Dlg Hill

Incorporating connected region labelling into automated image registration using mutual information

The information theoretic measure of mutual information has been successfully applied to multi-modality medical image registration for several applications. There remain however; modality combinations for which mutual information derived from the occurrence of image intensities alone does not provide a distinct optimum at true registration. The authors propose an extension of the technique through the use of an additional information channel supplying region labelling information. These labels which can specify simple regional connectivity or express higher level anatomical knowledge, can be derived from the images being registered. The authors show how the mutual information measure can be extended to include an additional channel of region labelling, and demonstrate the effectiveness of this technique for the registration of MR and PET images of the pelvis.

A System for Microscope-Assisted Guided Interventions

We present a system for surgical navigation using stereo overlays in the operating microscope aligned to the operative scene. This augmented reality system provides 3D information about nearby structures and offers a significant advancement over pointer-based guidance, which provides only the location of one point and requires the surgeon to look away from the operative scene. With a previous version of this system, we demonstrated feasibility, but it became clear that to achieve convincing guidance through the magnified microscope view, a very high alignment accuracy was required. We have made progress with several aspects of the system, including automated calibration, error simulation, bone-implanted fiducials and a dental attachment for tracking. We have performed experiments to establish the visual display parameters required to perceive overlaid structures beneath the operative surface. Easy perception of real and virtual structures with the correct transparency has been demonstrated in a laboratory and through the microscope. The result is a system with a predicted accuracy of 0.9 mm and phantom errors of 0.5 mm. In clinical practice errors are 0.5–1.5 mm, rising to 2–4 mm when brain deformation occurs.

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.

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.