Nonlinear Elastic Registration with Unbiased Regularization in Three Dimensions
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/1360
We propose a new nonlinear image registration model which is based on nonlinear elastic regularization and unbiased registration. The nonlinear elastic and the unbiased regularization terms are simplified using the change of variables by introducing an unknown that approximates the Jacobian matrix of the displacement field. This reduces the minimization to involve linear differential equations. In contrast to recently proposed unbiased fluid registration method, the new model is written in a unified variational form and is minimized using gradient descent. As a result, the new unbiased nonlinear elasticity model is computationally more efficient and easier to implement than the unbiased fluid registration. The unbiased large-deformation nonlinear elasticity method was tested using volumetric serial magnetic resonance images and shown to have some advantages for medical imaging applications.

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Categories: Data Representation, Deformable registration, Density Estimation, Density Functions, Derivatives and Integrals, Hypothesis Testing, Image, Information Theory, Linear Algebra, Mathematics, Multi-modality registration, Optimization, Probability, Registration, Registration metrics, Registration optimizers, Transforms
Keywords: unbiased image registration, computational anatomy, information theory, continuum mechanics, magnetic resonance imaging
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