期刊
NEUROIMAGE
卷 48, 期 1, 页码 63-72出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2009.06.060
关键词
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资金
- National Center for Research Resources [P41-RR14075, R01 RR16594-01A1, BIRN002]
- Functional Imaging Biomedical Informatics Research Network (FBIRN) [U24 RR021382]
- National Institute for Biomedical Imaging and Bioengineering [R01 EB001550, R01 EB006758]
- National Institute for Neurological Disorders and Stroke [R01 NS052585-01]
- Mental Illness and Neuroscience Discovery (MIND) Institute
- National Alliance for Medical Image Computing (NAMIC)
- NIH Roadmap for Medical Research [U54 EB005149]
- Ellison Medical Foundation
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. (C) 2009 Elsevier Inc. All rights reserved.
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