Abstract
High Angular Resolution Diffusion Imaging (HARDI) is a type of brain imaging that collects a very large amount of data, and if many subjects are considered then it amounts to a big data framework (e.g., the human connectome project has 20 Terabytes of data). HARDI is also becoming increasingly relevant for clinical settings (e.g., detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter-WM anatomy using tractography). Thus, this method is becoming a routine assessment in clinical settings. In such settings, the computation time is critical, and finding forms of reducing the processing time in high computation processes such as Diffusion Spectrum Imaging (DSI), a form of HARDI data, is very relevant to increase data-processing speed. Here we analyze a method for reducing the computation time of the dMRI-based axonal orientation distribution function h by using Monte Carlo sampling-based methods for voxel selection. Results evidenced a robust reduction in required data sampling of about 50 % without losing signal’s quality. Moreover, we show that the convergence to the correct value in this type of Monte Carlo HARDI/DSI data-processing has a linear improvement in data-processing speed of the ODF determination. Although further improvements are needed, our results represent a promissory step for future processing time reduction in big data.
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Acknowledgments
We thank the financial support by QREN, FEDER, COMPETE, Investigador FCT, FCT Ciencia 2007, FCT PTDC/SAU-BEB/100147/2008, FCT Project Scope UID/CEC/00319/2013, and the ERASMUS projects (FCT stands for “Fundação para a Ciência e Tecnologia”). We are thankful the relevant scientific conversations with Alard Roebroeck, Rainer Goebel, Van Wedeen, and Gina Caetano. Data collection for this work was in part from “Human Connectome Project” (HCP; Principal Investigators: Bruce Rosen, M.D., Ph.D., Arthur W. Toga, Ph.D., Van J. Weeden, MD). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California.
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This article is part of the Topical Collection on Systems-Level Quality Improvement
An erratum to this article is available at http://dx.doi.org/10.1007/s10916-016-0683-2.
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Lori, N.F..., Ibañez, A., Lavrador, R. et al. Processing Time Reduction: an Application in Living Human High-Resolution Diffusion Magnetic Resonance Imaging Data. J Med Syst 40, 243 (2016). https://doi.org/10.1007/s10916-016-0594-2
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DOI: https://doi.org/10.1007/s10916-016-0594-2