Large Deformation Inverse Consistent Elastic Image Registration

He, Jie; Christensen, Gary E.

doi:10.1007/978-3-540-45087-0_37

Cited by 30 publications

(20 citation statements)

References 11 publications

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“…We compute these transformations with the LDDMM algorithm that uses the standard one-sided cost function and the novel consistent CIC and CMC method presented in this paper. Thus, we evaluate (14) (15) (16) where is the maximum inverse consistency error and is the average inverse consistency error over the domain. A summary of the maximum and the average inverse consistency errors for each of the three data-sets are shown in Tables I and II, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Practically, however, most registration methods in use have tended to use asymmetric cost functions that lead to a solution that is not inverse consistent when the template and the target images being registered are interchanged. Several papers have recently attempted to make the registration symmetric or inverse consistent [1]- [3], [8], [10], [11], [15], [17], [18], [20]- [22], [24], [25]. Here, we present two novel cost functions in the large deformation image matching framework that are symmetric with respect to the choice of template and target images being matched.…”

Section: Introductionmentioning

confidence: 96%

“…In [22], transformations are made piecewise affine over triangulations of the 2-D domain. In [16], a concatenation of linearized transformations was used to generate a large-deformation inverse-consistent elastic image registration algorithm extending the work presented in [11].…”

Section: Introductionmentioning

confidence: 99%

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Symmetric Data Attachment Terms for Large Deformation Image Registration

Beg

Khan

2007

IEEE Trans. Med. Imaging

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Nonrigid medical image registration between images that are linked by an invertible transformation is an inherently symmetric problem. The transformation that registers the image pair should ideally be the inverse of the transformation that registers the pair with the order of images interchanged. This property is referred to as symmetry in registration or inverse consistent registration. However, in practical estimation, the available registration algorithms have tended to produce inverse inconsistent transformations when the template and target images are interchanged. In this paper, we propose two novel cost functions in the large deformation diffeomorphic framework that are inverse consistent. These cost functions have symmetric data-attachment terms; in the first, the matching error is measured at all points along the flow between template and target, and in the second, matching is enforced only at the midpoint of the flow between the template and target. We have implemented these cost functions and present experimental results to validate their inverse consistent property and registration accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

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Symmetric Data Attachment Terms for Large Deformation Image Registration

Beg

Khan

2007

IEEE Trans. Med. Imaging

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“…To take into account the highly non-linear elastic properties of human tissue (e.g., breast, muscle) several elastic deformation image registration algorithms have been published [3][4][5][6][7][8]. One of the dangers with elastic algorithms, however, is that some parts of the original image might vanish because of overlays or singular distortion fields [9].…”

Section: Introductionmentioning

confidence: 99%

Application and assessment of a robust elastic motion correction algorithm to dynamic MRI

et al. 2006

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The purpose of this study was to assess the performance of a new motion correction algorithm. Twenty-five dynamic MR mammography (MRM) data sets and 25 contrast-enhanced three-dimensional peripheral MR angiographic (MRA) data sets which were affected by patient motion of varying severeness were selected retrospectively from routine examinations. Anonymized data were registered by a new experimental elastic motion correction algorithm. The algorithm works by computing a similarity measure for the two volumes that takes into account expected signal changes due to the presence of a contrast agent while penalizing other signal changes caused by patient motion. A conjugate gradient method is used to find the best possible set of motion parameters that maximizes the similarity measures across the entire volume. Images before and after correction were visually evaluated and scored by experienced radiologists with respect to reduction of motion, improvement of image quality, disappearance of existing lesions or creation of artifactual lesions. It was found that the correction improves image quality (76% for MRM and 96% for MRA) and diagnosability (60% for MRM and 96% for MRA).

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“…Intensity-based registration computes a transformation between corresponding data by matching intensity functions, such as image intensity for image registration or surface curvature for surface geometric registration. Different intensitybased registration algorithms have been recently proposed [61], such as Demons [51,56], spherical Demons [58], elastic registration [23], Large Deformation diffeomorphic Metric Mapping (LDDMM) frameworks [9,10] and so on. On the other hand, landmark-based registration computes a smooth 1-1 dense pointwise correspondence between corresponding data that matches important features [2,[14][15][16]28,[39][40][41]52,54,57].…”

Section: Introductionmentioning

confidence: 99%

Landmark-Matching Transformation with Large Deformation Via n-dimensional Quasi-conformal Maps

2015

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We propose a new method to obtain landmark-matching transformations between n-dimensional Euclidean spaces with large deformations. Given a set of feature correspondences, our algorithm searches for an optimal folding-free mapping that satisfies the prescribed landmark constraints. The standard conformality distortion defined for mappings between 2-dimensional spaces is first generalized to the n-dimensional conformality distortion K ( f ) for a mapping f between n-dimensional Euclidean spaces (n ≥ 3). We then propose a variational model involving K ( f ) to tackle the landmark-matching problem in higher dimensional spaces. The generalized conformality term K ( f ) enforces the bijectivity of the optimized mapping and minimizes its local geometric distortions even with large deformations. Another challenge is the high computational cost of the proposed model. To tackle this, we have also proposed a numerical method to solve the optimization problem more efficiently. Alternating direction method with multiplier is applied to split the optimization problem into two subproblems. Preconditioned conjugate gradient method with multi-grid preconditioner is applied to solve one of the sub-problems, while a fixed-point iteration is proposed to solve another subproblem. Experiments have been carried out on both synthetic examples and lung CT images to compute the diffeomorphic landmark-matching transformation with different landmark constraints. Results show the efficacy of our proposed model to obtain a folding-free landmark-matching transformation between n-dimensional spaces with large deformations.

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Large Deformation Inverse Consistent Elastic Image Registration

Cited by 30 publications

References 11 publications

Symmetric Data Attachment Terms for Large Deformation Image Registration

Symmetric Data Attachment Terms for Large Deformation Image Registration

Application and assessment of a robust elastic motion correction algorithm to dynamic MRI

Landmark-Matching Transformation with Large Deformation Via n-dimensional Quasi-conformal Maps

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