Name
Abstract | ||
|---|---|---|
We propose a fast implementation for iterative MR image reconstruction using Graphics Processing Units (GPU). In MRI, iterative reconstruction with conjugate gradient algorithms allows for accurate modeling the physics of the imaging system. Specifically, methods have been reported to compensate for the magnetic field inhomogeneity induced by the susceptibility differences near the air/tissue interface in human brain (such as orbitofrontal cortex). Our group has previously presented an algorithm for field inhomogeneity compensation using magnetic field map and its gradients. However, classical iterative reconstruction algorithms are computationally costly, and thus significantly increase the computation time. To remedy this problem, one can utilize the fact that these iterative MR image reconstruction algorithms are highly parallelizable. Therefore, parallel computational hardware, such as GPU, can dramatically improve their performance. In this work, we present an implementation of our field inhomogeneity compensation technique using NVIDA CUDA(Compute Unified Device Architecture)-enabled GPU. We show that the proposed implementation significantly reduces the computation times around two orders of magnitude (compared with non-GPU implementation) while accurately compensating for field inhomogeneity. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ISBI.2010.5490112 | ISBI |
Keywords | Field | DocType |
iterative reconstruction,magnetic field inhomogeneity,field inhomogeneity compensation,conjugate gradient,gpu,air-tissue interface,neurophysiology,classical iterative reconstruction algorithm,magnetic field map,iterative field-compensated mr image reconstruction,graphics processing units,computer graphics,field inhomogeneity,compute unified device architecture,field inhomogeneity compensation technique,iterative mr image reconstruction,cuda,image reconstruction,computation time,biomedical mri,mri,brain,orbitofrontal cortex,fast implementation,nvida cuda,accelerating iterative,susceptibility differences,parallel computational hardware,conjugate gradient methods,medical image processing,conjugate gradient algorithms,human brain,acceleration,magnetic fields,computational modeling,parallel computer,magnetic resonance imaging,graphics,magnetic susceptibility,physics,magnetic field | Conjugate gradient method,Graphics,Iterative reconstruction,Computer vision,CUDA,Computer science,Image processing,Artificial intelligence,Graphics processing unit,Computer graphics,Computation | Conference |
ISSN | ISBN | Citations |
1945-7928 E-ISBN : 978-1-4244-4126-6 | 978-1-4244-4126-6 | 6 |
PageRank | References | Authors |
0.73 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Zhuo | 1 | 18 | 2.90 |
| Xiao-Long Wu | 2 | 6 | 0.73 |
| Justin P. Haldar | 3 | 350 | 35.40 |
| Wen-mei W. Hwu | 4 | 4322 | 511.62 |
| Zhi-Pei Liang | 5 | 522 | 64.94 |
| Brad Sutton | 6 | 199 | 27.18 |