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Accurate and Robust Brain Image Alignment using Boundary-based Registration

Greve D.N., Fischl B.


Institution:	Martinos Center for Biomedical Imaging, 143 13th Street, Charlestown, MA, USA. greve@nmr.mgh.harvard.edu
Publisher:	Neuroimage
Publication Date:	Oct-2009
Volume Number:	48
Issue Number:	1
Pages:	63-72
Citation:	Neuroimage. 2009 Oct 15;48(1):63-72.
PubMed ID:	19573611
PMCID:	PMC2733527
Appears in Collections:	NA-MIC
Sponsors:	BIRN002 () funded by PHS HHS P41 RR14075 (RR) funded by NCRR NIH HHS R01 EB001550 (EB) funded by NIBIB NIH HHS R01 EB006758 (EB) funded by NIBIB NIH HHS R01 NS052585-01 (NS) funded by NINDS NIH HHS R01 RR16594-01A1 (RR) funded by NCRR NIH HHS U24 RR021382 (RR) funded by NCRR NIH HHS U54 EB005149 (EB) funded by NIBIB NIH HHS
Generated Citation:	Greve D.N., Fischl B. Accurate and Robust Brain Image Alignment using Boundary-based Registration. Neuroimage. 2009 Oct 15;48(1):63-72. PMID: 19573611. PMCID: PMC2733527.
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The fine spatial scales of the structures in the human brain represent an enormous challenge to the successful integration of information from different images for both within- and between-subject analysis. While many algorithms to register image pairs from the same subject exist, visual inspection shows that their accuracy and robustness to be suspect, particularly when there are strong intensity gradients and/or only part of the brain is imaged. This paper introduces a new algorithm called Boundary-Based Registration, or BBR. The novelty of BBR is that it treats the two images very differently. The reference image must be of sufficient resolution and quality to extract surfaces that separate tissue types. The input image is then aligned to the reference by maximizing the intensity gradient across tissue boundaries. Several lower quality images can be aligned through their alignment with the reference. Visual inspection and fMRI results show that BBR is more accurate than correlation ratio or normalized mutual information and is considerably more robust to even strong intensity inhomogeneities. BBR also excels at aligning partial-brain images to whole-brain images, a domain in which existing registration algorithms frequently fail. Even in the limit of registering a single slice, we show the BBR results to be robust and accurate.

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