Christine Tanner

Validation of nonrigid image registration using finite-element methods: application to breast MR images

Presents a novel method for validation of nonrigid medical image registration. This method is based on the simulation of physically plausible, biomechanical tissue deformations using finite-element methods. Applying a range of displacements to finite-element models of different patient anatomies generates model solutions which simulate gold standard deformations. From these solutions, deformed images are generated with a range of deformations typical of those likely to occur in vivo. The registration accuracy with respect to the finite-element simulations is quantified by co-registering the deformed images with the original images and comparing the recovered voxel displacements with the biomechanically simulated ones. The functionality of the validation method is demonstrated for a previously described nonrigid image registration technique based on free-form deformations using B-splines and normalized mutual information as a voxel similarity measure, with an application to contrast-enhanced magnetic resonance mammography image pairs. The exemplar nonrigid registration technique is shown to be of subvoxel accuracy on average for this particular application. The validation method presented here is an important step toward more generic simulations of biomechanically plausible tissue deformations and quantification of tissue motion recovery using nonrigid image registration. It will provide a basis for improving and comparing different nonrigid registration techniques for a diversity of medical applications, such as intrasubject tissue deformation or motion correction in the brain, liver or heart.

Anisotropic multi-scale fluid registration: evaluation in magnetic resonance breast imaging

Registration using models of compressible viscous fluids has not found the general application of some other techniques (e.g., free-form-deformation (FFD)) despite its ability to model large diffeomorphic deformations. We report on a multi-resolution fluid registration algorithm which improves on previous work by (a) directly solving the Navier-Stokes equation at the resolution of the images, (b) accommodating image sampling anisotropy using semi-coarsening and implicit smoothing in a full multi-grid (FMG) solver and (c) exploiting the inherent multi-resolution nature of FMG to implement a multi-scale approach. Evaluation is on five magnetic resonance (MR) breast images subject to six biomechanical deformation fields over 11 multi-resolution schemes. Quantitative assessment is by tissue overlaps and target registration errors and by registering using the known correspondences rather than image features to validate the fluid model. Context is given by comparison with a validated FFD algorithm and by application to images of volunteers subjected to large applied deformation. The results show that fluid registration of 3D breast MR images to sub-voxel accuracy is possible in minutes on a 1.6 GHz Linux-based Athlon processor with coarse solutions obtainable in a few tens of seconds. Accuracy and computation time are comparable to FFD techniques validated for this application.

Factors influencing the accuracy of biomechanical breast models

Recently it has been suggested that finite element methods could be used to predict breast deformations in a number of applications, including comparison of multimodality images, validation of image registration and image guided interventions. Unfortunately knowledge of the mechanical properties of breast tissues is limited. This study evaluated the accuracy with which biomechanical breast models based on finite element methods can predict the displacements of tissue within the breast in the practical clinical situation where the boundaries of the organ might be known reasonably accurately but there is some uncertainty on the mechanical properties of the tissue. For two datasets, we investigate the influence of tissue elasticity values, Poissons ratios, boundary conditions, finite element solvers and mesh resolutions. Magnetic resonance images were acquired before and after compressing each volunteers breast by about 20%. Surface displacement boundary conditions were derived from a three-dimensional nonrigid image registration. Six linear and three nonlinear elastic material models with and without skin were tested. These were compared to hyperelastic models. The accuracy of the models was evaluated by assessing the ability of the model to predict the location of 12 corresponding anatomical landmarks. The accuracy was most sensitive to the Poissons ratio and the boundary condition. Best results were achieved for accurate boundary conditions, appropriate Poissons ratios and models where fibroglandular tissue was at most four times stiffer than fatty tissue. These configurations reduced the mean (maximum) distance of the landmarks from 6.6 mm (12.4 mm) to 2.1 mm (3.4 mm) averaged over all experiments.

Volume and Shape Preservation of Enhancing Lesions when Applying Non-rigid Registration to a Time Series of Contrast Enhancing MR Breast Images

In this paper we show first that a non-rigid registration algorithm used to register time-series MR images of the breast, can result in significant volume changes in the region of the enhanced lesion. Since this is physically implausible, given the short duration of the MR time series acquisition, the non-rigid registration algorithm was extended to allow the incorporation of rigid regions. In this way the registration is done in two stages. The enhanced lesions are first detected using the non-rigid registration algorithm in its original form. Secondly, the region of the enhanced lesion is set to be rigid and the new algorithm is applied to integrate this rigid region into the existing registration. By definition, volume and shape will be preserved in this rigid region. Preliminary results of applying this algorithm to 15 datasets are described.

Development of patient-specific biomechanical models for predicting large breast deformation

Physically realistic simulations for large breast deformation are of great interest for many medical applications such as cancer diagnosis, image registration, surgical planning and image-guided surgery. To support fast, large deformation simulations of breasts in clinical settings, we proposed a patient-specific biomechanical modelling framework for breasts, based on an open-source graphics processing unit-based, explicit, dynamic, nonlinear finite element (FE) solver. A semi-automatic segmentation method for tissue classification, integrated with a fully automated FE mesh generation approach, was implemented for quick patient-specific FE model generation. To solve the difficulty in determining material parameters of soft tissues in vivo for FE simulations, a novel method for breast modelling, with a simultaneous material model parameter optimization for soft tissues in vivo, was also proposed. The optimized deformation prediction was obtained through iteratively updating material model parameters to maximize the image similarity between the FE-predicted MR image and the experimentally acquired MR image of a breast. The proposed method was validated and tested by simulating and analysing breast deformation experiments under plate compression. Its prediction accuracy was evaluated by calculating landmark displacement errors. The results showed that both the heterogeneity and the anisotropy of soft tissues were essential in predicting large breast deformations under plate compression. As a generalized method, the proposed process can be used for fast deformation analyses of soft tissues in medical image analyses and surgical simulations.

Validation of Non-rigid Registration Using Finite Element Methods

We present a novel validation method for non-rigid registration using a simulation of deformations based on biomechanical modelling of tissue properties. This method is tested on a previously developed non-rigid registration method for dynamic contrast enhanced Magnetic Resonance (MR) mammography image pairs [1]. We have constructed finite element breast models and applied a range of displacements to them, with an emphasis on generating physically plausible deformations which may occur during normal patient scanning procedures. From the finite element method (FEM) solutions, we have generated a set of deformed contrast enhanced images against which we have registered the original dynamic image pairs. The registration results have been successfully validated at all breast tissue locations by comparing the recovered displacements with the biomechanical displacements. The validation method presented in this paper is an important tool to provide biomechanical gold standard deformations for registration error quantification, which may also form the basis to improve and compare different non-rigid registration techniques for a diversity of medical applications.

MR Navigated Breast Surgery: Method and Initial Clinical Experience

3D dynamic contrast enhanced magnetic resonance (MR) images may help to reduce the high re-excision rate associated with breast conserving surgery. However these images are acquired prone, whilst surgery is performed supine which results in a large deformation that limits their usefulness. We describe here a registration technique based on a biomechanical model to account for soft tissue deformation between prone MR imaging and surgery. The accuracies of the individual registration steps are assessed off-line. We then report our first clinical experience with an image-guided surgery system which incorporates these algorithms. The systems accuracy is assessed against tracked ultrasound images, and is determined to be around 5mm for this case.

A method for the comparison of biomechanical breast models

Biomechanical models of the breast are being developed for a wide range of applications including image alignment tasks to improve diagnosis and therapy monitoring, imaging related studies of the biomechanical properties of lesions, and image guided interventions. In this paper we present a method to evaluate the accuracy with which biomechanical breast models based on finite element methods (FEM) can predict the displacements of tissue within the breast. Our experimental data was obtained by compressing the breast of a volunteer in a controlled manner, and the acquisition of MR images of the breast before and after compression. Non-rigid registration of these two MR volumes together with interactive identification of corresponding landmarks provided an independent estimate of the displacements. In addition, the non-rigid registration provided estimates of the displacements of the surface points (skin points) of the breast. The accuracy of the FEM models was evaluated using all or a subset of these surface displacements as boundary conditions. The influence of pectoral muscle movement on the performance of the FEM models was also investigated. Our initial results indicate that the accurate setting of the boundary conditions is more important than the actual choice of elastic properties in these compression scenarios. With the complete boundary conditions, the displacements agreed to within 2.6 mm for all FEM models on average. Assuming no movement at the posterior or the medial side of the breast, the accuracy of the FEM models deteriorated to worse than 4.6 mm for all models.

A New Validation Method for X-ray Mammogram Registration Algorithms Using a Projection Model of Breast X-ray Compression

Establishing spatial correspondence between features visible in X-ray mammograms obtained at different times has great potential to aid assessment and quantitation of change in the breast indicative of malignancy. The literature contains numerous non- rigid registration algorithms developed for this purpose, but existing approaches are flawed by the assumption of inappropriate 2-D transformation models and quantitative estimation of registration accuracy is limited. In this paper, we describe a novel validation method which simulates plausible mammographic compressions of the breast using a magnetic resonance imaging (MRI) derived finite element model. By projecting the resulting known 3-D displacements into 2-D and generating pseudo-mammograms from these same compressed magnetic resonance (MR) volumes, we can generate convincing images with known 2-D displacements with which to validate a registration algorithm. We illustrate this approach by computing the accuracy for two conventional nonrigid 2-D registration algorithms applied to mammographic test images generated from three patient MR datasets. We show that the accuracy of these algorithms is close to the best achievable using a 2-D one-to-one correspondence model but that new algorithms incorporating more representative transformation models are required to achieve sufficiently accurate registrations for this application.

Quantitative evaluation of free-form deformation registration for dynamic contrast-enhanced MR mammography.

In this paper, we present an evaluation study of a set of registration strategies for the alignment of sequences of 3D dynamic contrast-enhanced magnetic resonance breast images. The accuracy of the optimal registration strategies was determined on unseen data. The evaluation is based on the simulation of physically plausible breast deformations using finite element methods and on contrast-enhanced image pairs without visually detectable motion artifacts. The configuration of the finite element model was chosen according to its ability to predict in vivo breast deformations for two volunteers. We computed transformations for ten patients with 12 simulated deformations each. These deformations were applied to the postcontrast image to model patient motion occurring between pre- and postcontrast image acquisition. The original precontrast images were registered to the corresponding deformed postcontrast images. The performance of several registration configurations (rigid, affine, B-spline based nonrigid, single-resolution, multi-resolution, and volume-preserving) was optimized for five of the ten patients. The images were most accurately aligned with volume-preserving single-resolution nonrigid registration employing 40 or 20 mm control point spacing. When tested on the remaining five patients the optimal configurations reduced the average mean registration error from 1.40 to 0.45 mm for the whole breast tissue and from 1.20 to 0.32 mm for the enhancing lesion. These results were obtained on average within 26 (81) min for 40 (20) mm control point spacing. The visual appearance of the difference images from 30 patients was significantly improved after 20 mm volume-preserving single-resolution nonrigid registration in comparison to no registration or rigid registration. No substantial volume changes within the region of the enhancing lesions were introduced by this nonrigid registration.