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Hum Brain Mapp. 2017 Apr 22. doi: 10.1002/hbm.23615. [Epub ahead of print]

Single-subject independent component analysis-based intensity normalization in non-quantitative multi-modal structural MRI.

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NeuroCure Clinical Research Center, NeuroCure Cluster of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Medical Image Analysis Center (MIAG AG), Basel, Switzerland.
Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité, Charité - Universitätsmedizin Berlin, Berlin, Germany.


Non-quantitative MRI is prone to intersubject intensity variation rendering signal intensity level based analyses limited. Here, we propose a method that fuses non-quantitative routine T1-weighted (T1w), T2w, and T2w fluid-saturated inversion recovery sequences using independent component analysis and validate it on age and sex matched healthy controls. The proposed method leads to consistent and independent components with a significantly reduced coefficient-of-variation across subjects, suggesting potential to serve as automatic intensity normalization and thus to enhance the power of intensity based statistical analyses. To exemplify this, we show that voxelwise statistical testing on single-subject independent components reveals in particular a widespread sex difference in white matter, which was previously shown using, for example, diffusion tensor imaging but unobservable in the native MRI contrasts. In conclusion, our study shows that single-subject independent component analysis can be applied to routine sequences, thereby enhancing comparability in-between subjects. Unlike quantitative MRI, which requires specific sequences during acquisition, our method is applicable to existing MRI data. Hum Brain Mapp, 2017.


independent component analysis; intensity normalization; multi-modal MRI; sexual dimorphism; structural MRI


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