Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

Surgical Planning Laboratory

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Gallery View | Download Statistics | Help | Import | Log in

A Locally Adaptive Regularization Based on Anisotropic Diffusion for Deformable Image Registration of Sliding Organs

1Kitware Inc., Carrboro, NC, USA.
2Department of Computer Science and the Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
IEEE Engineering in Medicine and Biology Society
Publication Date:
IEEE Trans Med Imaging
Volume Number:
Issue Number:
IEEE Trans Med Imaging. 2013 Nov;32(11):2114-26.
PubMed ID:
sliding motion, locally adaptive regularization, deformable image registration, respiratory motion, thoracic CT, abdominal CT
Appears in Collections:
R01 CA138419/CA/NCI NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
R41 CA153488/CA/NCI NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
P41 EB002025/EB/NIBIB NIH HHS/United States
R41 NS081792/NS/NINDS NIH HHS/United States
NSF EECS 0925875
NSF ECCS 1148870
Generated Citation:
Pace D., Aylward S.R., Niethammer M. A Locally Adaptive Regularization Based on Anisotropic Diffusion for Deformable Image Registration of Sliding Organs. IEEE Trans Med Imaging. 2013 Nov;32(11):2114-26. PMID: 23899632. PMCID: PMC4112204.
Downloaded: 1120 times. [view map]
Paper: Download, View online
Export citation:
Google Scholar: link

We propose a deformable image registration algorithm that uses anisotropic smoothing for regularization to find correspondences between images of sliding organs. In particular, we apply the method for respiratory motion estimation in longitudinal thoracic and abdominal CT scans. The algorithm uses locally adaptive diffusion tensors to determine the direction and magnitude with which to smooth the components of the displacement field that are normal and tangential to an expected sliding boundary. Validation was performed using synthetic, phantom, and fourteen clinical datasets, including the publicly available DIR-Lab dataset. We show that motion discontinuities caused by sliding can be effectively recovered, unlike conventional regularizations that enforce globally smooth motion. In the clinical datasets, target registration error (TRE) showed improved accuracy for lung landmarks compared to the diffusive regularization. We also present a generalization of our algorithm to other sliding geometries, including sliding tubes (e.g. needles sliding through tissue, or contrast agent flowing through a vessel). Potential clinical applications of this method include longitudinal change detection and radiotherapy for lung or abdominal tumours, especially those near the chest or abdominal wall.

Additional Material
1 File (311.633kB)
Pace-IEEETMI2013-fig6.jpg (311.633kB)