Search

A method by which to register multimodality medical images accommodating soft tissue deformation is presented in the context of interventional therapy with a MR scanner. Accuracy testing with arbitrarily deformed MR images and application studies of a pig's brain were undertaken to evaluate the feasibility of the method. When Mutual Information is employed as the voxel similarity measure in the matching energy function, the algorithm can accommodate multimodality images. Coupled with rigid registration, the deformable registration of pre- and intraoperative multi-modality images enables surgeons to precisely define critical anatomical structures, such as vessels and functional areas, and to localize and optimize trajectories. The method directly and automatically works on volumetric multimodality images. Thus the algorithm is suitable for intraoperative registration, where stability and simplicity are desirable. [ABSTRACT FROM AUTHOR]

We examine the enforcement patterns of the INS and find that while the INS vows to enforce the immigration laws in an equitable manner, there is significant variability in the agency's enforcement patterns. In states where construction jobs represent a large portion of the workforce, INS activity is significantly lower. Furthermore, while the agency is very active in enforcement in states where Russian and Haitian immigrants are prevalent, they appear to relax enforcement in states where Chinese, Jamaicans, and Mexicans reside. The differences in enforcement patterns are statistically significant, and suggest actions taken by the INS may be politically motivated. [ABSTRACT FROM AUTHOR]

This study examined the possible link between congressional membership on one of two powerful committees with Environmental Protection Agency (EPA) oversight power and the incidence of EPA citations being levied against those firms headquartered within the committee members' home districts. Using a sample of 109 Fortune 500 firms for the 1992-1993 time period, the results suggest a significant and negative link between committee membership on either the House Appropriations Committee or the House Veterans Affairs, Housing and Urban Development, and Independent Agencies Committee, and the number of EPA citations levied against firms headquartered in the districts of the congresspersons serving on these committees. The results suggest that politicians may exercise power in order to protect their constituents, rather than to protect the national interest. [ABSTRACT FROM AUTHOR]

A novel framework for joint clustering and point-by-point mapping of white matter fiber pathways is presented. Accurate clustering of the trajectories into fiber bundles requires point correspondence determined along the fiber pathways. This knowledge is also crucial for any tract-oriented quantitative analysis. We employ an expectation-maximization (EM) algorithm to cluster the trajectories in a Gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster, an estimate of the cluster parameters, and point correspondences along the trajectories. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center. Probabilistic assignment of the trajectories to clusters is controlled by imposing a minimum threshold on the membership probabilities, to remove outliers in a principled way. The presented results confirm the efficiency and effectiveness of the proposed framework for quantitative analysis of diffusion tensor MRI. [ABSTRACT FROM AUTHOR]

We describe a new approach for estimating the posterior probability of tissue labels. Conventional likelihood models are combined with a curve length prior on boundaries, and an approximate posterior distribution on labels is sought via the Mean Field approach. Optimizing the resulting estimator by gradient descent leads to a level set style algorithm where the level set functions are the logarithm-of-odds encoding of the posterior label probabilities in an unconstrained linear vector space. Applications with more than two labels are easily accommodated. The label assignment is accomplished by the Maximum A Posteriori rule, so there are no problems of "overlap" or "vacuum". We test the method on synthetic images with additive noise. In addition, we segment a magnetic resonance scan into the major brain compartments and subcortical structures. [ABSTRACT FROM AUTHOR]

We present a new objective function for the registration of multi-modal medical images. Our novel similarity metric incorporates both knowledge about the current observations and information gained from previous registration results and combines the relative influence of these two types of information in a principled way. We show that in the absence of prior information, the method reduces approximately to the popular entropy minimization approach of registration and we provide a theoretical comparison of incorporating prior information in our and other currently existing methods. We also demonstrate experimental results on real images. [ABSTRACT FROM AUTHOR]

Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases, segmentation is largely performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We present an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior information. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. Structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the maximum a posteriori probability estimation problem. We demonstrate the approach on 20 brain magnetic resonance images showing superior performance, particularly in cases where purely image based methods fail. [ABSTRACT FROM AUTHOR]

We present a population registration framework that acts on large collections or populations of data volumes. The data alignment procedure runs in a simultaneous fashion, with every member of the population approaching the central tendency of the collection at the same time. Such a mechanism eliminates the need for selecting a particular reference frame a priori, resulting in a non-biased estimate of a digital atlas. Our algorithm adopts an affine congealing framework with an information theoretic objective function and is optimized via a gradient-based stochastic approximation process embedded in a multi-resolution setting. We present experimental results on both synthetic and real images. [ABSTRACT FROM AUTHOR]

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging. [ABSTRACT FROM AUTHOR]

We present a statistical framework that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image inhomogeneities, anatomical labelmap, and a mapping from the atlas to the image space. An example of the approach is given for a brain structure-dependent affine mapping approach. The algorithm produces high quality segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. In addition, we show that the approach performs better than similar methods which separate the registration and segmentation problems. [ABSTRACT FROM AUTHOR]

We present a software package which uniquely integrates several facets of image-guided medicine into a single portable, extendable environment. It provides capabilities for automatic registration, semi-automatic segmentation, 3D surface model generation, 3D visualization, and quantitative analysis of various medical scans. We describe its system architecture, wide range of applications, and novel integration with an interventional Magnetic Resonance (MR) scanner to augment intra-operative imaging with pre-operative data. Analysis previously reserved for pre-operative data can now be applied to exploring the anatomical changes as the surgery progresses. Surgical instruments are tracked and used to drive the location of reformatted slices. Real-time scans are visualized as slices in the same 3D view along with the pre-operative slices and surface models. The system has been applied in over 20 neurosurgical cases at Brigham and Women's Hospital, and continues to be routinely used for 1-3 cases per week.Ron Kikinis and Ferenc Jolesz received partial support from NIH grants P41 RR13218-01, P01 CA67165-03, and R01 RR11747-01A. Eric Grimson received partial support from NSF grant IIS-9610249. [ABSTRACT FROM AUTHOR]

A method to measure spatial and temporal brain deformation from sequential intraoperative Magnetic Resonance Images (MRI) and its preliminary clinical results are reported. Deformation is estimated with a volumetric optical flow measurement based on local intensity differences. A multi-resolution approach was used to efficiently estimate the deformation. We applied the method to five different cases and the method is highlighted by illustrative features accompanied by five sets of intraoperative MRI scanned before and after dura opening, twice during tumor resection and immediately after dura closure. The maximum cortical surface shift measured was 11 mm and subsurface shift was 4 mm. Volume change was measured by aligning the sequence of intraoperative MR images immediately after the opening of the dura to the images during the tumor resection. The amount of deformation present at each stage of the surgery was visualized. The computed deformation field was most satisfactory when the skin was first segmented and removed from the images prior to the optical flow computation. Magnetic field inhomegeneities as well as administration of contrast agent (Gadolinium-DTPA) were observed to modify the deformation field. The method demonstrated a good capability of intra-operative surface, subsurface and midline shift measurement. [ABSTRACT FROM AUTHOR]

A new information-theoretic approach is presented for analyzing fMRI data to calculate the brain activation map. The method is based on a formulation of the mutual information between two waveforms-the fMRI temporal response of a voxel and the experimental protocol timeline. Scores based on mutual information are generated for all voxels and then used to compute the activation map of an experiment. Mutual information for fMRI analysis is employed because it has been shown to be robust in quantifying the relationship between any two waveforms. More importantly, our technique takes a principled approach toward calculating the brain activation map by making few assumptions about the relationship between the protocol timeline and the temporal response of a voxel. This is important especially in fMRI experiments where little is known about the relationship between these two waveforms. Experiments are presented to demonstrate this approach of computing the brain activation map. Comparisons to other more traditional analysis techniques are made and the results are presented. [ABSTRACT FROM AUTHOR]

We present an approach which performs the automatic labeling of the main cortical sulci using a priori information for the 3D spatial distribution of these entities. We have developed a methodology to extract the 3D cortical topography of a particular subject from in vivo observations obtained through MRI. The cortical topography is encoded in a relational graph structure composed of two main features: arcs and vertices. Each vertex contains a parametric surface representing the buried part of a sulcus. Points on this parametric surface are expressed in stereotaxic coordinates (i.e., with respect to a standardized brain coordinate system). Arcs represent the connections between these entities. Manual sulcal labeling is performed by tagging a sulcal surface in the 3-D graph and selecting from a menu of candidate sulcus names. Automatic labeling is dependent on a probabilistic atlas of sulcal anatomy derived from a set of 51 graphs that were labeled by an anatomist. We show how these 3D sulcal spatial distribution maps can be used to perform the identification of the cortical sulci. We focus our attention on the peri-central area (including pre-central, post-central and central sulci). Results show that the use of spatial priors permit automatic identification of the main sulci with a good accuracy. [ABSTRACT FROM AUTHOR]

This paper describes the development of a needle insertion manipulator for stereotactic neurosurgery. This robot fulfils the requirements of having both a safe mechanical design and the capacity for being sterilized. Many kinds of robots are examined in neurosurgery. Their purpose is the precise positioning of surgical instruments such as biopsy needles, electrodes etc. Some are already available commercially and have been proven useful in the operating theatre. However, their clinical application is limited by specific problems including cost, safety, positioning requirements, maintenance requirements. The main problems have been with the safety of the mechanical design and difficulties with sterilization and disinfecting pre-and post operatively. The manipulator described in this report achieves mechanical safety and has the capacity for cover-sheet-free sterilization. The manipulator has three major components: the main mechanical component (with 6 degrees of freedom), the torque transmission component, and the electric motor, which cannot be sterilized. The electrical parts are detachable. Using this mechanism, we can clearly separate the surgical area from the mechatronics components. In this paper, the basic design and the prototype development and testing are described. [ABSTRACT FROM AUTHOR]

In this paper, we propose a robust fully non-supervised method dedicated to the segmentation of the brain in T1-weighted MR images. The first step consists in the analysis of the scale-space of the histogram first and second derivative. We show first that the crossings in scale-space of trajectories of extrema of different derivative orders follow regular topological properties. These properties allow us to design a new structural representation of a 1D signal. Then we propose an heuristics using this representation to infer statistics on grey and white matter grey level values from the histogram. These statistics are used by an improved morphological process combining two opening sizes to segment the brain. The method has been validated with 70 images coming from 3 different scanners and acquired with various MR sequences. [ABSTRACT FROM AUTHOR]

A new, practical, and efficient approach is proposed for 3D vascular segmentation and bifurcation structure extraction. The method uses a combination of mathematical morphology, region-growing schemes, and shape features in addition to greyscale information. By an extension of math-morphological operations within bounded space of vascular shape, smooth and natural region-growing and sensitivity-controllable bifurcation detection were realized. The algorithm was implemented in the interactive segmentation and visualization software package VOXEL-MAN and validated with clinical data of X-ray CT angiography and MRA. [ABSTRACT FROM AUTHOR]

A new method to segment MR volumes has been developed. The method matches elastically a 3D deformable prior model, describing the structures of interest, to the MR volume of a patient. The deformation is done using a deformation grid. Oriented distance maps are utilized to guide the deformation process. Two alternative restrictions are used to preserve the geometrical prior knowledge of the model. The method is applied to extract the body, the lungs and the heart. The segmentation is needed to build individualized boundary element models for bioelectromagnetic inverse problem. The method is fast, automatic and accurate. Good results have been achieved for four MR volumes tested so far. [ABSTRACT FROM AUTHOR]

This paper proposes a method for fully automatic segmentation of brain tissues and MR bias field correction using a digital brain atlas. We have extended the EM segmentation algorithm, including an explicit parametric model of the bias field. The algorithm interleaves classification with parameter estimation, yielding better results at every iteration. The method can handle multi-channel data and slice-per-slice constant offsets, and is fully automatic due to the use of a digital brain atlas. [ABSTRACT FROM AUTHOR]

We have used MRI-based three-dimensional (3D) reconstruction and a real-time, frameless, stereotactic navigation device to facilitate the removal of seizure foci in children suffering from intractable epilepsy. Using this system, the location of subdural grid and strip electrodes is recorded on the 3D model to facilitate focus localization and resection. Ten operations were performed — two girls and eight boys ranging in age from 3-17 — during which 3D reconstruction and surgical instrument tracking navigation was used. In all cases, the patients tolerated the procedure well and showed no post-operative neurological deficits. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of segmenting bone from Computed Tomography (CT) data. In clinical practice, identification of bone is done by thresholding, a method which is simple and fast. Unfortunately, thresholding alone has significant limitations. In particular, segmentation of thin bone structures and of joint spaces is problematic. This problem is particularly severe for thin bones such as in the skull (the paranasal sinus and around the orbit). Another area where current techniques often fail is automatic, reliable and robust identification of individual bones, which requires precise separation of the joint spaces. This paper presents a novel solution to these problems based on three-dimensional filtering techniques. Improvement of the segmentation results in more accurate 3D models for the purpose of surgical planning and intraoperative navigation. [ABSTRACT FROM AUTHOR]

This paper presents a method for automatic segmentation of the tibia and femur in clinical magnetic resonance images of knees. Texture information is incorporated into an active contours framework through the use of vector-valued geodesic snakes with local variance as a second value at each pixel, in addition to intensity. This additional information enables the system to better handle noise and the non-uniform intensities found within the structures to be segmented. It currently operates independently on 2D images (slices of a volumetric image) where the initial contour must be within the structure but not necessarily near the boundary. These separate segmentations are stacked to display the performance on the entire 3D structure. [ABSTRACT FROM AUTHOR]

The in vivo investigation of joint kinematics in normal and injured wrist requires the segmentation of carpal bones from 3D (CT) images and their registration over time. The non-uniformity of bone tissue, ranging from dense cortical bone to textured spongy bone, the irregular, small shape of closely packed carpal bones which move with respect to one another, and with respect to CT resolution, augmented with the presence of blood vessels, and the inherent blurring of CT imaging renders the segmentation of carpal bones a challenging task. Specifically, four characteristic difficulties are prominent: (i) gaps or weak edges in the carpal bone surfaces, (ii) diffused edges, (iii) textured regions, and, (iv) extremely narrow inter-bone regions. We review the performance of statistical classification, deformable models, region growing, and morphological operations for this application. We then propose a model which combines several of these approaches in a single framework. Specifically, initialized seeds grow in a curve evolution implementation of active contours, but where growth is modulated by a skeletally-mediated competition between neighboring regions, thus combining the advantages of local and global region growing methods, region competition and active contours. This approach effectively deals with many of the difficulties presented above as illustrated by numerous examples. [ABSTRACT FROM AUTHOR]

We present an approach to elastic registration of tomographic brain images which is based on thin-plate splines and takes into account landmark errors. The inclusion of error information is important in clinical applications since landmark extraction is always prone to error. In comparison to previous work, our approach can cope with anisotropic errors, which is significantly more realistic than dealing only with isotropic errors. In particular, it is now possible to include different types of landmarks, e.g., quasi-landmarks at the outer contour of the brain. Also, we introduce an approach to estimate landmark localization uncertainties directly from the image data. Experimental results are presented for the registration of 2D and 3D MR images. [ABSTRACT FROM AUTHOR]

This paper describes a new method of non-rigid registration using the combined power of elastic and statistical shape models. The transformations are constrained to be consistent with a physical model of elasticity to maintain smoothness and continuity. A Bayesian formulation, based on this model, on an intensity similarity measure, and on statistical shape information embedded in corresponding boundary points, is employed to find a more accurate and robust non-rigid registration. A dense set of forces arises from the intensity similarity measure to accommodate complex anatomical details. A sparse set of forces constrains consistency with statistical shape models derived from a training set. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approach. It is shown that statistical boundary shape information significantly augments and improves elastic model based non-rigid registration. [ABSTRACT FROM AUTHOR]

A comparison of six similarity measures for use in intensity based 2D-3D image registration is presented. The accuracy of the similarity measures are compared to a "gold-standard" registration which has been accurately calculated using fiducial markers. The similarity measures are used to register a CT scan to a fluoroscopy image of a spine phantom. The registration is carried out within a region of interest in the fluoroscopy image which is user defined to contain a single vertebra. Many of the problems involved in this type of registration are caused by features which were not modelled by a phantom image alone. More realistic "gold standard" data sets were simulated using the phantom image with clinical image features overlaid. Results show that the introduction of soft tissue structures and interventional instruments into the phantom image can have a large effect on the performance of some similarity measures previously applied to 2D-3D image registration. Two measures were able to register accurately and robustly even when soft tissue structures and interventional instruments were present as differences between the images. These measures are called pattern intensity and gradient difference. [ABSTRACT FROM AUTHOR]

We present a new approach for the non-rigid registration of contrast-enhanced breast MRI using normalised mutual information. A hierarchical transformation model of the motion of the breast has been developed: The global motion of the breast is modelled using affine transformation models while the local motion of the breast is modelled using spline-based free-form deformation (FFD) models. The algorithm has been applied to the fully automated registration of 3D breast MRI. In particular, we have compared the results of the proposed non-rigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the non-rigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms. [ABSTRACT FROM AUTHOR]

We propose a new way of embedding shape distributions in a topological deformable template. These distributions rely on global shape descriptors corresponding to the 3D moment invariants. In opposition to usual Fourier-like descriptors, they can be updated during deformations at a relatively low cost. The moment-based distributions are included in a framework allowing the management of several simultaneously deforming objects. This framework is dedicated to the segmentation of brain deep nuclei in 3D MR images. The paper focuses on the learning of the shape distributions, on the initialization of the topological model and on the multi-resolution energy minimization process. Results are presented showing the segmentation of twelve brain deep structures. [ABSTRACT FROM AUTHOR]