Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint

By Torsten Rohlfing, Calvin R. Maurer Jr., David A. Bluemke, Michael A. Jacobs. IEEE Transactions on Medical Imaging, 2003

521 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f619e9e3b6a2247c73cd3f80ca2de04fc131ee1b

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Unsupervised End-to-end Learning for Deformable Medical Image Registration (2017, background): “Commonly adopted cost functions are the mean squared difference [29], mutual information [30], normalized mutual information [31] and normalized correlation [32].”
Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks (2020, background): “score of the subcortical segmentation maps (DSC), the percentage of voxels with non-positive Jacobian determinant (|Jφ|≤0), the standard deviation of the Jacobian determinant on the deformation fields (std(|Jφ|)), the volume change between the segmentation maps before and after transformation (TC) [”
Deformable Medical Image Registration: A Survey (2013, methodology): “In [238], such a strategy was employed to register contrast-enhanced MR breast images.”
Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains (2004, methodology): “As we showed recently (Rohlfing et al., 2003b), the accuracy of segmentation with multiple atlases can be further improved by applying more sophisticated methods for combining the individual segmentations.”
Evaluation of Optimization Methods for Nonrigid Medical Image Registration Using Mutual Information and B-Splines (2007, background): “Well-known examples for are the curvature term [6], the elastic energy [7], and the volume preserving penalty term [ 8 ].”
elastix: A Toolbox for Intensity-Based Medical Image Registration (2010, background): “An example is the incompressibility constraint described by [4], which penalizes compression and expansion of structures.”
Deformable medical image registration: setting the state of the art with discrete methods. (2011, background): “Other regularization terms could penalize higher-order derivatives or aim at favoring deformations with local rigidity (46) or volume preservation (47).”
Medical image registration: a review (2014, methodology): “The Jacobian of the transformation has also been used (Christensen et al., 1997; Noblet et al., 2005; Rohlfing and Maurer, 2001; Rohlfing et al., 2003); in this case, if the Jacobian is equal to one, then the deformation is categorized as incompressible.”
Elastic registration of biological images using vector-spline regularization (2005, methodology): “The most classical regularization involves a term that is equivalent to the Laplacian of the deformation and that favors smoothness [24]–[27].”
Non-rigid image registration: theory and practice. (2004, background): “Rohlfing et al [65] used breathing gated acquisitions to acquire MR liver images in normal subjects and then applied rigid followed by non-rigid registration to match each breathing phase with the end-expiration image.”
Dense image registration through MRFs and efficient linear programming (2008, methodology): “FFD are successfully applied in non-rigid image registration [24,28,31,32].”