Abstract

Abstract

Abstract. The registration of multimodal medical images is an important tool in surgical applications, since different scan modalities highlight complementary anatomical structures. We present a method of computing the best rigid registration of pairs of medical images of the same patient. The method uses prior information on the expected joint intensity distribution of the images when correctly aligned, given a priori registered training images. We discuss two methods of modeling the joint intensity distribution of the training data, mixture of Gaussians and Parzen windowing. The fitted Gaussians roughly correspond to various anatomical structures apparent in the images and provide a coarse anatomical segmentation of a registered image pair. Given a novel set of unregistered images, the algorithm computes the best registration by maximizing the log likelihood of the two images, given the transformation and the prior joint intensity model. Results aligning SPGR and dual-echo MR scans demonstrate that this algorithm is a fast registration method with a large region of convergence and sub-voxel registration accuracy.

No abstract

We describe an image-guided neurosurgery system which we have successfully used on 70 cases in the operating room. The system is designed to achieve high positional accuracy with a simple and efficient interface that interferes little with the operating room's usual procedures, but is general enough to use on a wide range of cases. It uses data from a laser scanner or a trackable probe to register segmented MR imagery to the patient's position in the operating room, and an optical tracking system to track head motion and localize medical instruments. Output visualizations for the surgeon consist of an "enhanced reality display," showing location of hidden internal structures, and an instrument tracking display, showing the location of instruments in the context of the MR imagery. Initial assessment of the system in the operating room indicates a high degree of robustness and accuracy.

While the role and utility of Magnetic Resonance Images as a diagnostic tool is well established in current clinical practice, there are a number of emerging medical arenas in which MRI can play an equally important role. In this article, we consider the problem of image-guided surgery, and provide an overview of a series of techniques that we h a v e recently developed in order to automatically utilize MRI-based anatomical reconstructions for surgical guidance and navigation.

A method is presented for segmentation of anatomical structures that incorporatesprior information about the intensity and curvature profile of the structuref" a training set of images and boundaries. Specijkally, we model the intensity distribution as a function of signed distance from the object boundary, instead of modeling only the intensity of the object as a whole. A curvature profile acts ns a boundary regularization term spec@ to the shape being extracted, ns opposed to simply penalizing high curvature. Using fhe prior model, the segmentah'on process estimates a man'mum a posteriori higher dimensional surface whose zem level set converges on the boundary of the object to be segmented. Segmentation results are demonstrated on synthetic data and magnetic resonance imagery.

We have used MRI-based three-dimensional (3D) reconstruction and a realtime, frameless, stereotactic navigation device to facilitate the removal of seizure foci in children suffering from intractable epilepsy. Using this system, the location of subdural grid and strip electrodes is recorded on the 3D model to facilitate focus localization and resection. Ten operations were performed, including 2 girls and 8 boys ranging in age from 3 to 17, during which 3D reconstruction and surgical instrument tracking navigation was used. In all the cases, the patients tolerated the procedure well and showed no postoperative neurological deficits. We believe this to be a valuable tool for a complete and safe resection of seizure foci, thereby reducing the incidence of postoperative neurological deficits and significantly improving the overall quality of life of the patients.