A high-performance computing voxel-based analysis pipeline for the rodent brain with a formal validation framework
Robert J. Anderson, James J. Cook, Natalie Delpratt, John C. Nouls, Bin Gu, James O. McNamara, G. Allan Johnson, Alexandra Badea
Duke University Medical Center, Durham, NC
Neuroinformatics volume 17, pages451–472(2019). Open Access
Voxel-based analysis (VBA) of preclinical magnetic resonance images is a powerful research tool for neuroscientists, but its use is limited by the prohibitively high computational demands in this domain. We have developed an automated VBA processing pipeline running on a high-performance computing cluster to mitigate this impediment, and can now routinely produce VBA results in 1-3 days for studies comprising large multidimensional arrays--a task that previously took upward of a month. Attempts to rigorously validate the pipeline have revealed a need for more complete and quantitative VBA evaluation methods. To address this, we propose a validation framework consisting of Jacobian calculation testing, morphological phantom creation, and three metrics derived from phantom VBA. The adoption of such a framework should facilitate the creation and communication of VBA results with increased confidence and integrity. We have used this framework to guide the selection of spatial registration parameters in a VBA study involving a mouse model of epilepsy. Due to significantly shortened processing times we have been able to explore multiple parameter sets and examine how alternative choices can impact VBA results. Additionally, testing the Jacobian calculation has revealed greater reliability when using the geometric rather than the finite differences method, and removed a potential source of confusion in this critical VBA processing step in regards to the direction of the warp used. Verifying the accuracy of VBA has so far not received the attention it rightfully deserves, and should be the focus of a broader effort within the community. We hope that by addressing the serious challenges posed by processing times and ensuring the reliability of results, this work will precipitate the ubiquitous adoption of high-quality VBA techniques among preclinical neuroimaging researchers.
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Copyright (c) 2015, May-Anh Vu
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Acknowledgements
The authors would like to thank James Cook, Center for In Vivo Microscopy, Duke University. We are grateful for support from Duke University Exploratory Research Fund, NIA K01-AG041211, Duke Center for In Vivo Microscopy (NIH/NIBIB P41 EB015897)
