Rapid computation of sodium bioscales using gpu-accelerated image reconstruction

• Published in: International journal of imaging systems and technology Vol. 23; no. 1; pp. 29 - 35
• Main Authors: Atkinson, Ian C., Liu, Geng, Obeid, Nady, Thulborn, Keith R., Hwu, Wen-mei
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.03.2013; Wiley; Wiley Subscription Services, Inc
• Subjects: Applied sciences; Artificial intelligence; Biological and medical sciences; bioscale; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Exact sciences and technology; graphics processing unit processing; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Nervous system; Pattern recognition. Digital image processing. Computational geometry; quantitative sodium magnetic resonance imaging; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Software; Measurement; Brain; Scalability; Central nervous system; Algorithmics; Processing time; bioscale; Nuclear magnetic resonance imaging; graphics processing unit processing; Image reconstruction; Encephalon; Tissue; Consumer; Graphic processing unit; Imaging; quantitative sodium magnetic resonance imaging; Central unit; Quantitative analysis
• ISSN: 0899-9457; 1098-1098
• DOI: 10.1002/ima.22033

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 × 256 × 256 data and 2.4 times faster when processing 128 × 128 × 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. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 29–35, 2013.