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000139298 0247_ $$2doi$$a10.1016/j.neuroimage.2017.02.055
000139298 0247_ $$2pmid$$apmid:28242316
000139298 0247_ $$2pmc$$apmc:PMC5432428
000139298 0247_ $$2ISSN$$a1053-8119
000139298 0247_ $$2ISSN$$a1095-9572
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000139298 037__ $$aDZNE-2020-05620
000139298 041__ $$aEnglish
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000139298 1001_ $$0P:(DE-HGF)0$$aAganj, Iman$$b0$$eCorresponding author
000139298 245__ $$aMid-space-independent deformable image registration.
000139298 260__ $$aOrlando, Fla.$$bAcademic Press$$c2017
000139298 264_1 $$2Crossref$$3print$$bElsevier BV$$c2017-05-01
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000139298 520__ $$aAligning images in a mid-space is a common approach to ensuring that deformable image registration is symmetric - that it does not depend on the arbitrary ordering of the input images. The results are, however, generally dependent on the mathematical definition of the mid-space. In particular, the set of possible solutions is typically restricted by the constraints that are enforced on the transformations to prevent the mid-space from drifting too far from the native image spaces. The use of an implicit atlas has been proposed as an approach to mid-space image registration. In this work, we show that when the atlas is aligned to each image in the native image space, the data term of implicit-atlas-based deformable registration is inherently independent of the mid-space. In addition, we show that the regularization term can be reformulated independently of the mid-space as well. We derive a new symmetric cost function that only depends on the transformation morphing the images to each other, rather than to the atlas. This eliminates the need for anti-drift constraints, thereby expanding the space of allowable deformations. We provide an implementation scheme for the proposed framework, and validate it through diffeomorphic registration experiments on brain magnetic resonance images.
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000139298 650_2 $$2MeSH$$aAlgorithms
000139298 650_2 $$2MeSH$$aAtlases as Topic
000139298 650_2 $$2MeSH$$aBrain: anatomy & histology
000139298 650_2 $$2MeSH$$aBrain Mapping: methods
000139298 650_2 $$2MeSH$$aFemale
000139298 650_2 $$2MeSH$$aHumans
000139298 650_2 $$2MeSH$$aImaging, Three-Dimensional
000139298 650_2 $$2MeSH$$aMagnetic Resonance Imaging
000139298 650_2 $$2MeSH$$aMale
000139298 650_2 $$2MeSH$$aMiddle Aged
000139298 650_2 $$2MeSH$$aSignal Processing, Computer-Assisted
000139298 7001_ $$0P:(DE-HGF)0$$aIglesias, Juan Eugenio$$b1
000139298 7001_ $$0P:(DE-2719)2812134$$aReuter, Martin$$b2$$udzne
000139298 7001_ $$0P:(DE-HGF)0$$aSabuncu, Mert Rory$$b3
000139298 7001_ $$0P:(DE-HGF)0$$aFischl, Bruce$$b4
000139298 77318 $$2Crossref$$3journal-article$$a10.1016/j.neuroimage.2017.02.055$$b : Elsevier BV, 2017-05-01$$p158-170$$tNeuroImage$$v152$$x1053-8119$$y2017
000139298 773__ $$0PERI:(DE-600)1471418-8$$a10.1016/j.neuroimage.2017.02.055$$gVol. 152, p. 158 - 170$$p158-170$$q152<158 - 170$$tNeuroImage$$v152$$x1053-8119$$y2017
000139298 8567_ $$2Pubmed Central$$uhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5432428
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000139298 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2812134$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b2$$kDZNE
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