Andy D. Castellano-Smith

A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations

This work presents a framework for non-rigid registration which extends and generalizes a previously developed technique by Rueckert et al. [1]. We combine multi-resolution optimization with free-form deformations (FFDs) based on multi-level B-splines to simulate a non-uniform control point distribution. We have applied this to a number of different medical registration tasks to demonstrate its wide applicability, including interventional MRI brain tissue deformation compensation, breathing motion compensation in liver MRI, intra-modality inter-modality registration of pre-operative brain MRI to CT electrode implant data, and inter-subject registration of brain MRI. Our results demonstrate that the new algorithm can successfully register images with an improved performance, while achieving a significant reduction in run-time.

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.

Measurement and analysis of brain deformation during neurosurgery

Recent studies have shown that the surface of the brain is deformed by up to 20 mm after the skull is opened during neurosurgery, which could lead to substantial error in commercial image-guided surgery systems. We quantitatively analyze the intraoperative brain deformation of 24 subjects to investigate whether simple rules can describe or predict the deformation. Interventional magnetic resonance images acquired at the start and end of the procedure are registered nonrigidly to obtain deformation values throughout the brain. Deformation patterns are investigated quantitatively with respect to the location an magnitude of deformation, and to the distribution and principal direction of the displacements. We also measure the volume change of the lateral ventricles by manual segmentation. Our study indicates that brain shift occurs predominantly in the hemisphere ipsi-lateral to the craniotomy, and that there is more brain deformation during resection procedures than during biopsy or functional procedures. However, the brain deformation patterns are extremely complex in this group of subjects. This paper quantitatively demonstrates that brain deformation occurs not only at the surface, but also in deeper brain structure, and that the principal direction of displacement does not always correspond with the direction of gravity. Therefore, simple computational algorithms that utilize limited intraoperative information (e.g., brain surface shift) will not always accurately predict brain deformation at the lesion.

Using Points and Surfaces to Improve Voxel-Based Non-rigid Registration

Voxel-based non-rigid registration algorithms have been successfully applied to a wide range of image types. However, in some cases the registration of quite different images, e.g. pre- and post-resection images, can fail because of a lack of voxel intensity correspondences. One solution is to introduce feature information into the voxel-based registration algorithms in order to incorporate higher level information about the expected deformation.We illustrate using one voxel-based registration algorithm that the incorporation of features yields considerable improvement of the registration results in such cases.

Constructing Patient Specific Models for Correcting Intraoperative Brain Deformation

In this work we present a Mesh Warping technique for the construction of patient-specific Finite Element Method models from patient MRI images, and demonstrate how simulated surgical loading can be applied to these models. We compare the results of this simulation with observed deformation during surgery, and show that our model matches well with the observed degree of deformation.

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.

Validation of Volume-Preserving Non-rigid Registration: Application to Contrast-Enhanced MR-Mammography

In this paper, we present a validation study for volume preserving non-rigid registration of 3D contrast-enhanced magnetic resonance mammograms. This study allows for the first time to assess the effectiveness of a volume preserving constraint to improve registration accuracy in this context. The validation is based on the simulation of physically plausible breast deformations with biomechanical breast models (BBMs) employing finite element methods. We constructed BBMs for four patients with four different deformation scenarios each. These deformations were applied to the post-contrast image to simulate patient motion occurring between pre- and post-contrast image acquisition. The original pre-contrast images were registered to the corresponding BBM-deformed post-contrast images. We assessed the accuracy of two optimisation schemes of a non-rigid registration algorithm. The first solely aims to improve the similarity of the images while the second includes the minimisation of volume changes as another objective. We observed reductions in residual registration error at every resolution when constraining the registration to preserve volume. Within the contrast enhancing lesion, the best results were obtained with a control point spacing of 20mm, resulting in target registration errors below 0.5mm on average. This study forms an important milestone in making the non-rigid registration framework applicable for clinical routine use.

Pitfalls in Comparing Functional Magnetic Resonance Imaging and Invasive Electrophysiology Recordings

Several authors have recently compared the results of fMRI studies on neurosurgery patients with invasive electrophysiology. These studies aim to validate MRI against an accepted gold standard, and ascertain whether fMRI could replace invasive electrophysiology in neurosurgical patients. We have identified and quantified two characteristics of these data that make such comparisons problematic. Firstly, the epilepsy surgery patients (n=8) studied move significantly more during fMRI experiments than normal volunteers (n=6) performing the same task. This motion has a particularly large out-of-plane component, and is significantly more correlated with the stimulus than for the normal volunteers. This motion is especially large when performing a task on the side affected by the lesion. This additional motion is hard to correct and substantially degrades the quality of the resulting fMRI images, making it a much less reliable technique on these surgical patients than on other subjects. Secondly, we have found that, following electrode implantation, the brain surface can shift by more than 10 mm relative to the skull compared to its preoperative location, substantially degrading the accuracy of the comparison of electrophysiology measurements made on the deformed brain and fMRI studies carried out preoperatively. Taken together, these findings suggest that studies of this sort are currently of limited use for validating fMRI, and further image analysis research is necessary to solve the problems caused by subject motion and brain deformation.