 2010, Digisens is pleased to announce the growing success of its novel electron tomography software DigiECT, which is the result of a two-year fruitful collaboration with Dr. Sergio Marco, Research Director Inserm U759, Institut Curie, Orsay, France. Moreover, DigiECT has been recently adopted by IPCMS-CNRS, a french nanotechnology institute based in Strasbourg, along with two other well-renowned research centers.
