基于造影图像的冠状动脉三维定量分析的研究

由于X射线造影成像把血管三维空间结构投影到二维图像上,基于二维造影图像的传统诊治方法存在很大局限性.本文在冠状动脉树三维重建的基础上,研究了冠状动脉的三维定量分析方法,提出血管直径、分支夹角和血管段长度的三维测量方法.并利用冠状动脉树实物模型进行实验,对二维和三维定量分析结果进行了比较.实验结果表明,三维定量分析能够有效地提高临床医学参数的测量精度.因此,在冠心病的临床诊断和介入治疗中,该方法能够可靠地诊断血管狭窄及选择和放置支架.     

Coronary Artery Stenosis Quantification for Computed Tomography Angiography Based on Modified Student's t‐Mixture Model

Coronary artery disease (CAD) is a major cause of death in the world. As a non‐invasive imaging modality, computed tomography angiography (CTA) is now usually used in clinical practice for CAD diagnosis. Precise quantification of coronary stenosis is of great interest for diagnosis and treatment planning. In this paper, a novel cluster method based on a Modified Student's t‐Mixture Model is applied to separate the region of vessel lumen from other tissues. Then, the area of the vessel lumen in each slice is computed and the estimated value of it is fitted with a curve. Finally, the location and the level of the most stenoses are captured by comparing the calculated and fitted areas of the vessel. The proposed method has been applied to 17 clinical CTA datasets and the results have been compared with reference standard degrees of stenosis defined by an expert. The results of the experiment indicate that the proposed method can accurately quantify the stenosis of the coronary artery in CTA.     

Quantitative coronary angiography with deformable spline models

Although current edge-following schemes can be very efficient in determining coronary boundaries, they may fail when the feature to be followed is disconnected (and the scheme is unable to bridge the discontinuity) or branch points exist where the best path to follow is indeterminate. Here, the authors present new deformable spline algorithms for determining vessel boundaries, and enhancing their centerline features. A bank of even and odd S-Gabor filter pairs of different orientations are convolved with vascular images in order to create an external snake energy field. Each fitter pair will give maximum response to the segment of vessel having the same orientation as the filters. The resulting responses across filters of different orientations are combined to create an external energy field for snake optimization. Vessels are represented by B-Spline snakes, and are optimized on filter outputs with dynamic programming. The points of minimal constriction and the percent-diameter stenosis are determined from a computed vessel centerline. The system has been statistically validated using fixed stenosis and flexible-tube phantoms. It has also been validated on 20 coronary lesions with two independent operators, and has been tested for interoperator and intraoperator variability and reproducibility. The system has been found to be specially robust in complex images involving vessel branchings and incomplete contrast filling     

Directional low-pass filtering for improved accuracy and reproducibility of stenosis quantification in coronary arteriograms

Considers the quantification of percent diameter stenosis in digital coronary arteriograms of low spatial resolution. To improve accuracy and reproducibility an edge-preserving smoothing method, called the directional low-pass filter (DLF), was developed to suppress quantum noise by averaging image intensity in a direction parallel to the vessel border. Accuracy of stenosis quantification was assessed by using stenosis phantoms. The standard error of the estimate (SEE) was 0.76 pixel-length (p) without spatial filtering and further reduced to 0.50 p by DLF; the average deviation as a measure of the regularity of border definition was also reduced by DLF from 1.00 to 0.68 p (n=50, P<0.001). It was shown that the DLF outperformed the conventional moving average filter and median filter. Reproducibility in terms of intraframe variability was assessed by using coronary arteriograms obtained from 10 patients. Intraframe variability of the percent stenosis measurements was reduced from 3.5% to 2.9% by DLF (n=10, P<0.005). An analysis of variance showed, however, that the interframe variability cannot be reduced by any of the spatial filters under investigation. The result of this study has provided a guideline for angiographically based quantification of percent stenosis under limited imaging resolution and suggests a new method for improving accuracy and reproducibility by directional low-pass filtering.     

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.     

Assessment of diffuse coronary artery disease by quantitative analysis of coronary morphology based upon 3-D reconstruction from biplane angiograms

Quantitative evaluations on coronary vessel systems are of increasing importance in cardiovascular diagnosis, therapy planning, and surgical verification. Whereas local evaluations, such as stenosis analysis, are already available with sufficient accuracy, global evaluations of vessel segments or vessel subsystems are not yet common. Especially for the diagnosis of diffuse coronary artery diseases, the authors combined a 3D reconstruction system operating on biplane angiograms with a length/volume calculation. The 3D reconstruction results in a 3D model of the coronary vessel system, consisting of the vessel skeleton and a discrete number of contours. To obtain an utmost accurate model, the authors focussed on exact geometry determination. Several algorithms for calculating missing geometric parameters and correcting remaining geometry errors were implemented and verified. The length/volume evaluation can be performed either on single vessel segments, on a set of segments, or on subtrees. A volume model based on generalized elliptical conic sections is created for the selected segments. Volumes and lengths (measured along the vessel course) of those elements are summed up. In this way, the morphological parameters of a vessel subsystem can be set in relation to the parameters of the proximal segment supplying it. These relations allow objective assessments of diffuse coronary artery diseases.     

Accuracy assessment of layer decomposition using simulatedangiographic image sequences

Layer decomposition is a promising method for obtaining accurate densitometric profiles of diseased coronary artery segments. This method decomposes coronary angiographic image sequences into moving densitometric layers undergoing translation, rotation, and scaling. In order to evaluate the accuracy of this technique, we have developed a technique for embedding realistic simulated moving stenotic arteries in real clinical coronary angiograms. We evaluate the accuracy of layer decomposition in two ways. First, we compute tracking errors as the distance between the true and estimated motion of a reference point in the arterial lesion. We find that noise-weighted phase correlation and layered background subtraction are superior to cross correlation and fixed mask subtraction, respectively. Second, we compute the correlation coefficient between the true vessel profile and the raw and processed images in the region of the stenosis. We find that layer decomposition significantly improves the correlation coefficient.     

Three-dimensional quantitative coronary angiography

A method for reconstructing the three-dimensional coronary arterial tree structure from biplane two-dimensional angiographic images is presented. This method exploits the geometrical mathematics of X-ray imaging and the tracking of leading edges of injected contrast material into each vessel for identification of corresponding points on two images taken from orthogonal views. Accurate spatial position and dimension of each vessel in three-dimensional space can be obtained by this reconstruction procedure. The reconstructed arterial configuration is displayed as a shaded surface model, which can be viewed from various angles. Such three-dimensional vascular information provides accurate and reproducible measurements of vascular morphology and function. Flow measurements are obtained by tracking the leading edge of contrast material down the three-dimensional arterial tree. A quantitative analysis of coronary stenosis based on transverse area narrowing and regional blood flow, including the effect of vasoactive drugs, is described. Reconstruction experiments on actual angiographic images of the human coronary artery yield encouraging results toward a realization of computer-assisted three-dimensional quantitative angiography.     

The detection and quantification of retinopathy using digital angiograms

An algorithm is presented for the analysis and quantification of the vascular structures of the human retina. Information about retinal blood vessel morphology is used in grading the severity and progression of a number of diseases. These disease processes are typically followed over relatively long time courses, and subjective analysis of the sequential images dictates the appropriate therapy for these patients. In this research, retinal fluorescein angiograms are acquired digitally in a 1024x1024 16-b image format and are processed using an automated vessel tracking program to identify and quantitate stenotic and/or tortuous vessel segments. The algorithm relies on a matched filtering approach coupled with a priori knowledge about retinal vessel properties to automatically detect the vessel boundaries, track the midline of the vessel, and extract useful parameters of clinical interest. By modeling the vessel profile using Gaussian functions, improved estimates of vessel diameters are obtained over previous algorithms. An adaptive densitometric tracking technique based on local neighborhood information is also used to improve computational performance in regions where the vessel is relatively straight.     

Robust simultaneous detection of coronary borders in complex images

Visual estimation of coronary obstruction severity from angiograms suffers from poor inter- and intraobserver reproducibility and is often inaccurate. In spite of the widely recognized limitations of visual analysis, automated methods have not found widespread clinical use, in part because they too frequently fail to accurately identify vessel borders. The authors have developed a robust method for simultaneous detection of left and right coronary borders that is suitable for analysis of complex images with poor contrast, nearby or overlapping structures, or branching vessels. The reliability of the simultaneous border detection method and that of the authors' previously reported conventional border detection method were tested in 130 complex images, selected because conventional automated border detection might be expected to fail. Conventional analysis failed to yield acceptable borders in 65/130 or 50% of images. Simultaneous border detection was much more robust (p<.001) and failed in only 15/130 or 12% of complex images. Simultaneous border detection identified stenosis diameters that correlated significantly better with observer-derived stenosis diameters than did diameters obtained with conventional border detection (p<0.001), Simultaneous detection of left and right coronary borders is highly robust and has substantial promise for enhancing the utility of quantitative coronary angiography in the clinical setting.     

Computer Aided Segmentation of Blockages in Coronary Heart Images Using Canfis Classifier

Coronary vessel blockage segmentation is the fundamental component which extracts significant features from angiogram images to detect heart disease. This paper proposes an automated method of blockage segmentation from coronary angiogram images using coactive adaptive neuro fuzzy inference system (CANFIS) classifier. The proposed method consists of preprocessing, feature extraction and classification. The vessels in the coronary image are enhanced using preprocessing technique and then features are extracted from these images which are given to the CANFIS classifier. This classifier classifies the given test coronary image into either normal or abnormal. Further, the blockage is detected and segmented if the proposed system classifies the test image as abnormal. The proposed method achieves 99.76% sensitivity, 99.9% specificity and 99.9% accuracy for blockage vessel pixel detection.     

基于视图感知的单视图三维重建算法

尽管由于丢弃维度将3维(3D)形状投影到2维(2D)视图看似是不可逆的,但是从可视化到计算机辅助几何设计,各个垂直行业对3维重建技术的兴趣正迅速增长。传统基于物体深度图或者RGB图的3维重建算法虽然可以在一些方面达到令人满意的效果,但是它们仍然面临若干问题:(1)粗鲁的学习2D视图与3D形状之间的映射;(2)无法解决物体不同视角下外观差异所带来的的影响;(3)要求物体多个观察视角下的图像。该文提出一个端到端的视图感知3维(VA3D)重建网络解决了上述问题。具体而言,VA3D包含多邻近视图合成子网络和3D重建子网络。多邻近视图合成子网络基于物体源视图生成多个邻近视角图像,且引入自适应融合模块解决了视角转换过程中出现的模糊或扭曲等问题。3D重建子网络使用循环神经网络从合成的多视图序列中恢复物体3D形状。通过在ShapeNet数据集上大量定性和定量的实验表明,VA3D有效提升了基于单视图的3维重建结果。     

Vessel surface reconstruction with a tubular deformable model

Three-dimensional (3-D) angiographic methods are gaining acceptance for evaluation of atherosclerotic disease. However, measurement of vessel stenosis from 3-D angiographic methods can be problematic due to limited image resolution and contrast. We present a method for reconstructing vessel surfaces from 3-D angiographic methods that allows for objective measurement of vessel stenosis. The method is a deformable model that employs a tubular coordinate system. Vertex merging is incorporated into the coordinate system to maintain even vertex spacing and to avoid problems of self-intersection of the surface. The deformable model was evaluated on clinical magnetic resonance (MR) images of the carotid (n=6) and renal (n=2) arteries, on an MR image of a physical vascular phantom and on a digital vascular phantom. Only one gross error occurred for all clinical images. All reconstructed surfaces had a realistic, smooth appearance. For all segments of the physical vascular phantom, vessel radii from the surface reconstruction had an error of less than 0.2 of the average voxel dimension. Variability of manual initialization of the deformable model had negligible effect on the measurement of the degree of stenosis of the digital vascular phantom     

Evaluation of iterative sparse object reconstruction from few projections for 3-D rotational coronary angiography

A 3-D reconstruction of the coronary arteries offers great advantages in the diagnosis and treatment of cardiovascular disease, compared to 2-D X-ray angiograms. Besides improved roadmapping, quantitative vessel analysis is possible. Due to the heart's motion, rotational coronary angiography typically provides only 5–10 projections for the reconstruction of each cardiac phase, which leads to a strongly undersampled reconstruction problem. Such an ill-posed problem can be approached with regularized iterative methods. The coronary arteries cover only a small fraction of the reconstruction volume. Therefore, the minimization of the ${mbi L}_1$ norm of the reconstructed image, favoring spatially sparse images, is a suitable regularization. Additional problems are overlaid background structures and projection truncation, which can be alleviated by background reduction using a morphological top-hat filter. This paper quantitatively evaluates image reconstruction based on these ideas on software phantom data, in terms of reconstructed absorption coefficients and vessel radii. Results for different algorithms and different input data sets are compared. First results for electrocardiogram-gated reconstruction from clinical catheter-based rotational X-ray coronary angiography are presented. Excellent 3-D image quality can be achieved.      

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.     

基于SIFT匹配的两视点坐标映射模型应用

针对两视点立体合成技术提出了一种镜头遮挡图像修复方法,该修复方法利用左右视图坐标映射模型实现。首先利用尺度不变特征变换（Scale Invariant Feature Transform）算法提取左右视图特征点,然后进行特征匹配,利用匹配点对坐标信息和随机采样一致性算法（Random Sample Consensus）求解左右视图基本矩阵,最后根据该映射模型,用正常视图内容修复被遮挡区块,以提供可靠的两视点立体素材,能达到良好的修复效果。实验证明,该算法具有高鲁棒性和高运算速度。     

Shape transformer nets: Generating viewpoint-invariant 3D shapes from a single image

Single-view 3D shapes generation has achieved great success in recent years. However, current methods always blind the learning of shapes and viewpoints. The generated shape only fit the observed viewpoints and would not be optimal from unknown viewpoints. In this paper, we propose a novel encoder–decoder based network which contains a disentangled transformer to generate the viewpoint-invariant 3D shapes. The differentiable and parametric Non-uniform B-spline (NURBS) surface generation and 3D-to-3D viewpoint transformation are incorporated to learn the viewpoint-invariant shape and the camera viewpoint, respectively. Our new framework allows us to learn the latent geometric parameters of shapes and viewpoints without knowing the ground truth viewpoint. That can simultaneously generate camera-viewpoint and viewpoint-invariant 3D shapes of the object. We analyze the effects of disentanglement and show both quantitative and qualitative results of shapes generated at various unknown viewpoints.     

Stereoscopic image generation based on depth images for 3D TV

A depth-image-based rendering system for generating stereoscopic images is proposed. One important aspect of the proposed system is that the depth maps are pre-processed using an asymmetric filter to smoothen the sharp changes in depth at object boundaries. In addition to ameliorating the effects of blocky artifacts and other distortions contained in the depth maps, the smoothing reduces or completely removes newly exposed (disocclusion) areas where potential artifacts can arise from image warping which is needed to generate images from new viewpoints. The asymmetric nature of the filter reduces the amount of geometric distortion that might be perceived otherwise. We present some results to show that the proposed system provides an improvement in image quality of stereoscopic virtual views while maintaining reasonably good depth quality.     

Simultaneous detection of both coronary borders

A method for simultaneous detection of both coronary borders that is based on three-dimensional graph searching principles is presented. The simultaneous method and the authors' previously reported conventional method were applied to 29 coronary images, of which 19 were selected because conventional methods might be expected to have difficulty. Coronary borders identified by the two methods were visually compared. In the 19 difficult images, simultaneous border detection yielded superior results in 7 images and equivalent results in 12 images. Superior or equivalent results were obtained in the remaining 10 typical images. In a set of 43 uncomplicated images, minimal lumen diameters derived using simultaneous border detection correlated well with diameters derived using conventional border detection (r=0.97), diameters obtained from observer-defined borders (r=0.91), and diameters obtained using the Brown-Dodge quantitative coronary arteriography method (r=0.85). Thus simultaneous detection of left and right coronary borders provides improved accuracy in the detection of vessel borders in difficult coronary angiograms.