Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images

Measuring tortuosity of the intracerebral vasculature from MRA images

The clinical recognition of abnormal vascular tortuosity, or excessive bending, twisting, and winding, is important to the diagnosis of many diseases. Automated detection and quantitation of abnormal vascular tortuosity from three-dimensional (3-D) medical image data would, therefore, be of value. However, previous research has centered primarily upon two-dimensional (2-D) analysis of the special subset of vessels whose paths are normally close to straight. This report provides the first 3-D tortuosity analysis of clusters of vessels within the normally tortuous intracerebral circulation. We define three different clinical patterns of abnormal tortuosity. We extend into 3-D two tortuosity metrics previously reported as useful in analyzing 2-D images and describe a new metric that incorporates counts of minima of total curvature. We extract vessels from MRA data, map corresponding anatomical regions between sets of normal patients and patients with known pathology, and evaluate the three tortuosity metrics for ability to detect each type of abnormality within the region of interest. We conclude that the new tortuosity metric appears to be the most effective in detecting several types of abnormalities. However, one of the other metrics, based on a sum of curvature magnitudes, may be more effective in recognizing tightly coiled, "corkscrew" vessels associated with malignant tumors.

Guide-wire tracking during endovascular interventions

A method is presented to extract and track the position of a guide wire during endovascular interventions under X-ray fluoroscopy. The method can be used to improve guide-wire visualization in low-quality fluoroscopic images and to estimate the position of the guide wire in world coordinates. A two-step procedure is utilized to track the guide wire in subsequent frames. First, a rough estimate of the displacement is obtained using a template-matching procedure. Subsequently, the position of the guide wire is determined by fitting a spline to a feature image. The feature images that have been considered enhance line-like structures on: 1) the original images; 2) subtraction images; and 3) preprocessed images in which coherent structures are enhanced. In the optimization step, the influence of the scale at which the feature is calculated and the additional value of using directional information is investigated. The method is evaluated on 267 frames from ten clinical image sequences. Using the automatic method, the guide wire could be tracked in 96% of the frames, with a similar accuracy to three observers, although the position of the tip was estimated less accurately.

Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans

Computer-aided diagnostic (CAD) schemes have been developed to assist radiologists in the early detection of lung cancer in radiographs and computed tomography (CT) images. In order to improve sensitivity for nodule detection, many researchers have employed a filter as a preprocessing step for enhancement of nodules. However, these filters enhance not only nodules, but also other anatomic structures such as ribs, blood vessels, and airway walls. Therefore, nodules are often detected together with a large number of false positives caused by these normal anatomic structures. In this study, we developed three selective enhancement filters for dot, line, and plane which can simultaneously enhance objects of a specific shape (for example, dot-like nodules) and suppress objects of other shapes (for example, line-like vessels). Therefore, as preprocessing steps, these filters would be useful for improving the sensitivity of nodule detection and for reducing the number of false positives. We applied our enhancement filters to synthesized images to demonstrate that they can selectively enhance a specific shape and suppress other shapes. We also applied our enhancement filters to real two-dimensional (2D) and three-dimensional (3D) CT images to show their effectiveness in the enhancement of specific objects in real medical images. We believe that the three enhancement filters developed in this study would be useful in the computerized detection of cancer in 2D and 3D medical images.

Localization and segmentation of aortic endografts using marker detection

A method for localization and segmentation of bifurcated aortic endografts in computed tomographic angiography (CTA) images is presented. The graft position is determined by detecting radiopaque markers sewn on the outside of the graft. The user indicates the first and the last marker, whereupon the remaining markers are automatically detected. This is achieved by first detecting marker-like structures through second-order scaled derivative analysis, which is combined with prior knowledge of graft shape and marker configuration. The identified marker centers approximate the graft sides and, derived from these, the central axis. The graft boundary is determined by maximizing the local gradient in the radial direction along a deformable contour passing through both sides. Three segmentation methods were tested. The first performs graft contour detection in the initial CT-slices, the second in slices that were reformatted to be orthogonal to the approximated graft axis, and the third uses the segmentation from the second method to find a more reliable approximation of the axis and subsequently performs contour detection. The methods have been applied to ten CTA images and the results were compared to manual marker indication by one observer and region growing aided segmentation by three observers. Out of a total of 266 markers, 262 were detected. Adequate approximations of the graft sides were obtained in all cases. The best segmentation results were obtained using a second iteration orthogonal to the axis determined from the first segmentation, yielding an average relative volume of overlap with the expert segmentations of 92%, while the interexpert reproducibility is 95%. The averaged difference in volume measured by the automated method and by the experts equals the difference among the experts: 3.5%.

Efficient center-line extraction for quantification of vessels in confocal microscopy images

In this paper we present a novel method for the three-dimensional (3-D) centerline extraction of elongated objects such as vessels. This method combines the basic ideas in distance transform-based, thinning, and path planning methods to extract thin and connected centerlines. This efficient approach needs no user interaction or any prior knowledge of the object shape. We used the path planning approach, which has exclusively been used in the virtual endoscopy or robotics, to obtain the medial curve of the objects. To make our approach fully automated, a distance transform mapping is used to identify the end points of the object branches. The initial paths are also constructed on the surface of the object, traversing the same distance map. Then a thinning algorithm centralizes the paths. The proposed approach is especially efficient for centerline extraction of the complex branching structures. The method has been applied on the confocal microscopy images of rat brains and the results confirm its efficiency in extracting the medial curve of vessels, essential for the computation of quantitative parameters.

Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis

A fully automated method for computerized detection of pulmonary embolism in spiral computed tomography angiography was developed based on volumetric image analysis. The detection method is based on segmentation of pulmonary vessels to limit the search space, and analysis of several three-dimensional features inside segmented vessel volume. The features utilized are vascular size, local contrast based on mathematical morphology, degree of curvilinearity based on second derivatives, and geometric features such as volume and length. Detection results were obtained for 19 clinical data sets and the performance of the method was evaluated. Using the number and locations of thrombi diagnosed by radiologists as the gold standard, 100% sensitivity was achieved with 7.7 false positives per case, and 85% sensitivity was obtained with 2.6 false positives. For identification of all the positive cases as positive, i.e., detection of at least one thrombus per positive case, 1.9 false positives per case were obtained. These preliminary results suggest that the method has potential for fully automated detection of pulmonary embolism.

A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II

Vascular segmentation has recently been given much attention. This review paper has two parts. Part I of this review focused on the physics of magnetic resonance angiography (MRA) and prefiltering techniques applied to MRA. Part II of this review presents the state-of-the-art overview, status, and new achievements in vessel segmentation algorithms from MRA. The first part of this review paper is focused on the nonskeleton or direct-based techniques. Here, we present eight different techniques along with their mathematical foundations, algorithms and their pros and cons. We will also focus on the skeleton or indirect-based techniques. We will discuss three different techniques along with their mathematical foundations, algorithms and their pros and cons. This paper also includes a clinical discussion on skeleton versus nonskeleton-based segmentation techniques. Finally, we shall conclude this paper with the possible challenges, the future, and a brief summary on vascular segmentation techniques.

Diffusion-enhanced visualization and quantification of vascular anomalies in three-dimensional rotational angiography: Results of an in-vitro evaluation

Three-dimensional rotational angiography (3DRA) is a new and promising technique for obtaining high-resolution isotropic 3D images of vascular structures. However, due to the relatively high noise level and the presence of other background structures in clinical 3DRA images, noise reduction is inevitable. In this paper, we evaluate a number of linear and nonlinear diffusion techniques for this purpose. Specifically, we analyze the effects of these techniques on the threshold-based visualization and quantification of vascular anomalies in 3DRA images. The results of in-vitro experiments indicate that edge-enhancing anisotropic diffusion filtering is most suitable: the increase in the user-dependency of visualizations and quantifications is considerably less with this technique compared to linear filtering techniques, and it is better at reducing noise near edges than isotropic nonlinear diffusion. However, in view of the memory and computation-time requirements of this technique, the latter scheme may be considered a useful alternative.

3D MRA coronary axis determination using a minimum cost path approach

A method is introduced to automatically find the coronary axis based on two or more user-defined points, even in the presence of a severe stenosis. The coronary axis is determined by finding a minimum cost path (MCP) in a feature image in which the tubular-like structures are enhanced. The results of the proposed method were compared with manually drawn central axes to estimate the accuracy. In 32 3D TFE-EPI acquisitions of patients and volunteers, 14 right coronary arteries (RCAs), 15 left anterior descending arteries (LADs), and eight left circumflex arteries (LCXs) were manually tracked twice by two operators to determine a reference axis and to assess the inter- and intra-user variability. On average, the maximum distance to the reference axis, based on only two user-defined points, is less than 1.5 mm; the average distance is around 0.65 mm, which is less than the average in-plane resolution. The results of the method are comparable to those of the manual operators.

White and black blood volumetric angiographic filtering: ellipsoidal scale-space approach

Prefiltering is a critical step in three-dimensional (3-D) segmentation of the blood vessel and its display (see the recent book by Suri et al.). This paper presents a scale-space approach for filtering the white blood and black blood angiographic volumes and its implementation issues. The raw MR angiographic volume is first converted to isotropic volume followed by 3-D higher order separable Gaussian derivative convolution with known scales to generate edge volume. The edge volume is then run by the directional processor at each voxel where the eigenvalues of the 3-D ellipsoid are computed. The vessel score per voxel is then estimated based on these three eigenvalues which suppress the nonvasculature and background structures yielding the filtered volume. The filtered volume is ray-cast to generate the maximum intensity projection images for display. The performance of the system is evaluated by computing the mean, variance, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) images. The system is run over 20 patient studies from different areas of the body such as the brain, abdomen, kidney, knee, and ankle. The computer program takes around 150 s of processing time per study for a data size of 512 x 512 x 194, which includes the complete performance evaluation. We also compare our strategy with the recently published MR filtering algorithms by Alexander et al. and Sun et al.

Dynamic polarization of the microtubule cytoskeleton during CTL-mediated killing

Efficient unidirectional killing by cytotoxic T lymphocytes (CTL) requires translocation of the microtubule organizing center (MTOC) to the target cell contact site. Here we utilize modulated polarization microscopy and computerized 3D reconstruction of tubulin and LFA-1 immunofluorescence images to investigate how this is accomplished. The results show that the MTOC is drawn vectorially to the contact site by a microtubule sliding mechanism. Once the MTOC arrives at the contact site, it oscillates laterally. Microtubules loop through and anchor to a ring-shaped zone (pSMAC) defined by the dense clustering of LFA-1 at the target contact site. Microtubules that run straight between the MTOC and pSMAC and then turn sharply may indicate the action of a microtubule motor such as dynein.

Acquisition, segmentation and tracking of the cerebral vascular tree on 3D magnetic resonance angiography images

This paper presents a method for the detection, representation and visualisation of the cerebral vascular tree and its application to magnetic resonance angiography (MRA) images. The detection method is an iterative tracking of the vessel centreline with subvoxel accuracy and precise orientation estimation. This tracking algorithm deals with forks. Centrelines of the vessels are modelled by second-order B-spline. This method is used to obtain a high-level description of the whole vascular network. Applications to real angiographic data are presented. An MRA sequence has been designed, and a global segmentation of the whole vascular tree is realised in three steps. Applications of this work are accurate 3D representation of the vessel centreline and of the vascular tree, and visualisation. The tracking process is also successfully applied to 3D contrast enhanced MR digital subtracted angiography (3D-CE-MRA) of the inferior member vessels. In addition, detection of artery stenosis for routine clinical use is possible due to the high precision of the tracking algorithm.

CURVES: curve evolution for vessel segmentation

The vasculature is of utmost importance in neurosurgery. Direct visualization of images acquired with current imaging modalities, however, cannot provide a spatial representation of small vessels. These vessels, and their branches which show considerable variations, are most important in planning and performing neurosurgical procedures. In planning they provide information on where the lesion draws its blood supply and where it drains. During surgery the vessels serve as landmarks and guidelines to the lesion. The more minute the information is, the more precise the navigation and localization of computer guided procedures. Beyond neurosurgery and neurological study, vascular information is also crucial in cardiovascular surgery, diagnosis, and research. This paper addresses the problem of automatic segmentation of complicated curvilinear structures in three-dimensional imagery, with the primary application of segmenting vasculature in magnetic resonance angiography (MRA) images. The method presented is based on recent curve and surface evolution work in the computer vision community which models the object boundary as a manifold that evolves iteratively to minimize an energy criterion. This energy criterion is based both on intensity values in the image and on local smoothness properties of the object boundary, which is the vessel wall in this application. In particular, the method handles curves evolving in 3D, in contrast with previous work that has dealt with curves in 2D and surfaces in 3D. Results are presented on cerebral and aortic MRA data as well as lung computed tomography (CT) data.

MR angiographic investigation of transient focal cerebral ischemia in rat

Contrast agent free time-of-flight magnetic resonance angiography (TOF-MRA) was applied to the intraluminal thread occlusion model of experimental stroke in rat. It was combined with perfusion- and diffusion-weighted imaging (PWI and DWI) sequences to correlate occlusion and reopening of the middle cerebral artery with alterations in these well-established magnetic resonance sequences. Since TOF-MRA can be repeated without limitations, the time course of vascular patency is demonstrated during an experimental period of up to 8 h (2 h control, 1 h ischemia, 3-6 h reperfusion). With an acquisition time of 10 min, TOF-MRA proved to be suitable to analyze the vascular state of occlusion and reperfusion repetitively in longitudinal studies. Spatial resolution was sufficient to observe neurovascular structural details. In eight out of 10 animals complete vessel occlusion by the intraluminal thread could be validated by an entirely extinguished signal of the ipsilateral middle cerebral artery (MCA) in the angiograms. This was in accordance with a perfusion deficit in the MCA vascular territory detected by PWI (reduction to 30.4 +/- 7.4% relative to contralateral side) and a disturbance of water ion homeostasis monitored by DWI in this area. One animal showed a delayed occlusion after 30 min of MCA occlusion, in another animal vessel occlusion failed. In seven out of the eight successful occlusion experiments there was immediate reperfusion after withdrawal of the thread. One animal showed a delayed reperfusion after suture retraction. Remarkable hemispheric differences in vascular branching of the MCA could be recognized in three out of 10 animals. In conclusion, TOF-MRA is considered a helpful method to survey even in small laboratory animals the correct time course of vascular occlusion and reopening in experimental ischemia, and provides complementary information to the tissue perfusion status monitored by PWI and the ischemic lesion territory detected by DWI.

Three-dimensional adaptive filtering in magnetic resonance angiography

In order to enhance 3D image data from magnetic resonance angiography (MRA), a novel method based on the theory of multidimensional adaptive filtering has been developed. The purpose of the technique is to suppress image noise while enhancing important structures. The method is based on local structure estimation using six 3D orientation selective filters, followed by an adaptive filtering step controlled by the local structure information. The complete filtering procedure requires approximately 3 minutes of computational time on a standard workstation for a 256 x 256 x 64 data set. The method has been evaluated using a mathematical vessel model and in vivo MRA data (both phase contrast and time of flight (TOF)). 3D adaptive filtering results in a better delineation of small blood vessels and efficiently reduces the high-frequency noise. Depending on the data acquisition and the original data type, contrast-to-noise ratio (CNR) improvements of up to 179% (8.9 dB) were observed. 3D adaptive filtering may provide an alternative to prolonging the scan time or using contrast agents in MRA when the CNR is low.

Symbolic description of intracerebral vessels segmented from magnetic resonance angiograms and evaluation by comparison with X-ray angiograms

We describe and evaluate methods that create detailed vessel trees by linking vessels that have been segmented from magnetic resonance angiograms (MRA). The tree-definition process can automatically exclude erroneous vessel segmentations. The parent-child connectivity information provided by our vessel trees is important to both surgical planning and to guidance of endovascular procedures. We evaluated the branch connection accuracy of our 3D vessel trees by asking two neuroradiologists to evaluate 140 parent-child connections comprising seven vascular trees against 17 digital subtraction angiography (DSA) views. Each reviewer rated each connection as (1) Correct, (2) Incorrect, (3) Partially correct (a minor error without clinical significance), or (4) Indeterminate. Analysis was summarized for each evaluator by calculating 95% confidence intervals for both the proportion completely correct and the proportion clinically acceptable (completely or partially correct). In order to protect the overall Type I error rate, alpha-splitting was done using a top down strategy. We additionally evaluated segmentation completeness by examining each slice in 11 MRA datasets in order to determine unlabeled vessels identifiable in cross-section following segmentation. Results indicate that only one vascular parent-child connection was judged incorrect by both reviewers. MRA segmentations appeared complete within MRA resolution limits. We conclude that our methods permit creation of detailed vascular trees from segmented 3D image data. We review the literature and compare other approaches to our own. We provide examples of clinically useful visualizations enabled by our methodology and taken from a visualization program now in clinical use.

Fast detection and characterization of vessels in very large 3-D data sets using geometrical moments

An improved and very fast algorithm dealing with the extraction of vessels in three-dimensional imaging is described. The approach is based on geometrical moments and a local cylindrical approximation. A robust estimation of vessel and background intensity levels, position, orientation, and diameter of the vessels with adaptive control of key parameters, is provided during vessel tracking. Experimental results are presented for lower limb arteries in multidetector computed tomography scanner.

Computer-assisted visualization of arteriovenous malformations on the home personal computer

OBJECTIVE

Arteriovenous malformations (AVMs) are difficult lesions to treat, partly because it is difficult to formulate a three-dimensional mental image of the nidus and its supplying arteries, draining veins, and arteries of passage. Our purpose is to develop personal computer software that allows better visualization of complex, three-dimensional, connected vascular anatomy for surgical planning.

METHODS

Vessels are defined from magnetic resonance angiograms and are symbolically linked to form vascular trees. The nidus of the AVM is also defined by magnetic resonance angiography. These representations of the nidus and vasculature can be viewed together in a software program that allows the user to color-code groups of vessels or to selectively turn connected groups of vessels "off" to avoid obscuring the part of the image that the user wants to observe. Structures can be viewed from any angle. The vessels can also be shown intersecting any magnetic resonance angiogram slice or superimposed upon digital subtraction angiograms obtained from the same patient.

RESULTS

We report results from two patients with AVMs in which our representations were compared with the findings during surgery. Our three-dimensional vascular trees correctly depicted the relationship of the nidus to feeding vessels in three dimensions. We show findings in an additional, unoperated patient for whom vessel trees were created from three-dimensional digital subtraction angiography data and compared with a volume rendering of the original data set.

CONCLUSION

Computer-assisted, three-dimensional visualizations of complex vascular anatomy can be helpful in planning the surgical excision of AVMs. Software programs that produce these images can provide important information that is difficult to obtain by traditional techniques. This imaging method is also applicable to guidance of endovascular procedures and removal of complex tumors.

Model-based quantitation of 3-D magnetic resonance angiographic images

Quantification of the degree of stenosis or vessel dimensions are important for diagnosis of vascular diseases and planning vascular interventions. Although diagnosis from three-dimensional (3-D) magnetic resonance angiograms (MRA's) is mainly performed on two-dimensional (2-D) maximum intensity projections, automated quantification of vascular segments directly from the 3-D dataset is desirable to provide accurate and objective measurements of the 3-D anatomy. A model-based method for quantitative 3-D MRA is proposed. Linear vessel segments are modeled with a central vessel axis curve coupled to a vessel wall surface. A novel image feature to guide the deformation of the central vessel axis is introduced. Subsequently, concepts of deformable models are combined with knowledge of the physics of the acquisition technique to accurately segment the vessel wall and compute the vessel diameter and other geometrical properties. The method is illustrated and validated on a carotid bifurcation phantom, with ground truth and medical experts as comparisons. Also, results on 3-D time-of-flight (TOF) MRA images of the carotids are shown. The approach is a promising technique to assess several geometrical vascular parameters directly on the source 3-D images, providing an objective mechanism for stenosis grading.

Local maximum intensity projection (LMIP): a new rendering method for vascular visualization

PURPOSE

The purpose of our study was to demonstrate a new visualization method (local maximum intensity projection; LMIP) that can clearly depict densitometric as well as geometric information in vascular visualization from 3D data such as obtained from MR and CT angiography.

METHOD

LMIP is an extended version of maximum intensity projection (MIP). However, LMIP differs from MIP in that the latter method selects the maximum value along an optical ray, whereas LMIP selects the first local maximum value encountered that is larger than a preselected threshold value along an optical ray from a viewpoint in the viewing direction.

RESULTS AND CONCLUSION

Examples are presented in which LMIP is used to visualize renal vessels from CT angiography data and cerebral vessels in the vicinity of an aneurysm from phase-contrast MR angiography data. We demonstrate that LMIP can clearly depict geometric information, as shaded surface display does, and densitometric information, as is done by volume rendering, in a straightforward and objective manner.