A viewpoint determination system for stenosis diagnosis and quantification in coronary angiographic image acquisition

An adaptive optimal viewing angle determination algorithm for TEVAR operation

BACKGROUND

The determination of the right x-ray angiography viewing angle is an important issue during the treatment of thoracic endovascular aortic repair (TEVAR). An inaccurate projection angle (manually determined today by the physicians according to their personal experience) may affect the placement of the stent and cause vascular occlusion or endoleak.

METHODS

Based on the acquisition of a computed tomography angiography (CTA) image before TEVAR, an adaptive optimization algorithm is proposed to determine the optimal viewing angle of the angiogram automatically. This optimal view aims at avoiding any overlapping between the left common carotid artery and the left subclavian artery. Moreover, the proposed optimal procedure exploits the patient-specific morphology to adaptively reduce the potential foreshortening effect.

RESULTS

Experimental results conducted on thirty-five patients demonstrate that the optimal angiographic viewing angle based on the proposed method has no significant difference when compared with the expert practice (p = 0.0678).

CONCLUSION

We propose a method that utilizes the CTA image acquired before TEVAR to automatically calculate the optimal C-arm angle. This method has the potential to assist surgeons during their interventional procedure by providing a shorter procedure time, less radiation exposure, and less contrast injection.

Prior-Free Respiratory Motion Estimation in Rotational Angiography

Rotational coronary angiography using C-arm angiography systems enables intra-procedural 3-D imaging that is considered beneficial for diagnostic assessment and interventional guidance. Despite previous efforts, rotational angiography was not yet successfully established in clinical practice for coronary artery procedures due to challenges associated with substantial intra-scan respiratory and cardiac motion. While gating handles cardiac motion during reconstruction, respiratory motion requires compensation. State-of-the-art algorithms rely on 3-D / 2-D registration that requires an uncompensated reconstruction of sufficient quality. To overcome this limitation, we investigate two prior-free respiratory motion estimation methods based on the optimization of: 1) epipolar consistency conditions (ECCs) and 2) a task-based auto-focus measure (AFM). The methods assess redundancies in projection images or impose favorable properties of 3-D space, respectively, and are used to estimate the respiratory motion of the coronary arteries within rotational angiograms. We evaluate our algorithms on the publicly available CAVAREV benchmark and on clinical data. We quantify reductions in error due to respiratory motion compensation using a dedicated reconstruction domain metric. Moreover, we study the improvements in image quality when using an analytic and a novel temporal total variation regularized algebraic reconstruction algorithm. We observed substantial improvement in all figures of merit compared with the uncompensated case. Improvements in image quality presented as a reduction of double edges, blurring, and noise. Benefits of the proposed corrections were notable even in cases suffering little corruption from respiratory motion, translating to an improvement in the vessel sharpness of (6.08 ± 4.46)% and (14.7 ± 8.80)% when the ECC-based and the AFM-based compensation were applied. On the CAVAREV data, our motion compensation approach exhibits an improvement of (27.6 ± 7.5)% and (97.0 ± 17.7)% when the ECC and AFM were used, respectively. At the time of writing, our method based on AFM is leading the CAVAREV scoreboard. Both motion estimation strategies are purely image-based and accurately estimate the displacements of the coronary arteries due to respiration. While current evidence suggests the superior performance of AFM, future work will further investigate the use of ECC in the context of angiography as they solely rely on geometric calibration and projection-domain images.

Viewpoint planning for quantitative coronary angiography

PURPOSE

In coronary angiography, the condition of myocardial blood supply is assessed by analyzing 2-D X-ray projections of contrasted coronary arteries. This is done using a flexible C-arm system. Due to the X-ray immanent dimensionality reduction projecting the 3-D scene onto a 2-D image, the viewpoint is critical to guarantee an appropriate view onto the affected artery and, thus, enable reliable diagnosis. In this work, we introduce an algorithm computing optimal viewpoints for the assessment of coronary arteries without the need for 3-D models.

METHODS

We introduce the concept of optimal viewpoint planning solely based on a single angiographic X-ray image. The subsequent viewpoint is computed such that it is rotated precisely around a vessel, while minimizing foreshortening.

RESULTS

Our algorithm reduces foreshortening substantially compared to the input view and completely eliminates it for [Formula: see text] rotations. Rotations around isocentered foreshortening-free vessels passing the isocenter are exact. The precision, however, decreases when the vessel is off-centered or foreshortened. We evaluate worst-case boundaries, providing insight in the maximal inaccuracies to be expected. This can be utilized to design viewpoints guaranteeing desired requirements, e.g., a true rotation around the vessel of at minimum [Formula: see text]. In addition, a phantom study is performed investigating the impact of input views to 3-D quantitative coronary angiography (QCA).

CONCLUSION

We introduce an algorithm for optimal viewpoint planning from a single angiographic X-ray image. The quality of the second viewpoint-i.e., vessel foreshortening and true rotation around vessel-depends on the first viewpoint selected by the physician; however, our computed viewpoint is guaranteed to reduce the initial foreshortening. Our novel approach uses fluoroscopy images only and, thus, seamlessly integrates with the current clinical workflow for coronary assessment. In addition, it can be implemented in the QCA workflow without increasing user interaction, making vessel-shape reconstruction more stable by standardizing viewpoints.

Automatic segmentation and measurement of vasculature in retinal fundus images using probabilistic formulation

The automatic analysis of retinal blood vessels plays an important role in the computer-aided diagnosis. In this paper, we introduce a probabilistic tracking-based method for automatic vessel segmentation in retinal images. We take into account vessel edge detection on the whole retinal image and handle different vessel structures. During the tracking process, a Bayesian method with maximum a posteriori (MAP) as criterion is used to detect vessel edge points. Experimental evaluations of the tracking algorithm are performed on real retinal images from three publicly available databases: STARE (Hoover et al., 2000), DRIVE (Staal et al., 2004), and REVIEW (Al-Diri et al., 2008 and 2009). We got high accuracy in vessel segmentation, width measurements, and vessel structure identification. The sensitivity and specificity on STARE are 0.7248 and 0.9666, respectively. On DRIVE, the sensitivity is 0.6522 and the specificity is up to 0.9710.

Global optimization of optimal angiographic viewing angles for coronary arteries with multiple segments

For angiographic images, different imaging angles may lead to different degrees of foreshortening and overlap, which hence considerably interferes with the angiogram based diagnosis of vascular diseases. In this study, a composite optimization method is proposed to determine the optimal viewing angle of angiograms, which can fully considerate stenosis and the interference caused due to multiple vascular segments. In order to obtain the optimal angle for segments at multiple branches' intersection, a hyper plane optimization method is proposed to separate the relative branches. The optimal angle for stenosis is designed by minimizing area of the projections of the vascular segments. The multiple segments and the stenosis optimization are integrated with the foreshortening and overlap, which can hence give a more objective viewing angle of the coronary arteries from angiographic images. Experimental results demonstrate that the proposed method is very effective and robust for optimizing the projection angle.

Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking

BACKGROUND

Segmentation of the coronary angiogram is important in computer-assisted artery motion analysis or reconstruction of 3D vascular structures from a single-plan or biplane angiographic system. Developing fully automated and accurate vessel segmentation algorithms is highly challenging, especially when extracting vascular structures with large variations in image intensities and noise, as well as with variable cross-sections or vascular lesions.

METHODS

This paper presents a novel tracking method for automatic segmentation of the coronary artery tree in X-ray angiographic images, based on probabilistic vessel tracking and fuzzy structure pattern inferring. The method is composed of two main steps: preprocessing and tracking. In preprocessing, multiscale Gabor filtering and Hessian matrix analysis were used to enhance and extract vessel features from the original angiographic image, leading to a vessel feature map as well as a vessel direction map. In tracking, a seed point was first automatically detected by analyzing the vessel feature map. Subsequently, two operators [e.g., a probabilistic tracking operator (PTO) and a vessel structure pattern detector (SPD)] worked together based on the detected seed point to extract vessel segments or branches one at a time. The local structure pattern was inferred by a multi-feature based fuzzy inferring function employed in the SPD. The identified structure pattern, such as crossing or bifurcation, was used to control the tracking process, for example, to keep tracking the current segment or start tracking a new one, depending on the detected pattern.

RESULTS

By appropriate integration of these advanced preprocessing and tracking steps, our tracking algorithm is able to extract both vessel axis lines and edge points, as well as measure the arterial diameters in various complicated cases. For example, it can walk across gaps along the longitudinal vessel direction, manage varying vessel curvatures, and adapt to varying vessel widths in situations with arterial stenoses and aneurysms.

CONCLUSIONS

Our algorithm performs well in terms of robustness, automation, adaptability, and applicability. In particular, the successful development of two novel operators, namely, PTO and SPD, ensures the performance of our algorithm in vessel tracking.

Sequential reconstruction of vessel skeletons from X-ray coronary angiographic sequences

X-ray coronary angiography (CAG) is one of widely used imaging modalities for diagnosis and interventional treatment of cardiovascular diseases. Dynamic CAG sequences acquired from several viewpoints record coronary arterial morphological information as well as dynamic performances. The aim of this work is to propose a semi-automatic method for sequentially reconstructing coronary arterial skeletons from a pair of CAG sequences covering one or several cardiac cycles acquired from different views based on snake model. The snake curve deforms directly in 3D through minimizing a predefined energy function and ultimately stops at the global optimum with the minimal energy, which is the desired 3D vessel skeleton. The energy function combines intrinsic properties of the curve and acquired image data with a priori knowledge of coronary arterial morphology and dynamics. Consequently, 2D extraction, 3D sequential reconstruction and tracking of coronary arterial skeletons are synchronously implemented. The main advantage of this method is that matching between a pair of angiographic projections in point-by-point manner is avoided and the reproducibility and accuracy are improved. Results are given for clinical image data of patients in order to validate the proposed method.

3D Axon structure extraction and analysis in confocal fluorescence microscopy images

The morphological properties of axons, such as their branching patterns and oriented structures, are of great interest for biologists in the study of the synaptic connectivity of neurons. In these studies, researchers use triple immunofluorescent confocal microscopy to record morphological changes of neuronal processes. Three-dimensional (3D) microscopy image analysis is then required to extract morphological features of the neuronal structures. In this article, we propose a highly automated 3D centerline extraction tool to assist in this task. For this project, the most difficult part is that some axons are overlapping such that the boundaries distinguishing them are barely visible. Our approach combines a 3D dynamic programming (DP) technique and marker-controlled watershed algorithm to solve this problem. The approach consists of tracking and updating along the navigation directions of multiple axons simultaneously. The experimental results show that the proposed method can rapidly and accurately extract multiple axon centerlines and can handle complicated axon structures such as cross-over sections and overlapping objects.

An improved algorithm for vessel centerline tracking in coronary angiograms

For automated visualization and quantification of artery diseases, the accurate determination of the arterial centerline is a prerequisite. Existing tracking-based approaches usually suffer from the inaccuracy, inflexion and discontinuity in the extracted centerlines, and they may even fail in complicated situations. In this paper, an improved algorithm for coronary arterial centerline extraction is proposed, which incorporates a new tracking direction updating scheme, a self-adaptive magnitude of linear extrapolation and a dynamic-size search window for matched filtering. A simulation study is conducted for the determination of the optimal weighting factor which is used to combine the geometrical topology information and intensity distribution information to obtain the proposed tracking direction. Synthetic and clinical examples, representing some difficult situations that may occur in coronary angiograms, are presented. Results show that the proposed algorithm outperforms the conventional methods. By adopting the proposed algorithm, centerlines are successfully extracted under these complicated situations, and with satisfactory accuracy.

Coronary extraction and stenosis quantification in X-ray angiographic imaging

This work describes a method to quantify stenosis in X-ray coronary angiography. Vascular edge extraction is first performed based on a deformable spline algorithm. It makes use of directional S-Gabor filters to build an external energy field that is then used in a snake optimisation scheme. A string matching technique is then applied to match the contour points and obtain a trace between the matched points. This trace allows then computing the vessel diameter and deriving quantitative stenosis measurements. Experimental results are presented on simulated data and real images.

Augmented vessels for quantitative analysis of vascular abnormalities and endovascular treatment planning

Endovascular treatment plays an important role in the minimally invasive treatment of patients with vascular diseases, a major cause of morbidity and mortality worldwide. Given a segmentation of an angiography, quantitative analysis of abnormal structures can aid radiologists in choosing appropriate treatments and apparatuses. However, effective quantitation is only attainable if the abnormalities are identified from the vasculature. To achieve this, a novel method is developed, which works on the simpler shape of normal vessels to identify different vascular abnormalities (viz. stenotic atherosclerotic plaque, and saccular and fusiform aneurysmal lumens) in an indirect fashion, instead of directly manipulating the complex-shaped abnormalities. The proposed method has been tested on three synthetic and 17 clinical data sets. Comparisons with two related works are also conducted. Experimental results show that our method can produce satisfactory identification of the abnormalities and approximations of the ideal post-treatment vessel lumens. In addition, it can help increase the repeatability of the measurement of clinical parameters significantly.

Reconstruction of coronary arteries from a single rotational X-ray projection sequence

Cardiovascular diseases remain the primary cause of death in developed countries. In most cases, exploration of possibly underlying coronary artery pathologies is performed using X-ray coronary angiography. Current clinical routine in coronary angiography is directly conducted in two-dimensional projection images from several static viewing angles. However, for diagnosis and treatment purposes, coronary artery reconstruction is highly suitable. The purpose of this study is to provide physicians with a three-dimensional (3-D) model of coronary arteries, e.g., for absolute 3-D measures for lesion assessment, instead of direct projective measures deduced from the images, which are highly dependent on the viewing angle. In this paper, we propose a novel method to reconstruct coronary arteries from one single rotational X-ray projection sequence. As a side result, we also obtain an estimation of the coronary artery motion. Our method consists of three main consecutive steps: 1) 3-D reconstruction of coronary artery centerlines, including respiratory motion compensation; 2) coronary artery four-dimensional motion computation; 3) 3-D tomographic reconstruction of coronary arteries, involving compensation for respiratory and cardiac motions. We present some experiments on clinical datasets, and the feasibility of a true 3-D Quantitative Coronary Analysis is demonstrated.

A review on MR vascular image processing algorithms: acquisition and prefiltering: part I

Vascular segmentation has recently been given much attention. This review paper has two parts. Part I focuses on the physics of magnetic resonance angiography (MRA) generation and prefiltering techniques applied to MRA data sets. Part II of the review focuses on the vessel segmentation algorithms. The first section of this paper introduces the five different sets of receive coils used with the MRI system for magnetic resonance angiography data acquisition. This section then presents the five different types of the most popular data acquisition techniques: time-of-flight (TOF), phase-contrast, contrast-enhanced, black-blood, T2-weighted, and T2*-weighted, along with their pros and cons. Section II of this paper focuses on prefiltering algorithms for MRA data sets. This is necessary for removing the background nonvascular structures in the MRA data sets. Finally, the paper concludes with a clinical discussion on the challenges and the future of the data acquisition and the automated filtering algorithms.

Intra-procedural coronary intervention planning using hybrid 3-dimensional reconstruction techniques1

RATIONALE AND OBJECTIVES

The purpose of this study was to develop a method to assist the cardiologist in planning an interventional procedure while the patient is on the catheterization table.

MATERIALS AND METHODS

A rotational single plane x-ray system is used to acquire images while rapidly rotating the C-arm around the patient. Based on electrocardiogram-selected projections, both a volumetric cone-beam reconstruction of the coronary tree as well as a three-dimensional model of the vessel segment of interest is generated. This information is used to compute the appropriateness of a range of different viewing angles with respect to the overlap and foreshortening of the vessel segment of interest during the cardiac cycle which results in an interactive optimal view map.

RESULTS

The proposed method has been tested on patient data and several phantom objects. The results show that both an accurate 3D model of a vessel segment of interest and its associated optimal view map can be generated to predict an appropriate gantry angle for subsequent image acquisition.

CONCLUSION

The method provides an appropriate and feasible tool to assist interventional cardiologists in planning a coronary intervention while the patient is still on the catheterization table following diagnostic coronary angiography.

Virtual coronary cineangiography

The mastering of myocardial infarction diagnosis is traditionally composed of laborious trial- and error-based examination of canonical coronary cineangiographies. In the following article we suggest a system that enables the instructor to generate student-specific cases, thus allowing teaching not only the basic feature searching and stenosis evaluation processes, but also the importance of the correct acquisition viewpoint. With the proposal of the development of the Digital Cardiologist intelligent agent we also envisage the possibility of the student's self-tutoring.

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.

Quantitative analysis of reconstructed 3-D coronary arterial tree and intracoronary devices

Traditional quantitative coronary angiography is performed on two-dimensional (2-D) projection views. These views are chosen by the angiographer to minimize vessel overlap and foreshortening. With 2-D projection views that are acquired in this nonstandardized fashion, however, there is no way to know or estimate how much error occurs in the QCA process. Furthermore, coronary arteries possess a curvilinear shape and undergo a cyclical deformation due to their attachment to the myocardium. Therefore, it is necessary to obtain three-dimensional (3-D) information to best describe and quantify the dynamic curvilinear nature of the human coronary artery. Using a patient-specific 3-D coronary reconstruction algorithm and routine angiographic images, a new technique is proposed to describe: 1) the curvilinear nature of 3-D coronary arteries and intracoronary devices; 2) the magnitude of the arterial deformation caused by intracoronary devices and due to heart motion; and 3) optimal view(s) with respect to the desired "pathway" for delivering intracoronary devices.

Three-dimensional tracking of coronary arteries from biplane angiographic sequences using parametrically deformable models

A new method for coronary artery tracking in biplane digital subtraction is presented. The dynamic tracking of nonrigid objects from two views is achieved using a generalization of parametrically deformable models. Three-dimensional (3-D) Fourier descriptors used for shape representation are obtained from the two-dimensional (2-D) descriptors of the projections. A new constraint inferred from epipolar geometry is applied to the contour model. Direct 3-D tracking is compared with the classical approach in two steps: independent 2-D tracking in each of the two projection planes; 3-D reconstruction using the epipolar constraint. Convergence quality and accuracy of the 3-D reconstruction are analyzed for several sequences showing different displacement amplitudes, deformation rates and image contrasts.

Evaluation of center-line extraction algorithms in quantitative coronary angiography

Objective testing of centerline extraction accuracy in quantitative coronary angiography (QCA) algorithms is a very difficult task. Standard tools for this task are not yet available. We present a simulation tool that generates synthetic angiographic images of a single coronary artery with predetermined centerline and diameter function. This simulation tool was used creating a library of images for the objective comparison and evaluation of QCA algorithms. This technique also provides the means for understanding the relationship between the algorithms' performance and limitations and the vessel's geometrical parameters. In this paper, two algorithms are evaluated and the results are presented.

Quantitative analysis of vascular morphology from 3D MR angiograms: In vitro and in vivo results

A 3D model-based approach for quantification of vascular morphology from several MRA acquisition protocols was evaluated. Accuracy, reproducibility, and influence of the image acquisition techniques were studied via in vitro experiments with ground truth diameters and the measurements of two expert readers as reference. The performance of the method was similar to or more accurate than the manual assessments and reproducibility was also improved. The methodology was applied to stenosis grading of carotid arteries from CE MRA data. In 11 patients, the approach was compared to manual scores (NASCET criterion) on CE MRA and DSA images, with the result that the model-based technique correlates better with DSA than the manual scores. Spearman's correlation coefficient was 0.91 (P < 0.001) for the model-based technique and DSA vs. 0.80 and 0.84 (P < 0.001) between the manual scores and DSA. From the results it can be concluded that the approach is a promising objective technique to assess geometrical vascular parameters, including degree of stenosis. Magn Reson Med 45:311-322, 2001.

3D coronary reconstruction from routine single-plane coronary angiograms: clinical validation and quantitative analysis of the right coronary artery in 100 patients

BACKGROUND

Current coronary angiographic techniques display complex three-dimensional (3D) coronary structures in two dimensions (2D). We have developed a 3D reconstruction (3DR) algorithm using standard single-plane angiographic images that allows for 3D display of coronary structures. The purpose of this study was to validate our 3DR algorithm and quantify anatomic characteristics of the right coronary artery (RCA) in vivo.

METHODS

Accuracy and reproducibility studies were performed using 3DRs of a coronary phantom and in vivo following 3DRs in 40 patients. The anatomic features of the RCA were then quantified in 100 patients.

RESULTS

Comparison of length and bifurcation angles (BA) from the phantom to the 3DRs revealed good accuracy and correlation for both (r = 0.95 and 0.93 respectively), with diameter error of < 7%. In vivo, the average root mean square (RMS) error in the spatial coordinates of the vessel centerlines was 3.12 +/- 0.77 and 3.16 +/- 0.75 mm in 20 left coronary arteries (LCA) and 20 RCAs respectively. Interobserver average RMS error was 3.47 +/- 1.96 mm and intraobserver average RMS error was 3.02 +/- 1.07 and 3.44 +/- 1.57 mm for two different operators (p = NS). The average RCA length was 10.2 +/- 1.7 cm, average radius of curvature (ROC) was 52 +/- 9 degrees, and the average 3D bifurcation angle of the posterior descending artery (PDA) from the RCA was 55 +/- 22 degrees. Foreshortening (FS) of the segments of the RCA in three 'standard' projections ranged from 0-60, 0-75, and 0-82% respectively.

CONCLUSIONS

Using our 3DR algorithm patient-specific anatomic characteristics can be accurately displayed and quantified, expanding the information that can be derived from routine coronary angiography.

3-D reconstruction of coronary arterial tree to optimize angiographic visualization

Due to vessel overlap and foreshortening, multiple projections are necessary to adequately evaluate the coronary tree with arteriography. Catheter-based interventions can only be optimally performed when these visualization problems are successfully solved. The traditional method provides multiple selected views in which overlap and foreshortening are subjectively minimized based on two dimensional (2-D) projections. A pair of images acquired from routine angiographic study at arbitrary orientation using a single-plane imaging system were chosen for three-dimensional (3-D) reconstruction. After the arterial segment of interest (e.g., a single coronary stenosis or bifurcation lesion) was selected, a set of gantry angulations minimizing segment foreshortening was calculated. Multiple computer-generated projection images with minimized segment foreshortening were then used to choose views with minimal overlapped vessels relative to the segment of interest. The optimized views could then be utilized to guide subsequent angiographic acquisition and interpretation. Over 800 cases of coronary arterial trees have been reconstructed, in which more than 40 cases were performed in room during cardiac catheterization. The accuracy of 3-D length measurement was confirmed to be within an average root-mean-square (rms) 3.5% error using eight different pairs of angiograms of an intracoronary guidewire of 105-mm length with eight radiopaque markers of 15-mm interdistance. The accuracy of similarity between the additional computer-generated projections versus the actual acquired views was demonstrated with the average rms errors of 3.09 mm and 3.13 mm in 20 LCA and 20 RCA cases, respectively. The projections of the reconstructed patient-specific 3-D coronary tree model can be utilized for planning optimal clinical views: minimal overlap and foreshortening. The assessment of lesion length and diameter narrowing can be optimized in both interventional cases and studies of disease progression and regression.

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.

CTA-based angle selection for diagnostic and interventional angiography of saccular intracranial aneurysms

Coil embolization is a safe treatment for cerebral aneurysms only if the width of the neck in relation to the fundus of the aneurysm is small. Therefore, accurate visualization of the aneurysmal neck is required both in the diagnostic process and during the intervention. Conventional digital subtraction angiography (DSA) is still the preferred modality for the examination of cerebrovascular abnormalities like aneurysms, but it often does not provide the required morphological characteristics due to the suboptimal selection of projection angles and resulting overprojections of surrounding vasculature. This paper presents a method for performing a computer-assisted calculation of the optimal projection angles for DSA by post-processing computed tomographic angiography (CTA) volume data using ray-casting techniques and a combination of image processing algorithms. By means of phantom studies, retrospective simulations of angiograms, and in vivo applications of calculated optimal viewing angles, it is demonstrated that the proposed method results in better angiographic projections of the neck of saccular aneurysms with small neck-fundus ratio than those acquired at standard angles prescribed by clinical protocols.

A viewpoint determination system for stenosis diagnosis and quantification in coronary angiographic image acquisition

This paper describes the usefulness of computer assistance in the acquisition of "good" images for stenosis diagnosis and quantification in coronary angiography. The system recommends the optimal viewpoints from which stenotic lesions can be observed clearly based on images obtained from initial viewpoints. First, the viewpoint dependency of the apparent severity of a stenotic lesion is experimentally analyzed using software phantoms in order to show the seriousness of the problem. The implementation of the viewpoint determination system is then described. The system provides good user-interactive tools for the semiautomated estimation of the orientation and diameter of stenotic segments and the three-dimensional (3-D) reconstruction of vessel structures. Using these tools, viewpoints that will not give rise to foreshortening and vessel overlap can be efficiently determined. Experiments using real coronary angiograms show the system to be capable of the reliable diagnosis and quantification of stenosis.