John A. Little

A Comparison of Simularity Measures for use in 2D-3D Medical Image Registration

A comparison of six similarity measures for use in intensity based 2D-3D image registration is presented. The accuracy of the similarity measures are compared to a “gold-standard” registration which has been accurately calculated using fiducial markers. The similarity measures are used to register a CT scan to a fluoroscopy image of a spine phantom. The registration is carried out within a region of interest in the fluoroscopy image which is user defined to contain a single vertebra. Many of the problems involved in this type of registration are caused by features which were not modelled by a phantom image alone. More realistic “gold standard” data sets were simulated using the phantom image with clinical image features overlaid. Results show that the introduction of soft tissue structures and interventional instruments into the phantom image can have a large effect on the performance of some similarity measures previously applied to 2D-3D image registration. Two measures were able to register accurately and robustly even when soft tissue structures and interventional instruments were present as differences between the images. These measures are called pattern intensity and gradient difference.

Deformation for Image Guided Interventions Using a Three Component Tissue Model

In image guided neurosurgery it is necessary to align preoperative image data with the patient. The rigid body approximation is usually applied, but is often not valid due to tissue deformation. Most non-rigid registration algorithms, such as those used for atlas matching, provide a smooth deformation, which does not model the characteristics of different tissues accurately since, for example, bone will appear to deform. We suggest that a physically based model of tissue could provide a powerful tool for tracking tissue movement. Since the algorithm must ultimately run in real time, we have developed a simplified model of tissue deformation based on a three component system. Regions are labelled as either rigid, deformable or fluid. A novel strategy to avoid folding in the transformation is described. Our model was applied to MRI and CT data from a neurosurgery patient with epilepsy. The test data is limited and the current implementation is in 2D, but initial results are promising.

Medical image registration incorporating deformations

Multiple sources of 3D medical image data can be used to construct detailed patient representations. Typically registration is achieved assuming the validity of rigid body transformation. In many applications, and in particular when updating representations used for guidance during surgery and therapeutic interventions, this assumption is inappropriate. In this paper we describe a general method for 3D deformation, show how registration can incorporate a composite of rigid body and deformation components and illustrate this methodology on 3 example sets of images. The first is a repeated 3D MR scan of the abdomen of a volunteer who purposely changed position between scans; the second is an MR and CT scan of the head and neck, in which the patient was in a different position for the two scans; and the third is a set of MR and CT images of the head taken before and after epilepsy surgery. Non rigid deformation and composite warping showed significant improvement in registration accuracy in each case.

Automated Multimodality Registration Using the Full Affine Transformation: Application to MR and CT Guided Skull Base Surgery

We have used a twelve degrees of freedom global affine registration technique incorporating a multiresolution optimisation of mutual information to register MR and CT images with uncertain voxel dimensions and CT gantry tilt. Visual assessment indicates improved accuracy, without loss of robustness compared to rigid body registration.

Deformations incorporating rigid structures [medical imaging]

Medical image registration can provide useful clinical information by relating images of the same patient acquired from different modalities, or from serial studies with a single modality. Current algorithms invariably assume that the objects in the images can be treated as a rigid body. In practice, some parts of a patient, usually bony structures, may move as rigid bodies while others may deform. To address this, the authors have developed a new technique that allows identified objects in the image to move as rigid bodies, while the remainder smoothly deforms. Euclidean distance transforms calculated from the rigid objects are used to weight a linear combination of pre-defined linear transformations, one for each rigid body in the image, and also to form a modified radial basis function. This ensures that the non-linear deformation tends to zero as one moves towards the rigid body boundary. The resulting deformation technique is valid in any dimension, subject to the choice of the basis function. The authors demonstrate this technique in two dimensions on a pattern of rigid square structures to simulate the vertebral bodies of the spine, and on sagittal magnetic resonance images collected from a volunteer.

The registration of multiple medical images acquired from a single subject: why, how, what next?

This paper reviews some of the recent techniques which have been used to register multiple images of the same patient. Image registration is a problem which has been receiving significant attention from the medical image processing community in recent years. A successful image registration can aid in patient diagnosis, treatment assessment, image guided interventions, surgery planning and surgery. At present the majority of work has focused on rigid body transformations of images. We shall discuss some of the approaches used and outline a key automatic method in detail. In order to allow image registration of parts of the body which do not remain rigid, either due to patient movement or a change in pathology, nonlinear deformation techniques are being developed. We shall talk of the history of these methods before explaining deformations using landmarks and a recent extension to allow the definition of rigid structures in such warps in more detail. Validation of these methods is of great importance and we shall discuss work which has already been carried out on this topic for rigid body registrations as well as ideas for the validation of deformation algorithms.

Edgels and Tangent Planes in Image Warping

In this paper we first describe generally the uses of thin-plate splines for image deformation. We then go on to demonstrate why we feel that incorporating edgel information into a warp has great potential. An extension to the edgel method of Bookstein and Green (1993a,b) is outlined for use in three dimensions. Finally we give an example of the results of using this formulation for three-dimensional magnetic resonance images of the human head.

Anatomical landmark image registration: validation and comparison

This paper describes a validation approach for (interactive) anatomical landmark registration of CT and MR images. Eleven MR-CT image pairs were used, four of which had been scanned with a Leksell stereotactic frame present. Four observers each selected twelve pairs of anatomical landmarks per image pair. The images were then registered using a least squares minimization technique, only taking six transformation parameters (translation and rotation) into account. Each observer processed all twelve image pairs five times. Secondly we describe an algorithm to automatically detect a Leksell stereotactic frame from CT and MR images, scanned according to Leksell protocol. Results have been assessed by examining observer variations in the six parameters. Observer performance is differentiated by comparing median distances (and mean deviations) of identical points in different registrations. Comparison of all results show that intra- and interobserver variations are of the same magnitude as the difference between the automatic frame registration and the average observer registration.

Deforming a preoperative volume to better represent the intraoperative scene

Soft-tissue deformation can be a problem if a pre-operative modality is used to help guide a surgical or an interventional procedure. We present a method which can warp a pre-operative CT image to represent the intra-operative scene shown by an interventional fluoroscopy image. The method is a novel combination of a 2D-3D image registration algorithm and a deformation algorithm which allows rigid bodies to be incorporated into a non-linear deformation based on a radial basis function. The 2D-3D registration algorithm is used to obtain information on relative vertebral movements between pre-operative and intra-operative images. The deformation algorithm uses this information to warp the pre-operative image to represent the intra-operative scene more accurately. Images from an aortic stenting procedure were used. The observed deformation was 5 degree flexion and 5 mm lengthening of the lumbar spine. The vertebral positions in the warped CT volume represent the intra-operative scene more accurately than in the pre-operative CT volume. Although we had no gold- standard with which to assess the registration accuracy of soft-tissue structures, the position of soft-tissue structures within the warped CT volume appeared visually realistic.