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Magnetic resonance imaging (MRI) is an important tool for analysis of deep brain grey matter structures. However, analysis of these structures is limited due to low intensity contrast typically found in whole brain imaging protocols. Herein, we propose a big data registration-enhancement (BDRE) technique to augment the contrast of deep brain structures using an efficient large-scale non-rigid registration strategy. Direct validation is problematic given a lack of ground truth data. Rather, we validate the usefulness and impact of BDRE for multi-atlas (MA) segmentation on two sets of structures of clinical interest: the thalamic nuclei and hippocampal subfields. The experimental design compares algorithms using T1-weighted 3 T MRI for both structures (and additional 7 T MRI for the thalamic nuclei) with an algorithm using BDRE. As baseline comparisons, a recent denoising (DN) technique and a super-resolution (SR) method are used to preprocess the original 3 T MRI. The performance of each MA segmentation is evaluated by the Dice similarity coefficient (DSC). BDRE significantly improves mean segmentation accuracy over all methods tested for both thalamic nuclei (3 T imaging: 9.1%; 7 T imaging: 15.6%; DN: 6.9%; SR: 16.2%) and hippocampal subfields (3 T T1 only: 8.7%; DN: 8.4%; SR: 8.6%). We also present DSC performance for each thalamic nucleus and hippocampal subfield and show that BDRE can help MA segmentation for individual thalamic nuclei and hippocampal subfields. This work will enable large-scale analysis of clinically relevant deep brain structures from commonly acquired T1 images., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Our objective was to prospectively evaluate implementation of a new cochlear implant (CI) mapping technique, image-guided cochlear implant programming (IGCIP), at a site distant to the site of development. IGCIP consists of identifying the geometric relationship between CI electrodes and the modiolus and deactivating electrodes that interfere with neighboring electrodes. IGCIP maps for 17 ears of 15 adult CI patients were developed at a central image-processing center, Vanderbilt, and implemented at a distant tertiary care center, House Ear Institute. Before IGCIP and again 4 weeks after, qualitative and quantitative measures were made. While there were no statistically significant groupwise differences detected between baseline and IGCIP qualitative or quantitative measures, 11 of the 17 (64.7%) elected to keep the IGCIP map. Computed tomography (CT) image quality appears to be crucial for successful IGCIP, with 100% of those with high-resolution CT scans keeping their maps compared to 53.8% without.

Localizing the anterior and posterior commissures (AC/PC) and the midsagittal plane (MSP) is crucial in stereotactic and functional neurosurgery, human brain mapping, and medical image processing. We present a learning-based method for automatic and efficient localization of these landmarks and the plane using regression forests. Given a point in an image, we first extract a set of multiscale long-range contextual features. We then build random forests models to learn a nonlinear relationship between these features and the probability of the point being a landmark or in the plane. Three-stage coarse-to-fine models are trained for the AC, PC, and MSP separately using downsampled by 4, downsampled by 2, and the original images. Localization is performed hierarchically, starting with a rough estimation that is progressively refined. We evaluate our method using a leave-one-out approach with 100 clinical T1-weighted images and compare it to state-of-the-art methods including an atlas-based approach with six nonrigid registration algorithms and a model-based approach for the AC and PC, and a global symmetry-based approach for the MSP. Our method results in an overall error of 0.55 ±0.30 mm for AC, 0.56 ±0.28 mm for PC, 1.08(°) ±0.66 in the plane's normal direction, and 1.22 ±0.73 voxels in average distance for MSP; it performs significantly better than four registration algorithms and the model-based method for AC and PC, and the global symmetry-based method for MSP. We also evaluate the sensitivity of our method to image quality and parameter values. We show that it is robust to asymmetry, noise, and rotation. Computation time is 25 s.

The cochlear implant (CI) has been labeled the most successful neural prosthesis. Despite this success, a significant number of CI recipients experience poor speech understanding, and, even among the best performers, restoration to normal auditory fidelity is rare. While significant research efforts have been devoted to improving stimulation strategies, few developments have led to significant hearing improvement over the past two decades. We have recently introduced image processing techniques that open a new direction for advancement in this field by making it possible, for the first time, to determine the position of implanted CI electrodes relative to the nerves they stimulate using computed tomography images. In this article, we present results of an image-guided, patient-customized approach to stimulation that utilizes the electrode position information our image processing techniques provide. This approach allows us to identify electrodes that cause overlapping stimulation patterns and to deactivate them from a patient's map. This individualized mapping strategy yields significant improvement in speech understanding in both quiet and noise as well as improved spectral resolution in the 68 adult CI recipients studied to date. Our results indicate that image guidance can improve hearing outcomes for many existing CI recipients without requiring additional surgery or the use of 'experimental' stimulation strategies, hardware or software., (© 2014 S. Karger AG, Basel.)

Identification of the anterior and posterior commissure is crucial in stereotactic and functional neurosurgery, human brain mapping, and medical image processing. We present a learning-based algorithm to automatically and rapidly localize these landmarks using random forests regression. Given a point in the image, we extract a set of multi-scale long-range textural features, and associate a probability for this point to be the landmark. We build random forests models to learn the relationship between the value of these features and the probability of a point to be a landmark point. Three-stage coarse-to-fine models are trained for AC and PC separately using down-sampled by 4, down-sampled by 2, and the original images. Testing is performed in a hierarchical approach to first obtain a rough estimation at the coarse level and then fine-tune the landmark position. We extensively evaluate our method in a leave-one-out fashion using a large dataset of 100 T1-weighted images. We also compare our method to the state-of-art AC/PC detection methods including an atlas-based approach with six well-established nonrigid registration algorithms and a publicly available implementation of a model-based approach. Our method results in an overall error of 0.84±0.41mm for AC, 0.83±0.36mm for PC and a maximum error of 2.04mm; it performs significantly better than the model-based AC/PC detection method we compare it to and better than three of the nonrigid registration methods. It is much faster than nonrigid registration methods.

Over the last 20 years, cochlear implants (CIs) have become what is arguably the most successful neural prosthesis to date. Despite this success, a significant number of CI recipients experience marginal hearing restoration, and, even among the best performers, restoration to normal fidelity is rare. In this paper, we present image processing techniques that can be used to detect, for the first time, the positions of implanted CI electrodes and the nerves they stimulate for individual CI users. These techniques permit development of new, customized CI stimulation strategies. We present one such strategy and show that it leads to significant hearing improvement in an experiment conducted with 11 CI recipients. These results indicate that image-guidance can be used to improve hearing outcomes for many existing CI recipients without requiring additional surgical procedures.

Image segmentation has become a vital and often rate-limiting step in modern radiotherapy treatment planning. In recent years, the pace and scope of algorithm development, and even introduction into the clinic, have far exceeded evaluative studies. In this work we build upon our previous evaluation of a registration driven segmentation algorithm in the context of 8 expert raters and 20 patients who underwent radiotherapy for large space-occupying tumours in the brain. In this work we tested four hypotheses concerning the impact of manual segmentation editing in a randomized single-blinded study. We tested these hypotheses on the normal structures of the brainstem, optic chiasm, eyes and optic nerves using the Dice similarity coefficient, volume, and signed Euclidean distance error to evaluate the impact of editing on inter-rater variance and accuracy. Accuracy analyses relied on two simulated ground truth estimation methods: simultaneous truth and performance level estimation and a novel implementation of probability maps. The experts were presented with automatic, their own, and their peers' segmentations from our previous study to edit. We found, independent of source, editing reduced inter-rater variance while maintaining or improving accuracy and improving efficiency with at least 60% reduction in contouring time. In areas where raters performed poorly contouring from scratch, editing of the automatic segmentations reduced the prevalence of total anatomical miss from approximately 16% to 8% of the total slices contained within the ground truth estimations. These findings suggest that contour editing could be useful for consensus building such as in developing delineation standards, and that both automated methods and even perhaps less sophisticated atlases could improve efficiency, inter-rater variance, and accuracy.

Segmenting the thyroid gland in head and neck CT images is of vital clinical significance in designing intensity-modulated radiation therapy (IMRT) treatment plans. In this work, we evaluate and compare several multiple-atlas-based methods to segment this structure. Using the most robust method, we generate automatic segmentations for the thyroid gland and study their clinical applicability. The various methods we evaluate range from selecting a single atlas based on one of three similarity measures, to combining the segmentation results obtained with several atlases and weighting their contribution using techniques including a simple majority vote rule, a technique called STAPLE that is widely used in the medical imaging literature, and the similarity between the atlas and the volume to be segmented. We show that the best results are obtained when several atlases are combined and their contributions are weighted with a measure of similarity between each atlas and the volume to be segmented. We also show that with our data set, STAPLE does not always lead to the best results. Automatic segmentations generated by the combination method using the correlation coefficient (CC) between the deformed atlas and the patient volume, which is the most accurate and robust method we evaluated, are presented to a physician as 2D contours and modified to meet clinical requirements. It is shown that about 40% of the contours of the left thyroid and about 42% of the right thyroid can be used directly. An additional 21% on the left and 24% on the right require only minimal modification. The amount and the location of the modifications are qualitatively and quantitatively assessed. We demonstrate that, although challenged by large inter-subject anatomical discrepancy, atlas-based segmentation of the thyroid gland in IMRT CT images is feasible by involving multiple atlases. The results show that a weighted combination of segmentations by atlases using the CC as the similarity measure slightly outperforms standard combination methods, e.g. the majority vote rule and STAPLE, as well as methods selecting a single most similar atlas. The results we have obtained suggest that using our contours as initial contours to be edited has clinical value.

In recent years, radiation therapy has become the preferred treatment for many types of head and neck tumors. To plan the procedure, vital structures, including the optic nerves and chiasm, must be identified using CT/MR imagery. In this work we present a novel method for automatically localizing the optic nerves and chiasm using a tubular structure localization algorithm in which a statistical model and image registration are used to incorporate a priori local intensity and shape information. The method results in mean Dice coefficients of 0.8 when compared to manual segmentations over ten test cases. This suggests that our method is more accurate than existing techniques developed for the segmentation of these structures., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Accurate anatomic co-registration is a prerequisite for identifying structural and functional changes in longitudinal studies of brain plasticity. Current MRI methods permit collection of brain images across multiple scales, ranging from whole brain at relatively low resolution (≥1 mm), to local brain areas at the level of cortical layers and columns (∼100 μm) in the same session, allowing detection of subtle structural changes on a similar spatial scale. To measure these changes reliably, high resolution structural and functional images of local brain regions must be registered accurately across imaging sessions. The present study describes a robust fully automated strategy for the registration of high resolution structural images of brain sub-volumes to lower resolution whole brain images collected within a session, and the registration of partially overlapping high resolution MRI sub-volumes ("slabs") across imaging sessions. In high field (9.4 T) reduced field-of-view high resolution structural imaging studies using a surface coil in an anesthetized non-human primate model, this fully automated coregistration pipeline was robust in the face of significant inhomogeneities in image intensity and tissue contrast arising from the spatially inhomogeneous transmit and receive properties of the surface coil, achieving a registration accuracy of 30±15 μm between sessions., (Copyright © 2011 Elsevier B.V. All rights reserved.)

The purpose of this work was to characterize expert variation in segmentation of intracranial structures pertinent to radiation therapy, and to assess a registration-driven atlas-based segmentation algorithm in that context. Eight experts were recruited to segment the brainstem, optic chiasm, optic nerves, and eyes, of 20 patients who underwent therapy for large space-occupying tumors. Performance variability was assessed through three geometric measures: volume, Dice similarity coefficient, and Euclidean distance. In addition, two simulated ground truth segmentations were calculated via the simultaneous truth and performance level estimation algorithm and a novel application of probability maps. The experts and automatic system were found to generate structures of similar volume, though the experts exhibited higher variation with respect to tubular structures. No difference was found between the mean Dice similarity coefficient (DSC) of the automatic and expert delineations as a group at a 5% significance level over all cases and organs. The larger structures of the brainstem and eyes exhibited mean DSC of approximately 0.8-0.9, whereas the tubular chiasm and nerves were lower, approximately 0.4-0.5. Similarly low DSCs have been reported previously without the context of several experts and patient volumes. This study, however, provides evidence that experts are similarly challenged. The average maximum distances (maximum inside, maximum outside) from a simulated ground truth ranged from (-4.3, +5.4) mm for the automatic system to (-3.9, +7.5) mm for the experts considered as a group. Over all the structures in a rank of true positive rates at a 2 mm threshold from the simulated ground truth, the automatic system ranked second of the nine raters. This work underscores the need for large scale studies utilizing statistically robust numbers of patients and experts in evaluating quality of automatic algorithms.

Biomechanical models that describe soft tissue deformation provide a relatively inexpensive way to correct registration errors in image-guided neurosurgical systems caused by nonrigid brain shift. Quantifying the factors that cause this deformation to sufficient precision is a challenging task. To circumvent this difficulty, atlas-based methods have been developed recently that allow for uncertainty, yet still capture the first-order effects associated with deformation. The inverse solution is driven by sparse intraoperative surface measurements, which could bias the reconstruction and affect the subsurface accuracy of the model prediction. Studies using intraoperative MR have shown that the deformation in the midline, tentorium, and contralateral hemisphere is relatively small. The dural septa act as rigid membranes supporting the brain parenchyma and compartmentalizing the brain. Accounting for these structures in models may be an important key to improving subsurface shift accuracy. A novel method to segment the tentorium cerebelli will be described, along with the procedure for modeling the dural septa. Results in seven clinical cases show a qualitative improvement in subsurface shift accuracy making the predicted deformation more congruous with previous observations in the literature. The results also suggest a considerably more important role for hyperosmotic drug modeling for the intraoperative shift correction environment.

Purpose: Intensity-modulated radiation therapy (IMRT) is the state of the art technique for head and neck cancer treatment. It requires precise delineation of the target to be treated and structures to be spared, which is currently done manually. The process is a time-consuming task of which the delineation of lymph node regions is often the longest step. Atlas-based delineation has been proposed as an alternative, but, in the authors' experience, this approach is not accurate enough for routine clinical use. Here, the authors improve atlas-based segmentation results obtained for level II-IV lymph node regions using an active shape model (ASM) approach., Methods: An average image volume was first created from a set of head and neck patient images with minimally enlarged nodes. The average image volume was then registered using affine, global, and local nonrigid transformations to the other volumes to establish a correspondence between surface points in the atlas and surface points in each of the other volumes. Once the correspondence was established, the ASMs were created for each node level. The models were then used to first constrain the results obtained with an atlas-based approach and then to iteratively refine the solution., Results: The method was evaluated through a leave-one-out experiment. The ASM- and atlas-based segmentations were compared to manual delineations via the Dice similarity coefficient (DSC) for volume overlap and the Euclidean distance between manual and automatic 3D surfaces. The mean DSC value obtained with the ASM-based approach is 10.7% higher than with the atlas-based approach; the mean and median surface errors were decreased by 13.6% and 12.0%, respectively., Conclusions: The ASM approach is effective in reducing segmentation errors in areas of low CT contrast where purely atlas-based methods are challenged. Statistical analysis shows that the improvements brought by this approach are significant.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can estimate parameters relating to blood flow and tissue volume fractions and therefore may be used to characterize the response of breast tumors to treatment. To assess treatment response, values of these DCE-MRI parameters are observed at different time points during the course of treatment. We propose a method whereby DCE-MRI data sets obtained in separate imaging sessions can be co-registered to a common image space, thereby retaining spatial information so that serial DCE-MRI parameter maps can be compared on a voxel-by-voxel basis. In performing inter-session breast registration, one must account for patient repositioning and breast deformation, as well as changes in tumor shape and volume relative to other imaging sessions. One challenge is to optimally register the normal tissues while simultaneously preventing tumor distortion. We accomplish this by extending the adaptive bases algorithm through adding a tumor-volume preserving constraint in the cost function. We also propose a novel method to generate the simulated breast magnetic resonance (MR) images, which can be used to evaluate the proposed registration algorithm quantitatively. The proposed nonrigid registration algorithm is applied to both simulated and real longitudinal 3D high resolution MR images and the obtained transformations are then applied to lower resolution physiological parameter maps obtained via DCE-MRI. The registration results demonstrate the proposed algorithm can successfully register breast MR images acquired at different time points and allow for analysis of the registered parameter maps.

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

A successful surface-based image-to-physical space registration in image-guided liver surgery (IGLS) is critical to provide reliable guidance information to surgeons and pertinent surface displacement data for use in deformation correction algorithms. The current protocol used to perform the image-to-physical space registration involves an initial pose estimation provided by a point based registration of anatomical landmarks identifiable in both the preoperative tomograms and the intraoperative presentation. The surface based registration is then performed via a traditional iterative closest point (ICP) algorithm between the preoperative liver surface, segmented from the tomographic image set, and an intraoperatively acquired point cloud of the liver surface provided by a laser range scanner. Using this more conventional method, the registration accuracy can be compromised by poor initial pose estimation as well as tissue deformation due to the laparotomy and liver mobilization performed prior to tumor resection. In order to increase the robustness of the current surface-based registration method used in IGLS, we propose the incorporation of salient anatomical features, identifiable in both the preoperative image sets and intraoperative liver surface data, to aid in the initial pose estimation and play a more significant role in the surface-based registration via a novel weighting scheme. Examples of such salient anatomical features are the falciform groove region as well as the inferior ridge of the liver surface. In order to validate the proposed weighted patch registration method, the alignment results provided by the proposed algorithm using both single and multiple patch regions were compared with the traditional ICP method using six clinical datasets. Robustness studies were also performed using both phantom and clinical data to compare the resulting registrations provided by the proposed algorithm and the traditional method under conditions of varying initial pose. The results provided by the robustness trials and clinical registration comparisons suggest that the proposed weighted patch registration algorithm provides a more robust method with which to perform the image-to-physical space registration in IGLS. Furthermore, the implementation of the proposed algorithm during surgical procedures does not impose significant increases in computation or data acquisition times.

Functional magnetic resonance imaging (FMRI) is a major tool for the evaluation of brain function and architecture. It is widely used by physicians, neuroscientists, psychologists and others. In order to process the data collected using FMRI, it is necessary to use post-acquisition processing software that employs motion correction and statistical modeling capabilities. These types of programs permit the user to extract the information about areas of brain activations that have occurred during a study. How well a particular motion-correction technique works and what effect it has on statistical processing are difficult to evaluate, since the level of activation present is not known a priori. This paper provides a description of the construction of a software phantom for use with FMRI post-acquisition processing tools with the properties that it is based on real subject data, has known locations and levels of activation, has known amounts of rigid body motion and noise added, and can be used to evaluate a processing system as if it were a real data set. Versions of the software phantom are available for downloading at the website: .

Purpose: To compare the accuracy and repeatability of a semiautomatic segmentation algorithm with those of manual segmentation for determining liver volume in living liver transplant donors at magnetic resonance (MR) imaging., Materials and Methods: The institutional review board approved this retrospective study and waived the requirement for informed consent. The semiautomatic segmentation algorithm is based on geometric deformable models and the level-set technique. It entails (a) placing initialization circle(s) on each image section, (b) running the algorithm, (c) inspecting and possibly manually modifying the contours obtained with the segmentation algorithm, and (d) placing lines to separate the liver segments. For 18 living donors (eight men and 10 women; mean age, 34 years; age range, 25-46 years), two observers each performed two semiautomatic and two manual segmentations on contrast material-enhanced T1-weighted MR images. Each measurement was timed. Actual graft weight was measured during surgery. The time needed for manual and that needed for semiautomatic segmentation were compared. Accuracy and repeatability were evaluated with the Bland-Altman method., Results: Mean interaction time was reduced from 25 minutes with manual segmentation to 5 minutes with semiautomatic segmentation. The mean total time for the semiautomatic process was 7 minutes 20 seconds. Differences between the actual volume and the estimated volume ranged from -223 to +123 mL for manual segmentation and from -214 to +86 mL for semiautomatic segmentation. The 95% limits of agreement for the ratio of actual graft volume to estimated graft volume were 0.686 and 1.601 for semiautomatic segmentation and 0.651 and 1.957 for manual segmentation. Semiautomatic segmentation improved estimation in 15 of 18 cases. Inter- and intraobserver repeatability was higher with semiautomatic segmentation., Conclusion: Use of the semiautomatic segmentation algorithm substantially reduces the time needed for volumetric measurement of liver segments while improving both accuracy and repeatability., ((c) RSNA, 2004)

Objective: Applications of three-dimensional ultrasound (3D US) are emerging throughout the field of medicine. In this study, tracked, free-hand 3D phantom US images were mapped to computed tomograms (CT) as a development for image-guided surgery (IGS) of the spine. In the operating room, the registration of tracked 3D US images to other imaging modalities, such as CT, could allow the surgeon to identify more precisely the surgical target area prior to the incision. An independent quantitative measure of registration accuracy using a fiducial marker system was provided., Methods: Three-dimensional free-hand US images of a phantom spine were created by tracking the transducer with an optical sensing system. Two sets of images were acquired from three lumbar vertebrae using 4.5- and 7.5-MHz transducers. These images were then segmented for the extraction of the posterior vertebral surface. Next, a surface-based registration of US to the corresponding segmented CT images was performed. Registration errors were computed as the distance between a set of target points transformed using the experimental transformation and the same set of target points transformed using fiducial markers as a gold standard., Results: Results indicated that alignment of these image sets is feasible using only part of the vertebral surface. In particular, the regions of the spinous process and laminae were used for registration. Target registration errors (TREs) were found to be lowest using the highest resolution CT images. Using the CT scans with 2-mm slice thickness, the TRE was calculated to be 1.33 +/- 0.30 mm for the 7.5-MHz US data set and 2.81 +/- 0.10 mm for the 4.5-MHz US data set. Moreover, residual errors in these surface alignments were 0.69 +/- 0.18 mm and 0.61 +/- 0.20 mm for the 4.5- and 7.5-MHz sets, respectively., Conclusion: A rigid, surface-based registration of CT images to phantom spinal US images, acquired with a free-hand, tracked transducer, is achievable with a limited, easily obtainable portion of the vertebral surface., (Copyright 2003 Wiley-Liss, Inc.)

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. These techniques have already been evaluated individually [27]. In this paper, however, we find that by grouping the techniques as volume based or surface based, we can make some interesting conclusions which were not visible in the earlier study. 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 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 (VOI's). 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. 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.

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.

Segmentation of the intracranial cavity in medical images is valuable in several research areas such as the quantitative analysis of normal and abnormal brain tissues, the registration of different imaging modalities (MRI, PET, CT) based on surface models of the brain, and the rendering of volume data. Because the manual delineation of the brain contour in the images can be demanding and error prone, an automatic procedure to perform this task is desirable. We have developed and tested a robust method that permits the automatic detection of the intracranial contour in transverse MR images. The method is described and its performance evaluated.