Intensity-Based Non-rigid Registration Using Adaptive Multilevel Free-Form Deformation with an Incompressibility Constraint

Confidence Estimation for Medical Image Registration Based On Stereo Confidences

In this paper, we propose a novel method to estimate the confidence of a registration that does not require any ground truth, is independent from the registration algorithm and the resulting confidence is correlated with the amount of registration error. We first apply a local search to match patterns between the registered image pairs. Local search induces a cost space per voxel which we explore further to estimate the confidence of the registration similar to confidence estimation algorithms for stereo matching. We test our method on both synthetically generated registration errors and on real registrations with ground truth. The experimental results show that our confidence measure can estimate registration errors and it is correlated with local errors.

Temporal sparse free-form deformations

FFD represent a widely used model for the non-rigid registration of medical images. The balance between robustness to noise and accuracy in modelling localised motion is typically controlled by the control point grid spacing and the amount of regularisation. More recently, TFFD have been proposed which extend the FFD approach in order to recover smooth motion from temporal image sequences. In this paper, we revisit the classic FFD approach and propose a sparse representation using the principles of compressed sensing. The sparse representation can model both global and local motion accurately and robustly. We view the registration as a deformation reconstruction problem. The deformation is reconstructed from a pair of images (or image sequences) with a sparsity constraint applied to the parametric space. Specifically, we introduce sparsity into the deformation via L1 regularisation, and apply a bending energy regularisation between neighbouring control points within each level to encourage a grouped sparse solution. We further extend the sparsity constraint to the temporal domain and propose a TSFFD which can capture fine local details such as motion discontinuities in both space and time without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate deformations in dynamic 2D and 3D image sequences. Compared to the classic FFD and TFFD approach, a significant increase in registration accuracy can be observed in natural images as well as in cardiac images.

Registration using sparse free-form deformations

Non-rigid image registration using free-form deformations (FFD) is a widely used technique in medical image registration. The balance between robustness and accuracy is controlled by the control point grid spacing and the amount of regularization. In this paper, we revisit the classic FFD registration approach and propose a sparse representation for FFDs using the principles of compressed sensing. The sparse free-form deformation model (SFFD) can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D image sequences. Compared to the classic FFD approach, a significant increase in registration accuracy can be observed in natural images (61%) as well as in cardiac MR images (53%) with discontinuous motions.

Breast image registration techniques: a survey

Breast cancer is the most common type of cancer in women worldwide. Image registration plays an important role in breast cancer detection. This paper gives an overview of the current state-of-the-art in the breast image registration techniques. For the intramodality registration techniques, X-ray, MRI, and ultrasound are the primary focuses of interest. Intermodality techniques will cover the combination of different modalities. Validation of breast registration methods is also discussed.

Physically correct mesh manipulation in multi-level free-form deformation-based nonrigid registration

We present a new solution to prevent mesh folding artifacts common in free-form deformation-based nonrigid registration. Our algorithm imposes linear bounds on the search space of control point locations, thereby enabling the use of constrained optimization algorithms. We also introduce a new method for controlling the mesh rigidity, based on maximum voxel displacement. Our method allows local control of the deformation, based on a priori knowledge of the magnitude of possible local deformations.

Registration-assisted segmentation of real-time 3-D echocardiographic data using deformable models

Real-time three-dimensional (3-D) echocardiography is a new imaging modality that presents the unique opportunity to visualize the complex 3-D shape and motion of the left ventricle (LV) in vivo and to measure the associated global and local function parameters. To take advantage of this opportunity in routine clinical practice, automatic segmentation of the LV in the 3-D echocardiographic data, usually hundreds of megabytes large, is essential. We report a new segmentation algorithm for this task. Our algorithm has two distinct stages, initialization of a deformable model and its refinement, which are connected by a dual "voxel + wiremesh" template. In the first stage, mutual-information-based registration of the voxel template with the image to be segmented helps initialize the wiremesh template. In the second stage, the wiremesh is refined iteratively under the influence of external and internal forces. The internal forces have been customized to preserve the nonsymmetric shape of the wiremesh template in the absence of external forces, defined using the gradient vector flow approach. The algorithm was validated against expert-defined segmentation and demonstrated acceptable accuracy. Our segmentation algorithm is fully automatic and has the potential to be used clinically together with real-time 3-D echocardiography for improved cardiovascular disease diagnosis.

Adaptive registration using local information measures

Rapidly advancing registration methods increasingly employ warping transforms. High degrees of freedom (DOF) warpings can be specified by manually placing control points or instantiating a regular, dense grid of control points everywhere. The former approach is laborious and prone to operator bias, whereas the latter is computationally expensive. We propose to improve upon the latter approach by adaptively placing control points where they are needed. Local estimates of mutual information (MI) and entropy are used to identify local regions requiring additional DOF.

The adaptive bases algorithm for intensity-based nonrigid image registration

Nonrigid registration of medical images is important for a number of applications such as the creation of population averages, atlas-based segmentation, or geometric correction of functional magnetic resonance imaging (fMRI) images to name a few. In recent years, a number of methods have been proposed to solve this problem, one class of which involves maximizing a mutual information (MI)-based objective function over a regular grid of splines. This approach has produced good results but its computational complexity is proportional to the compliance of the transformation required to register the smallest structures in the image. Here, we propose a method that permits the spatial adaptation of the transformation's compliance. This spatial adaptation allows us to reduce the number of degrees of freedom in the overall transformation, thus speeding up the process and improving its convergence properties. To develop this method, we introduce several novelties: 1) we rely on radially symmetric basis functions rather than B-splines traditionally used to model the deformation field; 2) we propose a metric to identify regions that are poorly registered and over which the transformation needs to be improved; 3) we partition the global registration problem into several smaller ones; and 4) we introduce a new constraint scheme that allows us to produce transformations that are topologically correct. We compare the approach we propose to more traditional ones and show that our new algorithm compares favorably to those in current use.

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

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 post-contrast 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.