Brain atlas deformation in the presence of small and large space-occupying tumors.

Brain atlases contain a wealth of information that could be used in radiation therapy or neurosurgical planning. Until now, however, when large space-occupying tumors and lesions drastically alter the shape of brain structures and substructures, atlas-based methods have been of limited use. In this work, we present a new technique that permits a brain atlas to be warped onto image volumes in which large lesions are present. First we show that a method previously used for atlas-based segmentation of normal brains can also be used for brains with small lesions. We then present an extension of this technique for brains with large lesions. This involves several steps: a global registration to bring the two volumes into approximate correspondence; a local registration to warp the atlas onto the patient volume; the seeding of the warped atlas with a tumor model derived from patient data; and the deformation of the seeded atlas. Global registration is performed using a mutual information criterion. The method we have used for atlas warping is derived from optical flow principles. Preliminary results obtained on real patient images are presented. These results indicate that the proposed method can be used to automatically segment structures of interest in brains with gross deformation. Potential areas of application for this method include automatic labeling of critical structures for radiation therapy and presurgical planning.

Effect of geometrical distortion correction in MR on image registration accuracy.

In this article we investigate the effect of geometrical distortion correction in MR images on the accuracy of the registration of X-ray CT and MR head images for both a fiducial marker (extrinsic point) method and a surface-matching technique. We use CT and T2-weighted MR image volumes acquired from seven patients who underwent craniotomies in a stereotactic neurosurgical clinical trial. Each patient had four external markers attached to transcutaneous posts screwed into the outer table of the skull. The MR images are corrected for static field inhomogeneity by using an image rectification technique and corrected for scale distortion (gradient magnitude uncertainty) by using an attached stereotactic frame as an object of known shape and size. We define target registration error (TRE) as the distance between corresponding marker positions after registration and transformation. The accuracy of the fiducial marker method is determined by using each combination of three markers to estimate the transformation and the remaining marker to calculate registration error. Surface-based registration is accomplished by fitting MR contours corresponding to the CSF-dura interface to CT contours derived from the inner surface of the skull. The mean point-based TRE using three noncollinear fiducials improved 34%-from 1.15 to 0.76 mm-after correcting for both static field inhomogeneity and scale distortion. The mean surface-based TRE improved 46%-from 2.20 to 1.19 mm. Correction of geometrical distortion in MR images can significantly improve the accuracy of point-based and surface-based registration of CT and MR head images. Distortion correction can be important in clinical situations such as stereotactic and functional neurosurgery where 1 to 2 mm accuracy is required.

A universal method for geometric correction of magnetic resonance images for stereotactic neurosurgery.

Accurate stereotactic navigation depends strongly upon the spatial fidelity of the image used for registration. Clinically significant levels of geometric distortion are present in standard MR images, limiting their utility. A technique for correction of all geometric distortions in spine echo MR images was assessed in a prospective clinical trial of 19 stereotactic craniotomies. The Euclidean error in target registration between CT and MR was significantly reduced, from 3.833 +/- 0.992 to 1.986 +/- 0.605 mm. The results of this clinical trial support the incorporation of this MR image rectification protocol into standard clinical practice.

Retrospective registration of PET and MR brain images: an algorithm and its stereotactic validation.

OBJECTIVE: We present a validation study of an algorithm for retrospective registration of PET and MR brain images. MATERIALS AND METHODS: This algorithm involves two steps. In the first step, the two volumes are reformatted by aligning their interhemispheric fissure planes (midsagittal plane). In the second step, the corresponding planes parallel to the midsagittal plane are further aligned in the reformatted volumes to produce a 3D rigid body registration of the two original volumes. It is an efficient algorithm because both steps are performed in 2D spaces, and in each step only a small number of landmarks are required. A user-friendly system has been implemented to facilitate easy and fast processing of registration and reformatting of image volumes. The accuracy of this algorithm is validated using clinical scans of neurosurgical patients with a stereotaxic frame attached to their skull. The frame-based stereotaxic system provides an effective method for transforming image coordinates from different image volumes into a common coordinate system. This common coordinate system is used for assessing the spatial correspondence of each pixel in the registered image volumes. Validation using the stereotaxic image volumes enables objective estimation of retrospective registration accuracy. RESULTS: Analysis of 11 MR/PET image pairs indicates that our registration method not only is efficient but also provides adequate accuracy for most clinical evaluation of PET studies. CONCLUSION: We have implemented and validated an efficient algorithm for retrospective registration of PET and MR brain images.