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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dang, V.Q. El-Araby, E. Dao, L.H. Lin-Ching Chang |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Electr. Eng. & Comput. Sci., Catholic Univ. of America, Washington, DC, USA (Dang, V.Q.; El-Araby, E.; Dao, L.H.; Lin-Ching Chang) |
| Abstract | Diffusion Tensor Imaging (DTI) is a non-invasive magnetic resonance technique that produces in vivo images of biological tissues with local microstructural characteristics such as water diffusion. It can be used, for example, to localize white matter lesions, or in neuro-navigation surgery of brain tumors. Diffusion tensor maps are usually computed on a voxel-by-voxel basis by fitting the signal intensities of diffusion weighted images as a function of their corresponding data acquisition parameters. This processing is highly computation-intensive and can be time-consuming which constraints the clinical use of DTI. This study presents the application of using high performance GPU clusters in diffusion tensor estimation by accelerating the multivariate non-linear regression. The results are tested in simulated DTI brain datasets and show significant performance gain in tensor fitting in addition to favorable scalability characteristics. The proposed GPU implementation framework can further promote the clinical use of DTI, and can be used to accelerate statistical analysis of DTI where Monte Carlo simulations are employed, or readily applied to quantitative assessment of DTI using bootstrap analysis. |
| Sponsorship | IEEE Comput. Soc. |
| Starting Page | 265 |
| Ending Page | 268 |
| File Size | 267638 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781479904945 |
| ISSN | 2160052X |
| e-ISBN | 9781479904938 |
| DOI | 10.1109/ASAP.2013.6567587 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-05 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Tensile stress Graphics processing units Diffusion tensor imaging Estimation Acceleration Mathematical model Scalability high performance clusters diffusion tensor imaging (DTI) nonlinear diffusion tensor estimation Levenberg-Marquardt algorithm GPU |
| Content Type | Text |
| Resource Type | Article |
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