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Longitudinal Image Registration with Temporally-dependent Image Similarity Measure

Institution:
1Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
2Kitware, Inc., Carrboro, NC, USA.
3Emory University, Atlanta, GA, USA.
Publisher:
IEEE Engineering in Medicine and Biology Society
Publication Date:
Oct-2013
Journal:
IEEE Trans Med Imaging
Volume Number:
32
Issue Number:
10
Pages:
1939-51
Citation:
IEEE Trans Med Imaging. 2013 Oct;32(10):1939-51.
PubMed ID:
23846465
PMCID:
PMC3947578
Keywords:
Deformable registration, longitudinal registration, magnetic resonance imaging (MRI), nonuniform appearance change
Appears in Collections:
NA-MIC
Sponsors:
P41 EB002025/EB/NIBIB NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
P30 HD003110/HD/NICHD NIH HHS/United States
P50 MH078105/MH/NIMH NIH HHS/United States
R01 NS061965/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Csapo I., Davis B., Shi Y., Sanchez M., Styner M., Niethammer M. Longitudinal Image Registration with Temporally-dependent Image Similarity Measure. IEEE Trans Med Imaging. 2013 Oct;32(10):1939-51. PMID: 23846465. PMCID: PMC3947578.
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Longitudinal imaging studies are frequently used to investigate temporal changes in brain morphology and often require spatial correspondence between images achieved through image registration. Beside morphological changes, image intensity may also change over time, for example when studying brain maturation. However, such intensity changes are not accounted for in image similarity measures for standard image registration methods. Hence, 1) local similarity measures, 2) methods estimating intensity transformations between images, and 3) metamorphosis approaches have been developed to either achieve robustness with respect to intensity changes or to simultaneously capture spatial and intensity changes. For these methods, longitudinal intensity changes are not explicitly modeled and images are treated as independent static samples. Here, we propose a model-based image similarity measure for longitudinal image registration that estimates a temporal model of intensity change using all available images simultaneously.

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