This paper describes a unified approach to the detection of point landmarks-whose neighborhoods convey discriminant information-including multidimensional scalar, vector, and higher-order tensor data. The method is based on the interpretation of generalized correlation matrices derived from the gradient of tensor functions, a probabilistic interpretation of point landmarks, and the application of tensor algebra. Results on both synthetic and real tensor data are presented.

The vasculature is of utmost importance in neurosurgery. Direct visualization of images acquired with current imaging modalities, however, cannot provide a spatial representation of small vessels. These vessels, and their branches which show considerable variations, are most important in planning and performing neurosurgical procedures. In planning they provide information on where the lesion draws its blood supply and where it drains. During surgery the vessels serve as landmarks and guidelines to the lesion. The more minute the information is, the more precise the navigation and localization of computer guided procedures. Beyond neurosurgery and neurological study, vascular information is also crucial in cardiovascular surgery, diagnosis, and research. This paper addresses the problem of automatic segmentation of complicated curvilinear structures in three-dimensional imagery, with the primary application of segmenting vasculature in magnetic resonance angiography (MRA) images. The method presented is based on recent curve and surface evolution work in the computer vision community which models the object boundary as a manifold that evolves iteratively to minimize an energy criterion. This energy criterion is based both on intensity values in the image and on local smoothness properties of the object boundary, which is the vessel wall in this application. In particular, the method handles curves evolving in 3D, in contrast with previous work that has dealt with curves in 2D and surfaces in 3D. Results are presented on cerebral and aortic MRA data as well as lung computed tomography (CT) data.

An automated brain tumor segmentation method was developed and validated against manual segmentation with three-dimensional magnetic resonance images in 20 patients with meningiomas and low-grade gliomas. The automated method (operator time, 5-10 minutes) allowed rapid identification of brain and tumor tissue with an accuracy and reproducibility comparable to those of manual segmentation (operator time, 3-5 hours), making automated segmentation practical for low-grade gliomas and meningiomas.

Intraoperative line scan diffusion imaging (LSDI) on a 0.5 Tesla interventional MRI was performed during neurosurgery in three patients. Diffusion trace images were obtained in acute ischemic cases. Scan time per slice was 46 seconds and 94 seconds, respectively, for diffusion tensor images. Diagnosis of acutely developed vascular occlusion was confirmed with follow-up scans. White matter tracts were displayed with the principal eigenvectors and provided guidance for the tumor surgery. In all cases, the diagnostic utility of LSDI was established. J. Magn. Reson. Imaging 2001;13:115-119.

Interventional MRI (IMRI) has entered into a new stage in which computer-based techniques play an increasing role in planning, monitoring, and controlling the procedures. The use of interactive imaging, navigational image guidance techniques, and image processing methods is demonstrated in various applications. The integration of intraoperative MRI guidance and computer-assisted surgery will greatly accelerate the clinical utility of image-guided therapy in general and interventional MRI in particular. J. Magn. Reson. Imaging 2001;13:69-77.