Diffeomorphic Registration Using B-Splines

By Daniel Rueckert, Paul Aljabar, Rolf A. Heckemann, Joseph V. Hajnal, Alexander Hammers. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2006

369 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b2624d002ac88fd582159687a426e94998ce13aa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Nikos Paragios
2. John Ashburner
3. Aristeidis Sotiras
4. Tom Kamiel Magda Vercauteren
5. Daniel Rueckert
6. Ben Glocker
7. Brian B. Avants
8. James C. Gee

In Computational Mechanics

1. Nikos Paragios
2. Aristeidis Sotiras
3. Christos Davatzikos
4. John Ashburner
5. Nikos Komodakis
6. Ben Glocker
7. Xavier Pennec
8. G. Tziritas

In Computer Vision and Pattern Recognition

1. Nikos Paragios
2. Christos Davatzikos
3. Aristeidis Sotiras
4. Mert Rory Sabuncu
5. Dinggang Shen
6. Daniel Rueckert
7. Ben Glocker
8. Ronald M. Summers

Citations in the corpus

• DRAMMS: Deformable Registration via Attribute Matching and Mutual-Saliency Weighting (2009, methodology): “In our implementation, the diffeomorphic FFD (Rueckert et al., 2006) is chosen, because of its flexibility to handle a smooth and diffeomorphic deformation field, and its popularity among the deformable registration community.”
• Machine Learning and Radiology (2012, methodology): “Rueckert et al. used B-spines in diffeomorphic registration which serve as a way to parameterize a deformation field (Rueckert et al., 2006).”
• Diffeomorphic demons: Efficient non-parametric image registration (2009, methodology): “In (Rueckert et al., 2006), the authors proposed a parametric approach by composing a set of constrained B-spline transformations.”
• Dense image registration through MRFs and efficient linear programming (2008, methodology): “Following [30], it is very easy to guarantee diffeomorphic deformations by hard constraints.”
• Deformable Medical Image Registration: A Survey (2013, background): “[138] imposed the hard constraints proven in [139] to produce diffeomorphic deformation fields.”
• MSM: a new flexible framework for Multimodal Surface Matching☆ (2014, positive): “of regular volumetric spline-based interpolation (Rueckert et al., 2006), although for irregularly spaced control points on a spherical surface this is not yet proven.”
• Non-parametric Diffeomorphic Image Registration with the Demons Algorithm (2007, methodology): “In [6], the authors proposed a parametric approach by composing a set of constrained B-spline transformations.”
• MRF-Based Deformable Registration and Ventilation Estimation of Lung CT (2013, background): “In [27] it is shown that B-spline transformations can also be guaranteed to be diffeomorphic if the maximum displacement of each control point is limited to times the grid spacing.”
• Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration (2009, background): “A penalty term for folding in the transformation is described in (Rueckert et al., 2006).”