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Multimodal Neuroimaging Computing: The Workflows, Methods, and Platforms

1School of IT, The University of Sydney, Sydney, Australia.
2Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
3Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, and the Sydney Medical School, The University of Sydney, Sydney, Australia.
4Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
Publication Date:
Volume Number:
Brain Informatics. 2015 Sep; 2:181-95.
PubMed ID:
Multimodal, Neuroimaging, Medical Image Computing
Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Liu S., Cai W., Liu S., Zhang F., Fulham M., Feng D., Pujol S., Kikinis R. Multimodal Neuroimaging Computing: The Workflows, Methods, and Platforms. Brain Informatics. 2015 Sep; 2:181-95. PMID: 27747508. PMCID: PMC4737665.
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The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms.

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