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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yanguang Lin Quanzheng Li Leahy, R.M. |
| Copyright Year | 2010 |
| Description | Author affiliation: Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089 (Yanguang Lin; Quanzheng Li; Leahy, R.M.) |
| Abstract | We describe the use of relatively low-cost GPU-based processors to implement iterative MAP (Maximum a Posteriori) PET image $reconstruction^{1}.$ The algorithm combines accurate system modeling using a factored matrix approach with a preconditioned conjugate gradient method. We describe the procedures used to parallelize the algorithm including use of data reordering schemes and partitioning of calculations to avoid race conditions. Current speed up compared to a quad core CPU is 6.9 using 1 GPU and 10.9 using 2 GPUs. Comparisons of reconstructed images revealed essentially identical images reconstructed using GPU and CPU-only computation. Preliminary results with geometric projection only for GPU-based TOF MAP reconstruction are also presented. Reconstruction using 11 TOF bins takes approximately twice the time per iteration of non-TOF data with the same geometry but also exhibits substantially faster convergence. |
| Starting Page | 2889 |
| Ending Page | 2893 |
| File Size | 1075584 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424491063 |
| ISSN | 10957863 |
| e-ISBN | 9781424491056 |
| DOI | 10.1109/NSSMIC.2010.5874324 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-10-30 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Graphics processing unit Image reconstruction Instruction sets Kernel Detectors Sparse matrices |
| Content Type | Text |
| Resource Type | Article |
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