Name
Abstract | ||
|---|---|---|
Quantitative sodium magnetic resonance imaging permits noninvasive measurement of the tissue sodium concentration (TSC) bioscale in the brain. Computing the TSC bioscale requires reconstructing and combining multiple datasets acquired with a non-Cartesian acquisition that highly oversamples the center of k-space. Even with an optimized implementation of the algorithm to compute TSC, the overall processing time exceeds the time required to collect data from the human subject. Such a mismatch presents a challenge for sustained sodium imaging to avoid a growing data backlog and provide timely results. The most computationally intensive portions of the TSC calculation have been identified and accelerated using a consumer graphics processing unit (GPU) in addition to a conventional central processing unit (CPU). A recently developed data organization technique called Compact Binning was used along with several existing algorithmic techniques to maximize the scalability and performance of these computationally intensive operations. The resulting GPU+CPU TSC bioscale calculation is more than 15 times faster than a CPU-only implementation when processing 256 x 256 x 256 data and 2.4 times faster when processing 128 x 128 x 128 data. This eliminates the possibility of a data backlog for quantitative sodium imaging. The accelerated quantification technique is suitable for general three-dimensional non-Cartesian acquisitions and may enable more sophisticated imaging techniques that acquire even more data to be used for quantitative sodium imaging. (c) 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 2935, 2013. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1002/ima.22033 | INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY |
Keywords | Field | DocType |
quantitative sodium magnetic resonance imaging, bioscale, graphics processing unit processing | Iterative reconstruction,Computer vision,Central processing unit,Computer science,Real-time computing,Computational science,Artificial intelligence,Graphics processing unit,Scalability,Computation | Journal |
Volume | Issue | ISSN |
23 | 1 | 0899-9457 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ian C. Atkinson | 1 | 71 | 8.16 |
| Geng (Daniel) Liu | 2 | 2 | 0.71 |
| Nady Obeid | 3 | 11 | 1.59 |
| Keith R. Thulborn | 4 | 71 | 23.53 |
| Wen-mei W. Hwu | 5 | 4322 | 511.62 |