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infoRAD |
1 From the Centre de Recherche et d’Applications en Traitement de l’Image et du Signal, Institut National des Sciences Appliquées de Lyon, Bâtiment Blaise Pascal, 7 rue Capelle, F-69621 Villeurbanne, France. Presented as an infoRAD exhibit at the 2000 RSNA scientific assembly. Received March 1, 2001; revision requested May 1; final revision received November 8; accepted November 19. Supported by Carena S.A., Région Rhône-Alpes (ADéMo Project), and the French National Center for Scientific Research (GdR PRC ISIS). Address correspondence to M.H.H. (e-mail: marcela.hernandez@creatis.insa-lyon.fr).
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 Top Abstract IntroductionTools for Postprocessing of...Our Contribution: MARACASConclusionsReferences |
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© RSNA, 2002
Index Terms: Arteries, stenosis or obstruction, 9*.7212 • Computers, diagnostic aid • Images, analysis • Magnetic resonance (MR), image processing Magnetic resonance (MR), three-dimensional • Magnetic resonance (MR), vascular studies, 9*.721
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Introduction |
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TopAbstractIntroductionTools for Postprocessing of...Our Contribution: MARACASConclusionsReferences |
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Conventional or digital subtraction angiography (DSA), computed tomography (CT), and magnetic resonance (MR) angiography are the three most commonly used imaging techniques in diagnosis, treatment planning, and follow-up of this pathologic condition. A growing body of literature supports the accuracy of MR angiography compared with that of DSA (1), which remains the standard of reference for diagnosis of vascular diseases (2,3). MR angiography provides three-dimensional (3D) anatomic images of blood vessels. Moreover, it is noninvasive. In cases where DSA is too risky (excessive x-ray exposure for both the patient and the radiologist, patients with extensive vascular disease, patients with a risk of decreased renal function, or patients with difficult vascular access) or where two-dimensional (2D) information is not sufficient, MR angiography appears to be an effective alternative (4,5).
Despite the 3D nature of MR angiography, tools for diagnosis and treatment planning are commonly 2D. Most visualization consoles used in clinical practice for MR angiographic evaluation of vascular stenosis provide (a) simultaneous display of 2D maximum intensity projection (MIP) images and axial sections and (b) interactive measurement tools for "manual" evaluation of the diameters of pathologic and healthy vessels. Unfortunately, even if measurements are performed with precise interactive tools, user intervention may lead to subjective stenosis estimation associated with significant interobserver variability (6). First, selection of the reference healthy segment and of the tightest segment of a vessel is user dependent. Second, manual windowing may enlarge or narrow the apparent vessel diameter, resulting in possible over- or underestimation of stenosis severity. Furthermore, interactive evaluation of a stenosis on MIP images requires multiple projections. Indeed, it is very important to choose the projection with the shortest diameter, since elliptical, semilunar, or mild stenosis may be underestimated (7). This choice is also user dependent. A subjective estimate of stenosis severity is not accurate and reproducible enough to be considered a reliable criterion for determining the indications for pharmacologic, intravascular, or surgical treatment and for allowing precise postoperative follow-up with MR angiography. To achieve precise evaluation of stenosis severity and of the entire set of vessel parameters (eg, length, different diameters, and section areas of the diseased segment), the MR angiographic analysis should take full advantage of its intrinsic 3D information.
To meet these clinical requirements, a computer-assisted analysis system needs to be tailored for this specific application. We developed a postprocessing program called Magnetic Resonance Angiography Computer Assisted Analysis (MARACAS). MARACAS was designed for visualization and analysis of blood vessels based on dynamic contrast material–enhanced subtracted 3D MR angiograms and for automatic quantification of arterial stenosis. The originality of our work resides in the methods of automatic extraction of the vessel axis and boundaries. These methods are fast and require minimal user interaction at the initialization step.
The article is organized as follows: First, the computer tools required for postprocessing of MR angiograms are discussed. Second, the characteristics of MARACAS as a postprocessing system are described: how it works and what image processing algorithms are implemented in the software for vessel tracking and quantification. Qualitative and quantitative results obtained with MARACAS are then presented, followed by a discussion and conclusions.
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Tools for Postprocessing of MR Angiograms |
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Initially, DICOM functions were limited to the exchange of data between proprietary workstations. Recently, some DICOM-compliant applications have become available on personal computers (PCs), and their easier diffusion allows medical institutions or laboratories to perform specific postprocessing tasks (measurements, multiplanar reformation [MPR], MIP, archiving) with a lower investment. Some of these applications can provide service class provider–service class user functions (13).
Image Visualization
Although exploration of the native sections that were acquired by the imaging unit gives access to precise local information (eg, vessel diameter, vessel area), mental reconstruction of the global morphology and spatial relationships is difficult. Moreover, this local information may not be exploitable when the sections are not perpendicular to the vessel of interest (14). Several postprocessing techniques are available for improved rendering of the overall morphology or the spatial relationships of the anatomic structures. Their purpose is to allow the user to simultaneously view the MR angiography data sets in different display formats such as MPR, MIP, surface rendering, volume rendering, and virtual intraluminal endoscopy. In this section, the principles of these techniques are briefly explained. Their main advantages and disadvantages are also discussed.
Multiplanar Reformation.— MPR involves display of planar sections through the data volume. Some systems extract the sections only in the planes perpendicular to the native sections. This process allows the user to "slide" through the volume along the axial, coronal, or sagittal orientation. Others allow reconstruction of image planes according to any orientation in the space, making it possible to generate sections following an irregular orientation (curvilinear reformation). MPR is based on two operations: interpolation and resampling, so as to assign intensities to voxels located "somewhere between" the actual data points. With MPR images, the radiologist can simultaneously visualize the arterial lumen and the adjacent structures in a localized region and interactively view features that are not visible in native sections (15). However, MPR requires a trained operator, who mentally reconstructs a 3D picture from 2D sections.
Maximum Intensity Projection.— To obtain the MIP, parallel rays are cast through the volume from each pixel of the projection image plane. Each pixel is assigned the maximum intensity encountered along the corresponding ray. MIP images have an aspect similar to that of conventional angiograms and are widely used in MR angiography analysis. They are often easier to interpret than native sections. However, this technique has some disadvantages. First, it tends to misrepresent anatomic spatial relationships because depth information is not displayed (16). Eccentrically located stenoses may remain undetected, and superimposition of structures may erroneously simulate a stenosis (17). Second, it tends to increase the mean intensity of the background (18,19). Consequently, some low-intensity vessels visible in individual sections may be partially or completely lost in the MIP image (20). To compensate for the loss of depth information in MIP images, postprocessing systems provide such solutions as (a) limiting the projection to a subvolume of interest, instead of projecting the whole volume, and (b) making the projection angle vary (interactive rotation).
Surface Rendering.— Surface rendering uses the boundary voxels between the object to be displayed and the background to create a 3D surface. Hence, it requires a preliminary segmentation. The segmentation methods capable of precisely delineating the vascular structures and separating them from other tissues belong to the field of vascular analysis and are still under investigation. However, for an approximate visualization, the segmentation may be done by simple thresholding. Surface rendering is also called shaded surface display (SSD) because the intensity of each pixel in an SSD image is calculated by using the local orientation of the surface with respect to a virtual lighting source. In contrast to MPR and MIP, SSD better renders the 3D aspect of MR angiography data. The surface representation accurately captures and represents the positions and shapes of the relevant vascular structures and can be visualized and manipulated interactively. However, it requires a user intervention to select the appropriate thresholding value to extract the surface of the vessel of interest. This can sometimes be quite difficult, especially if boundaries are not well defined (eg, noisy or low-contrast data). Threshold-based SSD images should not be used for quantification, since vessel diameters are threshold dependent and thus can lead to under- or overestimation of the stenosis. In consequence, surface-rendered images should always be displayed simultaneously with at least one complementary visualization mode to assess the appropriateness of the threshold choice (16).
Volume Rendering.— Volume rendering displays all of the 3D data at once. It works directly on the voxel intensities and creates translucent renderings of the full data set: objects with high signal intensity are opaque and objects with low signal intensity are transparent. As commonly implemented, volume rendering generates an image in the following way: First, it computes color and partial opacity for each voxel. Then, it blends contributions made by voxels projecting to the same pixel on the picture plane (along a line from the viewer’s eye through the data set). In most cases, the user manually sets color, opacity, and brightness corresponding to different voxel intensities. Some systems use standard presets (21). However, these presets are much more difficult to establish for MR angiograms than for CT images. The main advantage of this technique is the ability to generate images without explicitly defining surface geometry. It reveals internal structures that would normally be hidden or omitted when surface rendering techniques are used. Thus, volume rendering appears very interesting for the study of arterial plaque. However, its use requires a powerful computer configuration, and it has been demonstrated that the cost-to-performance ratios are not satisfactory (16).
Virtual Intraluminal Endoscopy.— Virtual intraluminal endoscopy is a recently developed technique for assessing the inside of the vascular wall. It combines the features of endoscopic viewing and cross-sectional volumetric imaging and involves generation of a sequence of perspective views calculated from points (flight path) within the vascular lumen. These views can be computed by using both surface rendering and volume rendering algorithms. In the most recent systems, the flight path is automatically calculated. This process is based on a preliminary extraction of the vessel centerline, which constitutes a high-level method of image processing.
Vascular Analysis
The purpose of the vascular analysis is to allow the clinician to perform a quantitative assessment of the vessel morphology to decide on the appropriate treatment (surgical or pharmacologic) according to the degree of stenosis and to monitor the progress of the disease. It requires different vessel measurements. On the one hand, intraluminal diameters and cross-sectional areas are needed for quantifying the degree of stenosis. Traditionally, only diameters were used for stenosis estimation. The standard equation for calculating the percentage of stenosis is as follows:
Most postprocessing systems provide interactive tools for manually performing these measurements. This approach considerably increases the accuracy of stenosis quantification compared with a purely visual evaluation. However, manual tracing of the lumen centerline and delineation of vessel boundaries are time-consuming tasks and are susceptible to variability between operators. These major drawbacks have motivated the development of new techniques for automatic quantification of vascular morphology.
In terms of image processing, the first step necessary for performing quantitative analysis of a vessel is to separate it from the surrounding structures to study its shape accurately. This procedure is called segmentation. Two main approaches to vessel segmentation can be distinguished in the literature. The first one relies on purely photometric criteria, mainly thresholding and region-growing techniques (23–26). Its major advantage is its generally simple implementation. However, a further modeling step is necessary to extract meaningful measurements from images segmented thus. The second approach exploits the geometric specificity of vessels, in particular the notions of orientation and tubular shape. Most of these approaches involve vessel tracking (27–31) and use (often implicitly) a generalized cylinder model, that is, an association between an axis (centerline) and a surface (vessel wall) (32,33). Consequently, the segmentation process involves two tasks: centerline extraction and vessel contour detection in the planes usually perpendicular to the axis. This procedure results in a stack of 2D contours along the vessel, allowing quantitative cross-sectional measurements and visualization by means of triangulation-based surface rendering. Other recent approaches use 3D models of the surface (34,35).
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Our Contribution: MARACAS |
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TopAbstractIntroductionTools for Postprocessing of...Our Contribution: MARACASConclusionsReferences |
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In response to the specific medical need for MR angiography–targeted image analysis software, we developed a postprocessing program called MARACAS. The main features and contribution of this software are described in this section.
What Is MARACAS?
MARACAS is an interactive software for visualization and analysis of blood vessels in 3D MR angiography that provides automatic quantification of arterial stenosis. We emphasized three main aspects in the development of MARACAS: reliability of the results, minimization of postprocessing time, and portability of the software. Our main contribution resides in a novel method of vessel segmentation and quantification, which is fast enough for clinical application and is implemented in a graphical environment portable to multiple platforms. Compared with the above-mentioned postprocessing workstations, MARACAS is a small system that does not require a dedicated configuration (a simple PC is sufficient) and can be installed in almost every computer (PC or Unix workstation). The only additional requirement is the memory size: for full-speed execution, the use of 256 Mbytes of random-access memory is recommended. Installation of the software requires 17 Mbytes of disk space.
MARACAS includes almost all of the functions described in the previous section. First, as regards data handling, it supports the reading of DICOM image files previously transferred from the imaging device system. At present, we use the software eFilm (eFilm Medical) (13) as a DICOM client (service class user) to perform this transfer, that is, to import external DICOM files, to sort the incoming DICOM data by studies and series, and to hierarchically store them on the local hard disk. Second, as regards image visualization, MARACAS includes the following display modes: MPR, MIP, surface rendering (Fig 2), and virtual endoscopy (Fig 3). Third, to achieve vascular analysis, MARACAS provides both interactive tools for manual measurements and an automatic tool for vessel segmentation and stenosis quantification (42). There are three interactive tools: probe, statistics, and profile. These are to be used on the 2D images (sections or MIP images). The probe displays the intensity of the pixel selected by the cursor. The statistics tool displays the mean intensity and the standard deviation of a hand-drawn rectangular region. The profile tool displays the length and the intensity profile of a straight-line segment traced by the user (Fig 4). This tool can also display an estimate of the vessel width: If one supposes that the line is perpendicular to the vessel, the vessel width is calculated from the intensity profile as the width at middle height (full width at half maximum) of the highest peak of intensity.
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How Does It Work?
The vessel extraction and quantification process is as follows (Fig 5): After manual determination of a volume of interest and of a point on the vessel surface, an initial point in the vessel is calculated and the vessel tracking process is started. First, extraction of the central axis of the vessel is performed with an expansible skeleton method. Contours are then detected in the planes locally orthogonal to the centerline by using an improved active contour. Finally, measurements based on the resulting contours allow calculation of the stenosis parameters.
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Selection of a Starting Point.— The initialization is performed in the surface-rendered image of the volume of interest. The user defines the target segment (from which the centerline will be extracted) by marking one point on the vessel surface (the pick point). To extract the vessel axis, the algorithm needs a point within the vessel, which is automatically computed based on the position of the pick point (Fig 6).
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Extraction of the Vessel Axis.— Vessel axis extraction is achieved with an expansible skeleton method. It is based on a tracking strategy, which begins from the computed starting point within the vessel and then iteratively estimates the subsequent axis points (at each iteration, a new point is added to the model). Point generation is a two-step procedure: First, a prediction of the new point position is obtained, based on the local vessel orientation at the current point. This position is then corrected under the influence of image forces and shape constraints.
The position of the next point is predicted by applying a "constant velocity" displacement of the current point along the local vessel orientation. The local vessel orientation is estimated by inertia moment minimization for a small volume (cell) centered on the current point (44). The size of the cell is automatically deduced from the vessel width estimated at the initialization step. The inertia-based approach provides a convenient way to automate axis extraction for most vascular structures. Moreover, it offers good noise robustness and gives satisfactory results even for small vessels. However, it has been shown that the inertia criterion alone may lead to an absurd axis in the case of a bifurcation (Fig 7) or significant curvature (Fig 8) (28). Hence, the predicted position of the new point sometimes needs to be corrected.
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Manual corrections of the extracted vessel axis are allowed. This is particularly useful in the case of bifurcations. Although bifurcations are not incorporated into our local vessel model (a tubular structure), MARACAS succeeds in extracting the vessel centerline even at the branching points. However, it tracks only one branch at a time. Consequently, if the automatically extracted branch is not the desired one, the user can interactively force the tracking of another branch (Fig 9).
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Extraction of Vessel Contours in the Planes Orthogonal to the Vessel Axis.— MARACAS automatically generates image planes orthogonal to the extracted vessel centerline (Figs 10, 11). For this purpose, the centerline is interpolated by using a B-spline curve and the image volume is resectioned. MARACAS displays the reformatted 2D image of the orthogonal plane in a separate window, where it can be interactively panned and zoomed. The location of the plane is displayed together with the surface-rendered image of the vessel. The user can slide the plane along the axis, thus obtaining a curvilinear MPR image along the automatically extracted centerline.
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Qualitative Assessment.— Because the precision of the vessel axis extraction method is difficult to estimate quantitatively, we carried out a qualitative evaluation. It was performed on data from 23 patients with stenoses located in different arteries: the carotid arteries (Fig 18), renal arteries, and arteries of the lower limbs (Fig 19). We also performed a visual evaluation of the extracted contours. The algorithm was tested on numerous contours with different shapes and sizes (Fig 20): circular, elliptical, semilunar, amorphous, and so on.
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s is the standard deviation of the measured area. The degree of stenosis was computed as the ratio of the pathologic cross-sectional area Smin to Sn (Fig 16). The stenosis severity estimates were quantitatively evaluated by comparing them with the true (theoretical) stenosis values of the phantoms. A summary of the quantitative results for these data is given in Figure 21 and the Table.
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< 3.5%, CVmax < 7%) indicate good stability of the vessel contour extraction results. The fluctuations can be attributed to the partial volume effect. Indeed, let us consider the case of phantom B, which had the greatest coefficient of variation. The cross-sectional area of a normal (nonstenosed) section (diameter = 6 mm) is 28.27 mm2. The voxel size is 0.78 x 0.78 x 1.0 mm. Therefore, each missing or excessive pixel on the extracted surface represents an error of 2.15%. This outcome shows that our results contain no more than 3 misclassified pixels, which is a correct value if one takes into account the partial volume effect. Moreover, the absolute errors in stenosis quantification are not more significant than the variations and could be explained by the same reasons.
Discussion
Before discussing the accuracy of the extraction algorithms for the vessel axis and boundaries, let us note the efficiency of their implementation in terms of processing time. The processing time depends on the number of reconstructed cross sections. Indeed, computational cost is mainly determined by the extraction of image planes orthogonal to the axis, whereas extraction of the axis and of the boundaries is almost instantaneous. Typically, 1.4 seconds is needed to reconstruct one cross section on an 800-MHz PC. Consider a renal artery resectioned every 1 mm, which is a reasonably fine quantification step for typical MR angiographic image resolutions. The entire extraction and quantification process in this case takes 1 minute on average.
Precision of the Vessel Axis Extraction Method.— According to a visual evaluation, the results were satisfactory for large vessels as well as for small and low-intensity ones. The vessel centerline correctly tracks the vessel path and lies inside it even in the presence of severe stenosis. However, one can argue that in some cases the axis should lie outside the actual lumen. Indeed, in MR angiography, only the vessel lumen is imaged. Therefore, the vessel axis extracted by our algorithm is in fact the centerline of the circulating blood lumen. This is not a problem in analysis of healthy vessels because the vessel centerline coincides with the lumen centerline. However, in the case of vessels with a severe or extremely eccentric stenosis, a question remains: What should be detected as the vessel centerline to generate the best perpendicular planes? Two answers are possible: The vessel axis can be centered in the lumen or it can be equidistant from the true vessel surface. We will discuss the pros and cons of each approach.
A vessel axis centered in the lumen indicates the true path of the blood flow. It can be used for guiding a virtual endoscopic exploration of the vessel. However, such an axis demonstrates significant curvatures in the regions adjacent to the stenosed segment. Consequently, cross sections in these regions may be erroneous (Fig 22a): They are not perpendicular to the vessel but oblique, and the corresponding lumen shape is elliptical. With a vessel axis equidistant from the true vessel surface (Fig 22b), the problem of oblique cross sections can be avoided. Such an axis also seems more appropriate for length measurements in preoperative planning of an endovascular prosthesis. However, it cannot be used for guiding a virtual endoscopic exploration of the vessel. In addition, it cannot be used to initialize the starting point for our contour extraction algorithm in the stenosed region because the point may be outside the vessel.
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Accuracy of the Vessel Contour Detection Algorithm.— We used two criteria to assess the precision of the vessel contour extraction method. The first was the final shape of the active contour used to extract the vessel boundary. Preliminary results on phantom images and on large vessels from patient data show precise estimation of the contour position. It is very difficult to judge the shape of the contours in the case of vessels as thin as 2 or 3 voxels. The second criterion was the area measurement, which can be compared with known theoretical values on images of phantoms. The reported quantitative results obtained in phantoms show the precision of our method.
The new numeric implementation of the active contour used by MARACAS to extract the vessel boundaries offers some advantages compared with the classic "snake" model. The contour deformation is independent of the contour size, and the final outline is not determined by the initialization. This allows (a) initialization with a single pixel and (b) growth in each direction with retention of a smooth outline.
As mentioned earlier, the main disadvantage of the model is that it cannot be initialized on the vessel boundary or very close to it. This happens in the case of small cross sections, in which the vessel centerline may touch the boundary. To overcome this problem, boundary detection should probably not be limited to 2D contours in the planes locally orthogonal to the axis but should be carried out in a 3D neighborhood.
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Conclusions |
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TopAbstractIntroductionTools for Postprocessing of...Our Contribution: MARACASConclusionsReferences |
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Additional work is necessary to improve the reliability of the contour detection in some situations with complex geometry, such as bifurcations or severe eccentric stenosis. A more extensive validation study is being performed, which incorporates additional phantoms as well as numerous images from patients. Use of the methods on CT angiographic data sets will also be evaluated.
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Acknowledgments |
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Footnotes |
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Abbreviations: DICOM = Digital Imaging and Communications in Medicine, MARACAS = Magnetic Resonance Angiography Computer Assisted Analysis, MIP = maximum intensity projection, MPR = multiplanar reformation, PC = personal computer, 2D = two-dimensional, 3D = three-dimensional
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TopAbstractIntroductionTools for Postprocessing of...Our Contribution: MARACASConclusionsReferences |
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