M.Y. Wang

Retrospective intermodality registration techniques for images of the head: surface-based versus volume-based

A blinded evaluation of two groups of retrospective image registration techniques was performed using as a gold standard a prospective marker-based registration method, and we compared the performance of one group with the other. By grouping the techniques as volume-based or surface-based, we could make some interesting conclusions. In order to ensure blindness, all retrospective registrations were performed by participants who had no knowledge of the gold-standard results until after their results had been submitted. Image volumes of three modalities (X-ray CT, MRI and PET) were obtained from patients undergoing neurosurgery on whom bone-implanted fiducial markers were mounted. These volumes had all traces of the markers removed and were provided via the Internet to outside collaborators, who then performed retrospective registrations on the volumes, calculating transformations from CT to MRI and/or from PET to MRI. The accuracy of each registration was then evaluated. The accuracy is measured at multiple volumes of interest. The volume-based techniques in this study tended to give substantially more accurate and reliable results than the surface-based ones for the CT-to-MRI registration tasks, and slightly more accurate results for the PET-to-MRI tasks. Analysis of these results revealed that the rotational component of error was more pronounced for the surface-based group. It was also apparent that all of the registration techniques we examined have the potential to produce satisfactory results much of the time, but that visual inspection is necessary to guard against large errors.

Registration of head volume images using implantable fiducial markers

In this paper, we describe an extrinsic point-based, interactive image-guided neurosurgical system designed at Vanderbilt University as part of a collaborative effort among the departments of neurological surgery, computer science, and biomedical engineering. Multimodal image-to- image and image-to-physical registration is accomplished using implantable markers. Physical space tracking is accomplished with optical triangulation. We investigate the theoretical accuracy of point-based registration using numerical simulations, the experimental accuracy of our system using data obtained with a phantom, and the clinical accuracy of our system using data acquired in a prospective clinical trial by six neurosurgeons at four medical centers from 158 patients undergoing craniotomies to resect cerebral lesions. We can determine the position of our markers with an error of approximately 0.4 mm in x-ray computed tomography (CT) and magnetic resonance (MR) images and 0.3 mm in physical space. The theoretical registration error using four such markers distributed around the head in a configuration that is clinically practical is approximately 0.5 - 0.6 mm. The mean CT-physical registration error for the phantom experiments is 0.5 mm and for the clinical data obtained with rigid head fixation during scanning is 0.7 mm. The mean CT-MR registration error for the clinical data obtained without rigid head fixation during scanning is 1.4 mm, which is the highest mean error that we observed. These theoretical and experimental findings indicate that this system is an accurate navigational aid that can provide real-time feedback to the surgeon about anatomical structures encountered in the surgical field.

Estimation of intraoperative brain surface movement

It is becoming increasingly common for surgical navigation systems to be used intraoperatively to enable the surgeon to ascertain his or her position within the patient with respect to features in registered preoperative images. These systems assume that the skull and its contents behave as a rigid body between imaging and surgery, and during surgery. We have used the ACUSTAR I surgical navigation system to measure shifts in the brain surface relative to the skull between imaging and surgery on five patients. The preoperative images are registered to the coordinates of the surgical localizer using four fiducial markers screwed into the outer table of the skull. The brain surface is delineated from preoperative MR images, and the distance between this surface and brain surface points recorded intraoperatively is calculated. The median shift of points on the brain surface ranged from 0.3 mm to 7.4 mm. In all cases, the direction of this shift corresponds to a “sinking” of the brain intraoperatively compared to its preoperative position. We consider possible changes in CNS volume that might account for these shifts.

Design of fiducials for accurate registration of CT and MR volume images

There is an increasing demand for accurate registration of CT and MR images for diagnosis and for surgical planning. One useful technique registers images using externally attached fiducial markers. Previous studies have shown that point-based registration accuracy depends on fiducial localization accuracy, that is, the accuracy with which the position of a marker centroid can be estimated. This paper investigates the dependence of localization accuracy on marker size. The investigation is based on computer simulations. Three shapes are evaluated-- a sphere, a cylinder, and a cube, each with similar dimensions. The results indicate that (1) the accuracy depends strongly on the ratio of marker size to voxel size and (2) the dependence is almost identical among the three shapes tested. The simulation results are shown to agree with results from clinical images.

Retrospective intermodality registration techniques: surface-based versus volume-based

The primary objective of this study is to perform a blinded evaluation of two groups of retrospective image registration techniques using as a gold standard a prospective, marker-based registration method, and to compare the performance of one group with the other. In order to ensure blindedness, all retrospective registrations were performed by participants who had no knowledge of the gold-standard results until after their results had been submitted. Image volumes of three modalities—X-ray Computed Tomography (CT), Magnetic Resonance (MR), and Positron Emission Tomography (PET)—were obtained from patients undergoing neurosurgery at Vanderbilt University Medical Center on whom bone-implanted fiducial markers were mounted. These volumes had all traces of the markers removed and were provided via the Internet to project collaborators outside Vanderbilt, who then performed retrospective registrations on the volumes, calculating transformations from CT to MR and/or from PET to MR. These investigators communicated their transformations again via the Internet to Vanderbilt, where the accuracy of each registration was evaluated. In this evaluation, the accuracy is measured at multiple “volumes of interest” (VOIs). Our results indicate that the volume-based techniques in this study tended to give substantially more accurate and reliable results than the surface-based ones for the CT-to-MR registration tasks and slightly more accurate results for the PET-to-MR tasks. It was also apparent that all of the registration techniques we examined have the potential to produce satisfactory results much of the time but that visual inspection is necessary to guard against large errors.

Correction of geometrical distortion in MR image registration

Registration techniques quantitatively relate the information in one image to information in another image or physical space [5] by determining a one-to-one mapping between the points in each space. Several methods have been used to register medical images [8]. We recently examined the registration of multimodal volume head images using extrinsic markers [7]. We decomposed the problem into the three separate problems of finding the positions of a t least three non-collinear markers in the two images, matching corresponding markers in the two image spaces, and estimating the translation and rotation parameters of the rigid body transformation that maps one space into the other. Accurate three-dimensional image registration strongly depends on the quality of the image volumes. A special concern arises with MR images because of geometrical distortions caused by static field inhomogeneity arising from imperfections in the magnet system or from magnetization of the object being imaged [6]. DeSoto et al. have demonstrated that a linear correction (affine shear matrix) which is largely independent of time and pulse sequence can minimize distortions due to machine imperfections [3]. This finding is consistent with the results of previous unpublished phantom studies at Vanderbilt which suggest that there is frequently scale distortion (typically 1-2%) presumably due to error in the gradient strength magnitudes. The ultimate distortion correction probably involves using a combination of techniques that correct for static field inhomogeneity and gradient magnitude uncertainty.

A knowledge-based technique for localizing externally attached markers in MR and CT volume images of the head

A semi-automatic technique is presented for finding the centroid of a cylindrical fiducial marker attached to the head in CT and MR volume images. Clinical tests on scans with 4 mm slice thickness of patients with four markers attached externally to the head demonstrate fiducial registration accuracies of better than 0.6 mm. I . I N T R O D U C T I O N The registration of Magnetic Resonance (MR) images of the head with Computed Tomographic (CT) images is of growing importance for diagnosis and for surgical planning. The registration accuracy is dependent on the “localization” accuracy, which is the accuracy with which the position of the centroid of a marker can be determined, and on the geometrical fidelity of the MR and CT scans. It is the purpose of this work to demonstrate that extremely high accuracy can be achieved through the use of rigidly attached fiducial markers that are visible in both MR and CT when geometrical distortion is compensated for in the MR images and a simple, knowledge-based fiducial localization algorithm is applied. We report here only on the localization algorithm Distortion correction is dealt with in a companion paper 111. We include the results of tests with four markers on each of six patients, using a total of forty-two scans.

Partial Volume Effect on Marker Localization in Medical Density Images

Registration of medical images to each other and to physical space for the purposes of surgical planning and surgical navigation can be accomplished using externally attached fiducial markers. The accuracy of fiducial localization, that is, the accuracy of estimating the position of the markers centroid, is extremely important because marker- based registration accuracy is proportional to localization accuracy. The traditional method of calculating the marker centroid using intensity weighting contains a serious logic flaw. This paper introduces a novel and efficient method for correcting this flaw. Theoretical analysis, computer simulation, and analysis of clinical images demonstrate the importance of this correction.