Abstract #2985
GPU imaging analysis for ultra-fast non-Gaussian diffusion mapping
Marco Palombo 1,2 , Dianwen Zhang 3 , Chen Zhu 4 , Julien Valette 1 , Alessandro Gozzi 5 , Angelo Bifone 5 , Andrea Messina 6 , Gianluca Lamanna 7 , and Silvia Capuani 6,8
1
CEA/DSV/I2BM/MIRCen, Fontenay-aux-Roses,
France, France,
2
IPCF-UOS
Roma, Phys. Dpt., Sapienza University, Rome, Rome,
Italy,
3
ITG,
Beckman Institute, UIUC, Urbana, Illinois, United
States,
4
College
of Economics & Management, CAU, Beijing, China,
5
IIT,
Center for Neuroscience and Cognitive Systems @ UniTn,
Rovereto, Italy,
6
Physics
Dpt., Sapienza University, Rome, Italy,
7
INFN,
Pisa Section, Pisa, Italy,
8
IPCF-UOS
Roma, Phys. Dept., Sapienza University, Rome, Italy
The application of graphics processing units (GPUs) for
diffusion-weighted NMR (DW-NMR) images reconstruction by
using non-Gaussian diffusion models is presented. The
image processing based on non-Gaussian models (such as
Kurtosis and stretched exponential) currently are time
consuming for any application in real-time diagnostics.
Non-Gaussian diffusion imaging processing was
implemented on the massively parallel architecture of
GPUs, by employing a scalable parallel LM algorithm
(GPU-LMFit) optimized for the Nvidia CUDA platform. Our
results demonstrate that it is possible to reduce the
time for massive image processing from some hours to
some seconds, finally enabling automated parametric
non-Gaussian DW-NMR analyses in real-time.
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