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Deformable Image Registration of Sliding Organs using Anisotropic Diffusive Regularization

Institution:
1Kitware Inc., Clifton Park, NY and Carrboro, NC, USA.
2University of North Carolina at Chapel Hill, Department of Computer Science, Chapel Hill, NC, USA.
Publisher:
ISBI 2011
Publication Date:
Mar-2011
Journal:
Proc IEEE Int Symp Biomed Imaging
Pages:
407-13
Citation:
Proc IEEE Int Symp Biomed Imaging. 2011 Mar; 407-13.
PubMed ID:
21785755
PMCID:
PMC3141338
Keywords:
Deformable image registration, regularization, sliding organs, medical imaging
Appears in Collections:
NA-MIC, SLICER
Sponsors:
P41 EB002025/EB/NIBIB NIH HHS/United States
R01 CA138419/CA/NCI NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
R41 CA153488/CA/NCI NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Pace D., Enquobahrie A., Yang H., Aylward S.R., Niethammer M. Deformable Image Registration of Sliding Organs using Anisotropic Diffusive Regularization. Proc IEEE Int Symp Biomed Imaging. 2011 Mar; 407-13. PMID: 21785755. PMCID: PMC3141338.
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Traditional deformable image registration imposes a uniform smoothness constraint on the deformation field. This is not appropriate when registering images visualizing organs that slide relative to each other, and therefore leads to registration inaccuracies. In this paper, we present a deformation field regularization term that is based on anisotropic diffusion and accommodates the deformation field discontinuities that are expected when considering sliding motion. The registration algorithm was assessed first using artificial images of geometric objects. In a second validation, monomodal chest images depicting both respiratory and cardiac motion were generated using an anatomically-realistic software phantom and then registered. Registration accuracy was assessed based on the distances between corresponding segmented organ surfaces. Compared to an established diffusive regularization approach, the anisotropic diffusive regularization gave deformation fields that represented more plausible image correspondences, while giving rise to similar transformed moving images and comparable registration accuracy.

Additional Material
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