Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
, 2 (3), 181-195

Multimodal Neuroimaging Computing: The Workflows, Methods, and Platforms

Affiliations
Review

Multimodal Neuroimaging Computing: The Workflows, Methods, and Platforms

Sidong Liu et al. Brain Inform.

Abstract

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.

Keywords: Medical image computing; Multimodal; Neuroimaging.

Figures

Fig. 1
Fig. 1
Overview of the current status and major components of multimodal neuroimaging computing, including neuroimaging modalities, modality-specific computing workflows, multimodal computing methods, algorithms, task-oriented packages, all-integrated platforms, and neuroimaging research communities
Fig. 2
Fig. 2
Experimental visualization of brain tumor case of DTI Challenge 2015 using 3D Slicer. The panel on the left shows the GUI of the Slicer Mosaic Viewer module previously developed by us. The right side shows the four data viewers, each visualizing a specific step in the surgical planning workflows. The up left viewer shows the registered T1 that overlaid on the DTI volume. The up right viewer shows the segmented tumor (green), ventricle (blue), and motor cortex (red) surfaces. The bottom left viewer shows the reconstructed pial surface of the right hemisphere and white matter surface of the left hemisphere. The bottom right viewer interactively visualizes the peritumoral fiber tracts as the user moves the fiducial. (Color figure online)

Similar articles

See all similar articles

Cited by 7 PubMed Central articles

See all "Cited by" articles

References

    1. Kikinis R, Pieper SD, Vosburgh K. 3D Slicer: a platform for subject-specific image analysis, visualization, and clinical support. Intraoper Imaging Image-Guided Therapy. 2014;3(19):277–289. doi: 10.1007/978-1-4614-7657-3_19. - DOI
    1. He B, Liu Z. Multimodal functional neuroimaging: integrating functional MRI and EEG/MEG. IEEE Rev Biomed Eng. 2008;1:23–40. doi: 10.1109/RBME.2008.2008233. - DOI - PMC - PubMed
    1. Knopman AA, Wong CH, Stevenson RJ, et al. The relationship between neuropsychological functioning and FDG–PET hypometabolism in intractable mesial temporal lobe epilepsy. Epilepsy & Behavior. 2015;44:136–142. doi: 10.1016/j.yebeh.2015.01.023. - DOI - PubMed
    1. Zhang D, Wang Y, Zhou L, Yuan H, Shen D. Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage. 2011;55(3):856–867. doi: 10.1016/j.neuroimage.2011.01.008. - DOI - PMC - PubMed
    1. Savadjiev P, Rathi Y, Bouix S, Smith AR, et al. Fusion of white and gray matter geometry: a framework for investigating brain development. Med Image Anal. 2014;18:1349–1360. doi: 10.1016/j.media.2014.06.013. - DOI - PMC - PubMed

LinkOut - more resources

Feedback