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Journal ArticleDOI

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

TL;DR: The preliminary results suggest that incorporation of the incompressibility regularization term improves intensity-based free-form nonrigid registration of contrast-enhanced MR breast images by greatly reducing the problem of shrinkage of Contrast-enhancing structures while simultaneously allowing motion artifacts to be substantially reduced.
Abstract: In this paper, we extend a previously reported intensity-based nonrigid registration algorithm by using a novel regularization term to constrain the deformation. Global motion is modeled by a rigid transformation while local motion is described by a free-form deformation based on B-splines. An information theoretic measure, normalized mutual information, is used as an intensity-based image similarity measure. Registration is performed by searching for the deformation that minimizes a cost function consisting of a weighted combination of the image similarity measure and a regularization term. The novel regularization term is a local volume-preservation (incompressibility) constraint, which is motivated by the assumption that soft tissue is incompressible for small deformations and short time periods. The incompressibility constraint is implemented by penalizing deviations of the Jacobian determinant of the deformation from unity. We apply the nonrigid registration algorithm with and without the incompressibility constraint to precontrast and postcontrast magnetic resonance (MR) breast images from 17 patients. Without using a constraint, the volume of contrast-enhancing lesions decreases by 1%-78% (mean 26%). Image improvement (motion artifact reduction) obtained using the new constraint is compared with that obtained using a smoothness constraint based on the bending energy of the coordinate grid by blinded visual assessment of maximum intensity projections of subtraction images. For both constraints, volume preservation improves, and motion artifact correction worsens, as the weight of the constraint penalty term increases. For a given volume change of the contrast-enhancing lesions (2% of the original volume), the incompressibility constraint reduces motion artifacts better than or equal to the smoothness constraint in 13 out of 17 cases (better in 9, equal in 4, worse in 4). The preliminary results suggest that incorporation of the incompressibility regularization term improves intensity-based free-form nonrigid registration of contrast-enhanced MR breast images by greatly reducing the problem of shrinkage of contrast-enhancing structures while simultaneously allowing motion artifacts to be substantially reduced.
Citations
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Journal ArticleDOI
TL;DR: The software consists of a collection of algorithms that are commonly used to solve medical image registration problems, and allows the user to quickly configure, test, and compare different registration methods for a specific application.
Abstract: Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), different time points (e.g., follow-up scans), and/or different subjects (in case of population studies). A large number of methods for image registration are described in the literature. Unfortunately, there is not one method that works for all applications. We have therefore developed elastix, a publicly available computer program for intensity-based medical image registration. The software consists of a collection of algorithms that are commonly used to solve medical image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. The command-line interface enables automated processing of large numbers of data sets, by means of scripting. The usage of elastix for comparing different registration methods is illustrated with three example experiments, in which individual components of the registration method are varied.

3,444 citations


Cites background from "Volume-preserving nonrigid registra..."

  • ...An example is the incompressibility constraint described by [4], which penalizes compression and expansion of structures....

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  • ...derivatives of the transformation and [2]–[4]....

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  • ...ment of pre- and post-contrast images [2]–[4], updating treat-...

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Journal ArticleDOI
TL;DR: This paper attempts to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain, and provides an extensive account of registration techniques in a systematic manner.
Abstract: Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

1,434 citations


Cites methods from "Volume-preserving nonrigid registra..."

  • ...In [238], such a strategy was employed to register contrast-enhanced MR breast images....

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Journal ArticleDOI
TL;DR: The current state of the art of non-rigid registration is discussed to put on-going research in context and to highlight current and future clinical applications that might benefit from this technology.
Abstract: Image registration is an important enabling technology in medical image analysis. The current emphasis is on development and validation of application-specific non-rigid techniques, but there is already a plethora of techniques and terminology in use. In this paper we discuss the current state of the art of non-rigid registration to put on-going research in context and to highlight current and future clinical applications that might benefit from this technology. The philosophy and motivation underlying non-rigid registration is discussed and a guide to common terminology is presented. The core components of registration systems are described and outstanding issues of validity and validation are confronted.

768 citations


Cites background from "Volume-preserving nonrigid registra..."

  • ...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....

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  • ...In Rohlfing et al [119] and Tanner et al [67] volume-preserving constraints are applied during registration of dynamic images to reduce the effect of enhancing regions on the intensity based registration....

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Journal ArticleDOI
TL;DR: The aim of this paper is to be an introduction to the field, provide knowledge on the work that has been developed and to be a suitable reference for those who are looking for registration methods for a specific application.
Abstract: This paper presents a review of automated image registration methodologies that have been used in the medical field The aim of this paper is to be an introduction to the field, provide knowledge on the work that has been developed and to be a suitable reference for those who are looking for registration methods for a specific application The registration methodologies under review are classified into intensity or feature based The main steps of these methodologies, the common geometric transformations, the similarity measures and accuracy assessment techniques are introduced and described

689 citations


Cites background or methods from "Volume-preserving nonrigid registra..."

  • ...Normally, the similarity measure used for deformable image registration is composed of at least two terms: one related to the voxel intensity or structures similarity, and the other one to the deformation field (Collins and Evans 1997; Ashburner et al. 1999; Rueckert et al. 1999; Lötjönen and Mäkelä 2001; Rohlfing and Maurer 2001; Hermosillo et al. 2002; Rohlfing et al. 2003; Lu et al. 2004; Auzias et al. 2011)....

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  • ...There are several regularisation terms, but one of the most used is related to the second-order derivatives of the transformation, which are related to the bending energy of the transformation (Lötjönen and Mäkelä 2001; Shen and Davatzikos 2002; Rohlfing et al. 2003)....

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  • ...There are several regularisation terms, but one of the most used is related to the second-order derivatives of the transformation, which are related to the bending energy of the transformation (Lötjönen and Mäkelä 2001; Shen and Davatzikos 2002; Rohlfing et al. 2003)....

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  • ...…2008; Tsai et al. 2010), chest/lung (Mattes et al. 2003; Bhagalia et al. 2009), whole thorax (Loeckx et al. 2003), breast (Rueckert et al. 1999; Rohlfing et al. 2003; Schnabel et al. 2003; Washington and Miga 2004; Karaçali 2007; SerifovicTrbalic et al. 2008), abdomen (liver, kidney and…...

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  • ...…the voxel intensity or structures similarity, and the other one to the deformation field (Collins and Evans 1997; Ashburner et al. 1999; Rueckert et al. 1999; Lötjönen and Mäkelä 2001; Rohlfing and Maurer 2001; Hermosillo et al. 2002; Rohlfing et al. 2003; Lu et al. 2004; Auzias et al. 2011)....

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Journal ArticleDOI
TL;DR: The findings show that atlas selection is an important issue in atlas-based segmentation and that, in particular, multi-classifier techniques can substantially increase the segmentation accuracy.

637 citations


Cites methods from "Volume-preserving nonrigid registra..."

  • ...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....

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  • ...For the above reasons, we have chosen to apply a nonrigid registration algorithm by Rueckert et al. (1999) that we have found to be reliable and efficient in previous applications (Rohlfing and Maurer, 2003; Rohlfing et al., 2003a,b)....

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References
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Journal ArticleDOI
TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.
Abstract: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point. The simplex adapts itself to the local landscape, and contracts on to the final minimum. The method is shown to be effective and computationally compact. A procedure is given for the estimation of the Hessian matrix in the neighbourhood of the minimum, needed in statistical estimation problems.

27,271 citations


"Volume-preserving nonrigid registra..." refers methods in this paper

  • ...[37]: the gradient of the cost function is computed, and a simple line search (downhill–simplex algorithm [26] restricted to the direction of the steepest ascent; see [35] for a full description of our...

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Book
31 Jan 1986
TL;DR: Numerical Recipes: The Art of Scientific Computing as discussed by the authors is a complete text and reference book on scientific computing with over 100 new routines (now well over 300 in all), plus upgraded versions of many of the original routines, with many new topics presented at the same accessible level.
Abstract: From the Publisher: This is the revised and greatly expanded Second Edition of the hugely popular Numerical Recipes: The Art of Scientific Computing. The product of a unique collaboration among four leading scientists in academic research and industry, Numerical Recipes is a complete text and reference book on scientific computing. In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines (now well over 300 in all), plus upgraded versions of many of the original routines, this book is more than ever the most practical, comprehensive handbook of scientific computing available today. The book retains the informal, easy-to-read style that made the first edition so popular, with many new topics presented at the same accessible level. In addition, some sections of more advanced material have been introduced, set off in small type from the main body of the text. Numerical Recipes is an ideal textbook for scientists and engineers and an indispensable reference for anyone who works in scientific computing. Highlights of the new material include a new chapter on integral equations and inverse methods; multigrid methods for solving partial differential equations; improved random number routines; wavelet transforms; the statistical bootstrap method; a new chapter on "less-numerical" algorithms including compression coding and arbitrary precision arithmetic; band diagonal linear systems; linear algebra on sparse matrices; Cholesky and QR decomposition; calculation of numerical derivatives; Pade approximants, and rational Chebyshev approximation; new special functions; Monte Carlo integration in high-dimensional spaces; globally convergent methods for sets of nonlinear equations; an expanded chapter on fast Fourier methods; spectral analysis on unevenly sampled data; Savitzky-Golay smoothing filters; and two-dimensional Kolmogorov-Smirnoff tests. All this is in addition to material on such basic top

12,662 citations

Journal ArticleDOI
TL;DR: In this paper, a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image, and an iterative implementation is shown which successfully computes the Optical Flow for a number of synthetic image sequences.

10,727 citations

Proceedings ArticleDOI
12 Nov 1981
TL;DR: In this article, a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image, and an iterative implementation is shown which successfully computes the Optical Flow for a number of synthetic image sequences.
Abstract: Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences. The algorithm is robust in that it can handle image sequences that are quantized rather coarsely in space and time. It is also insensitive to quantization of brightness levels and additive noise. Examples are included where the assumption of smoothness is violated at singular points or along lines in the image.

8,078 citations

Book
01 Jan 1977

8,009 citations


"Volume-preserving nonrigid registra..." refers methods in this paper

  • ...We note that in our approach, unlike the numerical literature [29], [ 48 ], both components of the overall cost function are weighted in a way that allows us to exclusively optimize either of its two parts without requiring nonfinite values for the weight ....

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