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D Vandermeulen

Publications and source records attributed to D Vandermeulen.

17 recordsLinked to original sources
PubMed2001

Automated segmentation of multiple sclerosis lesions by model outlier detection.

This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimates tissue-specific intensity models from the data itself, and incorporates contextual information in the classification using a Markov random field. The results of the automated method are compared with lesion delineations by human experts, showing a high total lesion load correlation. When the degree of spatial correspondence between segmentations is taken into account, considerable disagreement is found, both between expert segmentations, and between expert and automatic measurements.

Algorithms↗
PubMed1999

Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information.

Maximization of mutual information of voxel intensities has been demonstrated to be a very powerful criterion for three-dimensional medical image registration, allowing robust and accurate fully automated affine registration of multimodal images in a variety of applications, without the need for segmentation or other preprocessing of the images. In this paper, we investigate the performance of various optimization methods and multiresolution strategies for maximization of mutual information, aiming at increasing registration speed when matching large high-resolution images. We show that mutual information is a continuous function of the affine registration parameters when appropriate interpolation is used and we derive analytic expressions of its derivatives that allow numerically exact evaluation of its gradient. Various multiresolution gradient- and non-gradient-based optimization strategies, such as Powell, simplex, steepest-descent, conjugate-gradient, quasi-Newton and Levenberg-Marquardt methods, are evaluated for registration of computed tomography (CT) and magnetic resonance images of the brain. Speed-ups of a factor of 3 on average compared to Powell's method at full resolution are achieved with similar precision and without a loss of robustness with the simplex, conjugate-gradient and Levenberg-Marquardt method using a two-level multiresolution scheme. Large data sets such as 256(2) x 128 MR and 512(2) x 48 CT images can be registered with subvoxel precision in <5 min CPU time on current workstations.

Algorithms↗
PubMed1999

Automated model-based bias field correction of MR images of the brain.

We propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method we propose applies an iterative expectation-maximization (EM) strategy that interleaves pixel classification with estimation of class distribution and bias field parameters, improving the likelihood of the model parameters at each iteration. The algorithm, which can handle multichannel data and slice-by-slice constant intensity offsets, is initialized with information from a digital brain atlas about the a priori expected location of tissue classes. This allows full automation of the method without need for user interaction, yielding more objective and reproducible results. We have validated the bias correction algorithm on simulated data and we illustrate its performance on various MR images with important field inhomogeneities. We also relate the proposed algorithm to other bias correction algorithms.

Algorithms↗
PubMed1999

Automated model-based tissue classification of MR images of the brain.

We describe a fully automated method for model-based tissue classification of magnetic resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single- and multispectral MR images, corrects for MR signal inhomogeneities, and incorporates contextual information by means of Markov random Fields (MRF's). A digital brain atlas containing prior expectations about the spatial location of tissue classes is used to initialize the algorithm. This makes the method fully automated and therefore it provides objective and reproducible segmentations. We have validated the technique on simulated as well as on real MR images of the brain.

Algorithms↗
PubMed1999

Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part I, Methodology and validation on normal subjects.

The study presented in this paper tests the hypothesis that the combination of a global similarity transformation and local free-form deformations can be used for the accurate segmentation of internal structures in MR images of the brain. To quantitatively evaluate our approach, the entire brain, the cerebellum, and the head of the caudate have been segmented manually by two raters on one of the volumes (the reference volume) and mapped back onto all the other volumes, using the computed transformations. The contours so obtained have been compared to contours drawn manually around the structures of interest in each individual brain. Manual delineation was performed twice by the same two raters to test inter- and intrarater variability. For the brain and the cerebellum, results indicate that for each rater, contours obtained manually and contours obtained automatically by deforming his own atlas are virtually indistinguishable. Furthermore, contours obtained manually by one rater and contours obtained automatically by deforming this rater's own atlas are more similar than contours obtained manually by two raters. For the caudate, manual intra- and interrater similarity indexes remain slightly better than manual versus automatic indexes, mainly because of the spatial resolution of the images used in this study. Qualitative results also suggest that this method can be used for the segmentation of more complex structures, such as the hippocampus.

Algorithms↗
PubMed1998

The EASI project--improving the effectiveness and quality of image-guided surgery.

In recent years, advances in computer technology and a significant increase in the accuracy of medical imaging have made it possible to develop systems that can assist the clinician in diagnosis, planning, and treatment. This paper deals with an area that is generally referred to as computer-assisted surgery, image-directed surgery, or image-guided surgery. We report the research, development, and clinical validation performed since January 1996 in the European Applications in Surgical Interventions (EASI) project, which is funded by the European Commission in their "4th Framework Telematics Applications for Health" program. The goal of this project is the improvement of the effectiveness and quality of image-guided neurosurgery of the brain and image-guided vascular surgery of abdominal aortic aneurysms, while at the same time reducing patient risks and overall cost. We have developed advanced prototype systems for preoperative surgical planning and intraoperative surgical navigation, and we have extensively clinically validated these systems. The prototype systems and the clinical validation results are described in this paper.

Quality of Health Care↗
PubMed1997

Multimodality image registration by maximization of mutual information.

A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. Maximization of MI is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the MI criterion is validated for rigid body registration of computed tomography (CT), magnetic resonance (MR), and photon emission tomography (PET) images by comparison with the stereotactic registration solution, while robustness is evaluated with respect to implementation issues, such as interpolation and optimization, and image content, including partial overlap and image degradation. Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications.

Algorithms↗
PubMed1996

Assessment of accuracy of renal artery stenosis grading in helical CT angiography using maximum intensity projections.

The purpose of the study was to investigate whether visual inspection of maximum intensity projection (MIP) images is a reliable method for assessment of the severity of renal artery stenoses (RAS). Therefore, 20 RAS were investigated with helical CT. Native axial images and MIP images were analysed separately or in combination ("two-step method"). A phantom study was performed to investigate the influence of window setting on apparent stenosis severity. Accuracy for diagnosis of 50-69% and 70-99% RAS and the mean inter-observer agreement were 82.5%, 77.5% and 82.5% respectively, using study of MIP images alone, and 100%, 97.5% and 95% using the "two-step method". The phantom study showed that the apparent severity of vascular stenoses on MIP images depends on the selected window centre values. It is concluded that combined evaluation of both MIP images and native axial images is more reliable than study of MIP images alone for assessing the severity of RAS.

Aged↗
PubMed1995

The use of magnetic resonance angiography in stereotactic neurosurgery.

The authors discuss the advantages and disadvantages of the use of magnetic resonance (MR) angiography images in stereotactic neurosurgery. Current computer programs designed to assist the neurosurgeon in the planning of stereotactic neurosurgical interventions use intraarterial digital subtraction angiography images to visualize the blood vessels. Magnetic resonance angiography is a recent technique with a number of advantages over the digital subtraction method: it is less invasive and less prone to complications; it provides truly three-dimensional data sets that can be viewed from any direction; and it can visualize both stationary and flowing tissues with the same imaging device and localizer frame. Although digital subtraction images are still superior in contrast and vascular detail, state-of-the-art high-resolution MR angiography sequences provide sufficient vascular detail for planning surgery. Contrast-enhanced MR angiography images were acquired using adapted gradient-echo sequences to compensate for flow-induced distortions; postacquisition distortion correction was not necessary. Five methods to integrate and inspect a possible trajectory in the MR angiography data are discussed. Initial clinical experience with eight patients led to the conclusion that MR angiography is a valuable imaging modality that can be integrated reliably into a stereotactic neurosurgery planning procedure.

Adult↗
PubMed1994

On the problem of geometric distortion in magnetic resonance images for stereotactic neurosurgery.

In this paper, we discuss the issue of geometric distortion in magnetic resonance (MR) images used to plan stereotactic neurosurgical interventions. We analyze the process for the case of Fourier transform imaging and demonstrate that spatial misregistrations are fundamentally due to two causes: deviations of the magnetic field from its ideal value and blood flow. This enables us to relate the causes of geometric distortion to the MR imaging system, the patient and the stereotactic localizer frame. Based on the general model, we propose model refinements and discuss methods for the quantification and correction of all causes. The results of our calculations and experiments indicate that, using the proposed corrections, MRI and MR angiography should be considered valuable and reliable acquisition modalities for the planning of stereotactic neurosurgical interventions.

Brain↗
PubMed1994

How does the stereotactic workstation help the neurosurgeon?

At the KUL University of Leuven a workstation for the planning of neurosurgical stereotactic procedures has been developed. Its benefits are illustrated in three exemplary cases. The CT and/or MR images, acquired under stereotactic conditions, are transmitted via a PACS network (picture archiving and communication systems) directly to the stereotactic workstation in the operating theater. Target and entry point can be accurately defined on zoomed images. The trajectory can be checked and modified on all registered data sets and on resliced images along any plane. Maximum intensity projection of magnetic resonance angiography data sets along any arbitrary direction show the relative position of the blood vessels and the trajectory. During the preceding 32 months 29 patients were operated on using the stereotactic workstation. Postoperatively no new neurological deficit was observed in any of these patients. The workstation improves patient safety and increases the accuracy of neurosurgical stereotactic operations, because it helps the neurosurgeon to avoid blood vessels and/or important functional areas.

Adult↗
PubMed1993

Image segmentation: methods and applications in diagnostic radiology and nuclear medicine.

We review and discuss different classes of image segmentation methods. The usefulness of these methods is illustrated by a number of clinical cases. Segmentation is the process of assigning labels to pixels in 2D images or voxels in 3D images. Typically the effect is that the image is split up into segments, also called regions or areas. In medical imaging it is essential for quantification of outlined structures and for 3D visualization of relevant image data. Based on the level of implemented model knowledge we have classified these methods into (1) manual delineation, (2) low-level segmentation, and (3) model-based segmentation. Pure manual delineation of structures in a series of images is time-consuming and user-dependent and should therefore be restricted to quick experiments. Low-level segmentation analyzes the image locally at each pixel in the image and is practically limited to high-contrast images. Model-based segmentation uses knowledge of object structure such as global shape or semantic context. It typically requires an initialization, for example in the form of a rough approximation of the contour to be found. In practice it turns out that the use of high-level knowledge, e.g. anatomical knowledge, in the segmentation algorithm is quite complicated. Generally, the number of clinical applications decreases with the level and extent of prior knowledge needed by the segmentation algorithm. Most problems of segmentation inaccuracies can be overcome by human interaction. Promising segmentation methods for complex images are therefore user-guided and thus semi-automatic. They require manual intervention and guidance and consist of fast and accurate refinement techniques to assist the human operator.

Algorithms↗
PubMed1990

A prototype medical workstation for computer-assisted stereotactic neurosurgery.

We have developed a prototype display workstation for use in stereotactic neurosurgery. Patient image data from computed tomography, magnetic resonance imaging, and digital subtraction angiography are acquired with the stereotactic frame in place and subsequently transferred to the workstation for further processing. Target points may be identified on any image type and probe trajectories defined. Any point or line indicated on one set of images may be transferred immediately to other images, to determine, for instance, safe avascular probe paths. We present some general outlines for the use of computers for stereotactic neurosurgery and discuss the different components of the current system. Finally, we make some suggestions as to further developments.

Brain↗
PubMed1987

Angiographic localizer ring for the BRW stereotactic system.

An accessory locating device to the existing BRW stereotactic system is presented. It can be used as a reference device to locate angiographic data with respect to the BRW stereotactic system. Hence, the projection of target points onto angiograms, visible on CT scans, are easily calculated, as well as the stereotactic coordinates of a set of points (e.g., AVM) indicated on at least two angiograms. As a final result integrated images of cerebral blood vessels and an outline of tumor lesions can be generated using more sophisticated computer equipment.

Cerebral Angiography↗
PubMed1987

Angiographic localizer for the BRW stereotactic system.

Preliminary experience with a newly constructed angiographic localizer system for use in stereotactic neurosurgery is reported. This localizer ring, mounted on the BRW head ring, allows for the transformation of target points with known stereotactic coordinates (e.g., visible on computerized tomography scans) onto angiograms, as well as the determination of stereotactic coordinates of a set of points (e.g., arteriovenous malformations) indicated on at least two angiograms.

Equipment Design↗
PubMedDate not supplied

Comparison and evaluation of retrospective intermodality brain image registration techniques.

PURPOSE: The primary objective of this study is to perform a blinded evaluation of a group of retrospective image registration techniques using as a gold standard a prospective, marker-based registration method. 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. A secondary goal of the project is to evaluate the importance of correcting geometrical distortion in MR images by comparing the retrospective registration error in the rectified images, i.e., those that have had the distortion correction applied, with that of the same images before rectification. METHOD: Image volumes of three modalities (CT, MR, and 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), i.e., areas in the brain that would commonly be areas of neurological interest. A VOI is defined in the MR image and its centroid c is determined. Then, the prospective registration is used to obtain the corresponding point c' in CT or PET. To this point, the retrospective registration is then applied, producing c" in MR. Statistics are gathered on the target registration error (TRE), which is the distance between the original point c and its corresponding point c". RESULTS: This article presents statistics on the TRE calculated for each registration technique in this study and provides a brief description of each technique and an estimate of both preparation and execution time needed to perform the registration. CONCLUSION: Our results indicate that retrospective techniques have the potential to produce satisfactory results much of the time, but that visual inspection is necessary to guard against large errors.

Brain↗