There is a need for frameless guidance systems to help surgeons plan the exact location for incisions, to define the margins of tumors, and to precisely identify locations of neighboring critical structures. The authors have developed an automatic technique for registering clinical data, such as segmented magnetic resonance imaging (MRI) or computed tomography (CT) reconstructions, with any view of the patient on the operating table. The authors demonstrate on the specific example of neurosurgery. The method enables a visual mix of live video of the patient and the segmented three-dimensional (3-D) MRI or CT model. This supports enhanced reality techniques for planning and guiding neurosurgical procedures and allows us to interactively view extracranial or intracranial structures nonintrusively. Extensions of the method include image guided biopsies, focused therapeutic procedures, and clinical studies involving change detection over time sequences of images.

The segmentation of MRI scans of patients with white matter lesions (WML) is difficult because the MRI characteristics of WML are similar to those of gray matter. Intensity‐based statistical classification techniques misclassify some WML as gray matter and some gray matter as WML. We developed a fast elastic matching algorithm that warps a reference data set containing information about the location of the gray matter into the approximate shape of the patient's brain. The region of white matter was segmented after segmenting the cortex and deep gray matter structures. The cortex was identified by using a three‐dimensional, region‐growing algorithm that was constrained by anatomical, intensity gradient, and tissue class parameters. White matter and WML were then segmented without interference from gray matter by using a two‐class minimum‐distance classifier. Analysis of double‐echo spin‐echo MRI scans of 16 patients with clinically determined multiple sclerosis (MS) was carried out. The segmentation of the cortex and deep gray matter structures provided anatomical context. This was found to improve the segmentation of MS lesions by allowing correct classification of the white matter region despite the overlapping tissue class distributions of gray matter and MS lesion. J Image Guid Surg 1:326–338 (1995). © 1996 Wiley‐Liss, Inc.

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A highly reproducible automated procedure for quantitative analysis of serial brain magnetic resonance (MR) images was developed for use in patients with multiple sclerosis (MS). The intracranial cavity (ICC) was identified on standard dual-echo spin-echo brain MR images using a supervised automated procedure. MR images obtained from one MS patient at 24 time points in the course of a 1-year follow-up were aligned with the images of one of the time points. Next, the contents of the ICC in each MR exam were segmented into four tissues, using a self-adaptive statistical algorithm. Misclassifications due to partial voluming were corrected using a combination of morphologic operators and connectivity criteria. Index terms: sclerosis, multiple; magnetic resonance (MR), image processing; magnetic resonance (MR), volume measurement; magnetic resonance (MR), surface rendition; brain, volume; brain, white matter MULTIPLE SCLEROSIS (MS) is one of the most challenging diseases for image segmentation of brain structures and therefore provides a good model for the quantitative evaluation of brain disease and its progression. We have developed an image processing system that automatically identifies and quantifies individual MS lesions over time. This paper describes a software system that was developed for the analysis of serial MRI and gives results from the analysis of one MS patient's 24 MRI studies over a 1-year period. By integrating automatic registration and segmentation with partial voluming artifact correction, it became possible to estimate lesion volumes automatically from proton density-weighted (PDw) and T2-weighted (T2w) MR images. Serial brain MRI is important in the study of the natural course of MS and in the evaluation of potential therapeutic agents (1-7). Besides neuroradiologic interpretation, several image processing methods have been applied to quantify lesion burden from PDw and T2w spin-echo images (8,9). Manual outlining of lesions on MR was used initially, in spite of its limited reproducibility and high labor intensity (10-16). The first semi-automated methods to identify and measure the volumes were based on signal intensity properties or image gradients (edges) but still needed significant manual correction, entailing significant loss in measurement reproducibility (15-26).Our group proposed such approaches for the measurement of white matter lesions in MS (22,(27)(28)(29)(30) and started looking for solutions leading to fully automated segmentation algorithms. Edge-preserving noise reduction was introduced in an attempt to enhance the contrast-to-noise ratio between normal and abnormal white matter (31), image registration was proposed to identify individual lesions automatically (28,32), and the first three-dimensional (3D) movies of lesion evolution clearly demonstrated that lesions would swell and regress in a matter of weeks (33). The combination of these tools contributed largely to the assessment of large, well-defined structures (34-37) but lacked the consistency and sensitivity to monito...

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.

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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.

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