Coronary Vessel Segments Research Articles

Pretrained subtraction and segmentation model for coronary angiograms

This study introduces a novel self-supervised learning method for single-frame subtraction and vessel segmentation in coronary angiography, addressing the scarcity of annotated medical samples in AI applications. We pretrain a U-Net model on a large dataset of unannotated coronary angiograms using an image-to-image translation framework, then fine-tune it on a limited set of manually annotated samples. The pretrained model excels at comprehensive single-frame subtraction, outperforming existing DSA methods. Fine-tuning with just 40 samples yields a Dice coefficient of 0.828 for vessel segmentation. On the public XCAD dataset, our model sets a new state-of-the-art benchmark with a Dice coefficient of 0.755, surpassing both unsupervised and supervised learning approaches. This method achieves robust single-frame subtraction and demonstrates that combining pretraining with minimal fine-tuning enables accurate coronary vessel segmentation with limited manual annotations. We successfully apply this approach to assist physicians in visualizing potential vascular stenosis sites during coronary angiography. Code, dataset, and a live demo will be available available at: https://github.com/newfyu/DeepSA.

Coronary Vessel Segmentation in X-ray Angiography Images Using Edge-Based Tracking Method

Automated extraction of coronary arteries is an essential process in the diagnosis of treatment for coronary artery disease (CAD) with computer assistance. Accurately outlining the coronary artery is difficult when using X-ray coronary angiography (XCA) because of the low signal-to-noise ratio and the presence of interfering background structures. In this paper, a new approach for segmenting vessels in angiograms is presented, specifically designed to tackle the difficulties arising from non-uniform illumination, artifacts, and noise present in angiographic images. The proposed method employs an edge-based tracking tool to generate an initial probability map for segmentation. A segmentation method based on coronary vessel tracking is presented for finding the border and centerline of the vessel. The proposed method is designed based on two main components: preprocessing and tracking. In the preprocessing stage, a guided filter and edge-sharpening algorithms are used to enhance the features of the original image. In the tracking stage, an initial point is selected, and using the Gaussian property, a semi-circle operator is applied to track the line perpendicular to the vessel. The proposed method demonstrated remarkable performance in terms of sensitivity and specificity, achieving values of 86.93 and 99.61, respectively. Additionally, the method achieved an accuracy rate of 97.81. Notably, the proposed method outperformed existing state-of-the-art segmentation methods, as indicated by its higher dice score. These impressive results signify a significant advancement in the field of vessel segmentation, highlighting the effectiveness and superiority of the proposed approach.

DFA-Net: Dual multi-scale feature aggregation network for vessel segmentation in X-ray digital subtraction angiography

Even though deep learning is fascinated in fields of coronary vessel segmentation in X-ray angiography and achieves prominent progresses, most of those models probably bring high false and missed detections due to indistinct contrast between coronary vessels and background, especially for tiny sub-branches. Image improvement technique is able to better such contrast, while boosting extraneous information, e.g., other tissues with similar intensities and noise. If incorporating features derived from original and enhanced images, the segmentation performance is improved because those images comprise complementary information from different contrasts. Accordingly, inspired from advantages of contrast improvement and encoding-decoding architecture, a dual multi-scale feature aggregation network (named DFA-Net) is introduced for coronary vessel segmentation in digital subtraction angiography (DSA). DFA-Net integrates the contrast improvement using exponent transformation into a semantic segmentation network that individually accepts original and enhanced images as inputs. Through parameter sharing, multi-scale complementary features are aggregated from different contrasts, which strengthens leaning capabilities of networks, and thus achieves an efficient segmentation. Meanwhile, a risk cross-entropy loss is enforced on the segmentation, for availably decreasing false negatives, which is incorporated with Dice loss for joint optimization of the proposed strategy during training. Experimental results demonstrate that DFA-Net can not only work more robustly and effectively for DSA images under diverse conditions, but also achieve better performance, in comparison with state-of-the-art methods. Consequently, DFA-Net has high fidelity and structure similarity to the reference, providing a way for early diagnosis of cardiovascular diseases.

Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images

Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies from angiographic images. SE-RegUNet incorporates RegNet encoders and squeeze-and-excitation blocks to enhance feature extraction. A dual-phase image preprocessing strategy further improves the model's performance, employing unsharp masking and contrast-limited adaptive histogram equalization. Following fivefold cross-validation and Ranger21 optimization, the SE-RegUNet 4GF model emerged as the most effective, evidenced by performance metrics such as a Dice score of 0.72 and an accuracy of 0.97. Its potential for real-world application is highlighted by its ability to process images at 41.6 frames per second. External validation on the DCA1 dataset demonstrated the model's consistent robustness, achieving a Dice score of 0.76 and an accuracy of 0.97. The SE-RegUNet 4GF model's precision in segmenting blood vessels in coronary angiographies showcases its remarkable efficiency and accuracy. However, further development and clinical testing are necessary before it can be routinely implemented in medical practice.

Coronary vessel segmentation in coronary angiography with a multi-scale U-shaped transformer incorporating boundary aggregation and topology preservation.

Coronary vessel segmentation plays a pivotal role in automating the auxiliary diagnosis of coronary heart disease. The continuity and boundary accuracy of the segmented vessels directly affect the subsequent processing. Notably, during segmentation, vessels with severe stenosis can easily cause boundary errors and breakage, resulting in isolated islands. To address these issues, we propose a novel multi-scale U-shaped transformer with boundary aggregation and topology preservation (UT-BTNet) for coronary vessel segmentation in coronary angiography. Specifically, considering the characteristics of coronary vessels, we first develop the UT-BTNet for coronary vessels segmentation, which combines the advantages of a convolutional neural networks (CNN) and a transformer, and is able to effectively extract the local and global features of angiographic images. Secondly, we innovatively employ boundary loss and topological loss in two stages, in addition to the traditional losses. In the first stage, boundary loss is adopted, which has the effect of boundary aggregation. In the second stage, topological loss is applied to preserve the topology of the vessels, after the network converges. In the experiment, in addition to the two metrics of Dice and intersection over union (IoU), we specifically propose two metrics of boundary intersection over union (BIoU) and Betti error to evaluate boundary accuracy and the continuity of segmentation results. The results show that the Dice is 0.9291, the IoU is 0.8687, the BIoU is 0.5094, and the Betti error is 0.3400. Compared with the other state-of-the-art methods, UT-BTNet achieves better segmentation results, while ensuring the continuity and boundary accuracy of the vessels, indicating its potential clinical value.

CIDN: A context interactive deep network with edge-aware for X-ray angiography images segmentation

Accurate segmentation of X-ray angiography images is imperative for cardiovascular disease diagnosis. Despite significant strides in segmentation through deep learning, challenges persist in precisely delineating vessel edges. This paper introduces a Context Interactive Deep Network (CIDN) tailored for coronary vessel segmentation. CIDN integrates a Bio-inspired Attention Block (BAB) and a Multi-scale Interactive Block (MIB) to optimize the capture of spatial and edge information. The encoder-decoder facilitates optimal interaction between low-level features and high-level semantics. A compound loss function, amalgamating binary cross-entropy and active contour elasticity loss, enhances the recognition of intricate vessel edges. In experiments conducted on both public and private X-ray contrast images, the CIDN exhibits superior performance compared to the state-of-the-art Spatial Multi-scale Attention U-improved Network (SMAU-Net). Specifically, on dataset DCA1, the F1 value reached 0.7675, with an accuracy of 0.9795. Furthermore, on dataset JMA, the CIDN model achieved an F1 value of 0.8732, coupled with an accuracy of 0.9757. Accurate segmentation provides clinicians with precise vessel depictions, aiding in the identification of coronary stenosis and facilitating more informed diagnostic and therapeutic decisions.

A framework of myocardial bridge detection with x-ray angiography sequence

BackgroundMyocardial bridges are congenital anatomical abnormalities in which myocardium covers a segment of coronary arteries, leading to stenocardia, myocardial ischemia, and sudden cardiac death in severe cases. However, automatic diagnosis of myocardial bridge presents significant challenges.MethodA novel framework of myocardial bridge detection with x-ray angiography sequence is proposed, which can realize automatic detection of vessel stenosis and myocardial bridge. Firstly, we employ a novel neural network model for coronary vessel segmentation, which consists of both CNNs and transformer structures to effectively extract both local and global information of the vessels. Secondly, we describe the vessel segment information, establish the vessel tree in the image, and fuse the vessel tree information between sequences. Finally, based on vessel stenosis detection, we realize automatic detection of the myocardial bridge by querying the blood vessels between the image sequence information.ResultsIn experiment, we evaluate the segmentation results using two metrics, Dice and ASD, and achieve scores of 0.917 and 1.39, respectively. In the stenosis detection, we achieve an average accuracy rate of 92.7% in stenosis detection among 262 stenoses. In multi-frame image processing, vessels in different frames can be well-matched, and the accuracy of myocardial bridge detection achieves 75%.ConclusionsOur experimental results demonstrate that the algorithm can automatically detect stenosis and myocardial bridge, providing a new idea for subsequent automatic diagnosis of coronary vessels.

CLINICAL CONSEQUENCES DEPENDENCE OF ACUTE CORONARY SYNDROME INVASIVE TREATMENT ON THE CHARACTERISTICS OF SEGMENTAL INJURY OF CORONARY ARTERIES

Cardiovascular diseases are the main cause of mortality and disability factors. Prediction of the possible consequences of treatment, based on data on the features of anatomical localization of lesions, will obviously be able to improve the quality of treatment and reduce the level of disability. Optimization of interventional treatment methods depending on the coronary vasculature anatomical features, the development of collateral blood flow is aimed at the implementation of a personalized approach to the medical care.The aim of the work was to optimize interventional treatment of patients with acute coronary syndrome by studying the features of segmental formation of coronary arteries. The study analyzed the data of 121 patients with acute coronary syndrome who underwent coronary angiography with radial access and stenting of the infarct-related artery. Determination of coronary artery segments was performed according to the numbering method proposed by the American Heart Association. The severity of coronary artery disease was determined according to the Gensini scale. 88.43% of the patients included in the study had multivessel coronary disease. A relation was established between the lesion of segment 10 (r=0.312, p<0.001), segment 11 (r=0.211, p=0.015) and segment 14 (r=0.228, p=0.008) with the occurrence of MI recurrences. In case of diagonal artery lesions (D2), recurrences of MI occurred in 40.0% of patients, in the circumflex artery: 14 segment – 25.0%, and 11segment – 14.29%. In the remaining cases, the proportion of MI recurrences was significantly lower (from 2.67 to 8.86%). Analysis of the segmental localization of the infarct-related area of the coronary artery shows that there are certain "typical" areas of lesions in right and left coronary arteries - the middle section of the right coronary artery and the proximal section of the anterior interventricular branch of the left coronary artery. We also consider the obtained data to be important for predicting the risk of myocardial infarction recurrence, which turned out to be greater with occlusion of the 10th, 11th, and 14th segments of the coronary artery. In patients over 63 years of age, attention should be paid to the fourth segment of the right coronary artery, since occlusion of this section occurs more often in this age group. Atherosclerotic injury to the proximal part of the right coronary artery usually is not accompanied by prodromal syndrome of myocardial infarction in the form of unstable angina pectoris.The number of implanted stents did not reliably affect mortality and long-term survival. Correlations of lesions of certain segments of coronary vessels with previous manifestations of unstable angina pectoris and recurrent course, age and gender differences, presence of comorbidity, especially with arterial hypertension and diabetes have been established. Structural and functional changes in the myocardium were found to be the most severe in pathology of the left main stem. The relation between the lesion of the left main stem and the deterioration in the left ventricle ejection fraction (r=-0.244, p=0.005), the development of the left ventricle aneurysm (r=0.211, p=0.015) was established in 16.67% (in other segments - from 0 to 5.55%), rhythm disorders such as atrial fibrillation (r=0.304, p<0.001) in 25.00% (in other segments – from 2.17 to 9.52%), were more often accompanied by acute heart failure (Killip3-4) in 17.67% (in other segments - from 0 to 7.50%).

Semi-supervised segmentation of coronary DSA using mixed networks and multi-strategies

The coronary arteries supply blood to the myocardium, which originate from the root of the aorta and mainly branch into the left and right. X-ray digital subtraction angiography (DSA) is a technique for evaluating coronary artery plaques and narrowing, that is widely used because of its time efficiency and cost-effectiveness. However, automated coronary vessel classification and segmentation remains challenging using a little data. Therefore, the purpose of this study is twofold: one is to propose a more robust method for vessel segmentation, the other is to provide a solution that is feasible with a small amount of labeled data. Currently, there are three main types of vessel segmentation methods, i.e., graphical- and statistical-based; clustering theory based, and deep learning-based methods for pixel-by-pixel probabilistic prediction, among which the last method is the mainstream with high accuracy and automation. Under this trend, an Inception-SwinUnet (ISUnet) network combining the convolutional neural network and Transformer basic module was proposed in this paper. Considering that data-driven fully supervised learning (FSL) segmentation methods require a large set of paired data with high-quality pixel-level annotation, which is expertise-demanding and time-consuming, we proposed a Semi-supervised Learning (SSL) method to achieve better performance with a small amount of labeled and unlabeled data. Different from the classical SSL method, i.e., Mean-Teacher, our method used two different networks for cross-teaching as the backbone. Meanwhile, inspired by deep supervision and confidence learning (CL), two effective strategies for SSL were adopted, which were denominated Pyramid-consistency Learning (PL) and Confidence Learning (CL), respectively. Both were designed to filter the noise and improve the credibility of pseudo labels generated by unlabeled data. Compared with existing methods, ours achieved superior segmentation performance over other FSL and SSL ones by using data with a small equal number of labels. Code is available in https://github.com/Allenem/SSL4DSA.

Human vs. machine vs. core lab for the assessment of coronary atherosclerosis with lumen and vessel contour segmentation with intravascular ultrasound.

A machine learning (ML) algorithm for automatic segmentation of intravascular ultrasound was previously validated. It has the potential to improve efficiency, accuracy and precision of coronary vessel segmentation compared to manual segmentation by interventional cardiology experts. The aim of this study is to compare the performance of human readers to the machine and against the readings from a Core Laboratory. This is a post-hoc, cross-sectional analysis of the IBIS-4 study. Forty frames were randomly selected and analyzed by 10 readers of varying expertise two separate times, 1week apart. Their measurements of lumen, vessel, plaque areas, and plaque burden were performed in an offline software. Among humans, the intra-observer variability was not statistically significant. For the total 80 frames, inter-observer variability between human readers, the ML algorithm and Core Laboratory for lumen area, vessel area, plaque area and plaque burden were not statistically different. For lumen area, however, relative differences between the human readers and the Core Lab ranged from 0.26 to 12.61%. For vessel area, they ranged from 1.25 to 9.54%. Efficiency between the ML algorithm and the readers differed notably. Humans spent 47min on average to complete the analyses, while the ML algorithm took on average less than 1min. The overall lumen, vessel and plaque means analyzed by humans and the proposed ML algorithm are similar to those of the Core Lab. Machines, however, are more time efficient. It is warranted to consider use of the ML algorithm in clinical practice.

A coronary artery CTA segmentation approach based on deep learning.

Presence of plaque and coronary artery stenosis are the main causes of coronary heart disease. Detection of plaque and coronary artery segmentation have become the first choice in detecting coronary artery disease. The purpose of this study is to investigate a new method for plaque detection and automatic segmentation and diagnosis of coronary arteries and to test its feasibility of applying to clinical medical image diagnosis. A multi-model fusion coronary CT angiography (CTA) vessel segmentation method is proposed based on deep learning. The method includes three network layer models namely, an original 3-dimensional full convolutional network (3D FCN) and two networks that embed the attention gating (AG) model in the original 3D FCN. Then, the prediction results of the three networks are merged by using the majority voting algorithm and thus the final prediction result of the networks is obtained. In the post-processing stage, the level set function is used to further iteratively optimize the results of network fusion prediction. The JI (Jaccard index) and DSC (Dice similarity coefficient) scores are calculated to evaluate accuracy of blood vessel segmentations. Applying to a CTA dataset of 20 patients, accuracy of coronary blood vessel segmentation using FCN, FCN-AG1, FCN-AG2 network and the fusion method are tested. The average values of JI and DSC of using the first three networks are (0.7962, 0.8843), (0.8154, 0.8966) and (0.8119, 0.8936), respectively. When using new fusion method, average JI and DSC of segmentation results increase to (0.8214, 0.9005), which are better than the best result of using FCN, FCN-AG1 and FCN-AG2 model independently.

Coronary Vessel Segmentation by Coarse-to-Fine Strategy Using U-nets.

Each level of the coronary artery has different sizes and properties. The primary coronary arteries usually have high contrast to the background, while the secondary coronary arteries have low contrast to the background and thin structures. Furthermore, several small vessels are disconnected or broken up vascular segments. It is a challenging task to use a single model to segment all coronary artery sizes. To overcome this problem, we propose a novel segmenting method for coronary artery extraction from angiograms based on the primary and secondary coronary artery. Our method is a coarse-to-fine strategic approach for extracting coronary arteries in many different sizes. We construct the first U-net model to segment the main coronary artery extraction and build a new algorithm to determine the junctions of the main coronary artery with the secondary coronary artery. Using these junctions, we determine regions of the secondary coronary arteries (rectangular regions) for a secondary coronary artery-extracted segment with the second U-net model. The experiment result is 76.40% in terms of Dice coefficient on coronary X-ray datasets. The proposed approach presents its potential in coronary vessel segmentation.

Coronary vessel segmentation using multiresolution and multiscale deep learning

We present a coronary vessel segmentation method for X-Ray coronary angiography images using multiresolution and multiscale deep learning. Our segmentation method constructs a set of multiresolution images from an input image via bilinear interpolation, which can handle coronary vessels with uneven distribution of contrast. We incorporate Multiresolution and Multiscale Convolution Filtering into an U-Net Network, which can help to improve accuracy of segmentation results by dealing with various thickness of coronary vessels in different positions. We investigate two types of experiments of multiresolution strategy with U-Net and multiscale strategy with U-Net, respectively. Our method has been evaluated and compared both qualitatively with networks such as single U-Net, Attention U-Net, R2U-Net and R2AttU-Net, and quantitatively with 20 state-of-the-art visual segmentation methods using a benchmark X-Ray coronary angiography database. The experiments demonstrate that our segmentation method outperforms methods using each of these networks alone and these 20 methods significantly in terms of Dice Coefficient metric, which is considered as a major evaluation criteria of segmentation results.

Main Coronary Vessel Segmentation Using Deep Learning in Smart Medical

The automatic segmentation of main vessels on X-ray angiography (XRA) images is of great importance in the smart coronary artery disease diagnosis system. However, existing methods have been developed to this task, but these methods have difficulty in recognizing the coronary artery structure in XRA images. Main vessel segmentation is still a challenging task due to the diversity and small-size region of the vessel in the XRA images. In this study, we propose a robust method for main vessel segmentation by using deep learning architectures with fully convolutional networks. Four deep learning models based on the UNet architecture are evaluated on a clinical dataset, which consists of 3200 X-ray angiography images collected from 1118 patients. Using the precision (Pre), recall (Re), and F1 score (F1) as evaluation metrics, the average Pre, Re, and F1 for main vessel segmentation in the entire experimental dataset is 0.901, 0.898, and 0.900, respectively. 89.8% of the images exhibited a high F1 score >0.8. For the main vessel segmentation in XRA images, our deep learning methods demonstrated that vessels could be segmented in real time with a more optimized implementation, to further facilitate the online diagnosis in smart medical.

Deep learning segmentation of major vessels in X-ray coronary angiography

X-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary vessels. In the present study, we proposed a robust method for major vessel segmentation using deep learning models with fully convolutional networks. When angiographic images of 3302 diseased major vessels from 2042 patients were tested, deep learning networks accurately identified and segmented the major vessels in X-ray coronary angiography. The average F1 score reached 0.917, and 93.7% of the images exhibited a high F1 score > 0.8. The most narrowed region at the stenosis was distinctly captured with high connectivity. Robust predictability was validated for the external dataset with different image characteristics. For major vessel segmentation, our approach demonstrated that prediction could be completed in real time with minimal image preprocessing. By applying deep learning segmentation, QCA analysis could be further automated, thereby facilitating the use of QCA-based diagnostic methods.

Retraction: A Review of Coronary Vessel Segmentation Algorithms.

[This retracts the article on p. 49 in vol. 1, PMID: 22606658.].

Measurements of Lumen Areas and Diameters of Proximal and Middle Coronary Artery Segments in Subjects Without Coronary Atherosclerosis

There are plenty of data on morphology and lumen dimensions of diseased coronary arteries. However, information on normal coronary vessel anatomy is scarce. We provided computed tomography angiography-derived reference values of lumen dimensions in proximal and middle coronary segments in a healthy population with respect to gender and vessel dominance. Consecutive 2,849 computed tomography angiography examinations were reviewed to identify 201 subjects (77 men, patient age 50 ± 13 years) whose coronary arteries were free from any sign of atherosclerosis (calcium score 0, no detectable plaque). For all proximal and middle coronary segments, lumen areas (LAs) and lumen diameters were measured. Coronary vessel segmentation and dominance pattern were defined using the Syntax Score. Normal values of LAs and lumen diameters were significantly smaller for women compared with men except for the proximal right coronary artery and the left main coronary artery (LMCA) (20.2 ± 6.6 mm2 vs 23.0 ± 6.1 mm2, p = 0.0003, and 5.0 ± 0.8 mm vs 5.4 ± 0.7 mm, p = 0.0001). The lower limit of normal for the LMCA (defined as mean LA - 2 standard deviations) equaled 7.0 and 10.8 mm2 for women and men, respectively. Subjects with left (vs right) coronary dominance had significantly larger areas and diameters of the LMCA (26.2 ± 9.2 mm2 vs 20.7 ± 6.0 mm2, p = 0.0017, and 5.7 ± 1.0 mm vs 5.1 ± 0.7 mm, p = 0.0017, respectively) and proximal left circumflex (13.8 ± 2.7 mm2 vs 10.4 ± 3.8 mm2, p = 0.0001, and 4.2 ± 0.4 mm vs 3.6 ± 0.7 mm, p = 0.0001, respectively) and smaller areas and diameters of the proximal right coronary artery (7.1 ± 2.0 mm2 vs 13.3 ± 3.6 mm2, p <0.0001, and 3.0 ± 0.4 mm vs 4.1 ± 0.6 mm, p <0.0001, respectively). In conclusion, gender and coronary artery dominance pattern significantly impact normal LAs and dimensions in subjects without coronary atherosclerosis.

Effect of age and plaque morphology on diagnostic accuracy of dual source multidetector computed tomography coronary angiography.

BackgroundMultidetector computed tomography (MDCT) coronary angiography represents one of the most exciting technological revolutions in cardiac imaging and it has been increasingly used in the diagnosis of coronary artery disease. The purpose of this study is to investigate the effect of age and coronary plaque calcification on diagnostic accuracy of MDCT.MethodsThe patients were examined by using dual-source MDCT and conventional coronary angiography. MDCT results were analyzed with regard to the severity (> 50% stenosis) and morphology (non-calcified, mixed, or calcified) of coronary atherosclerotic plaques evaluated in a 16-segment model.ResultsIn total, 181 patients (94 men and 87 women) with 2,687 coronary artery segments were examined with MDCT. Ninety three patients were older than 65 years of age (group A, 42 men) and 88 were younger (group B, 52 men). Two-hundred nine coronary artery segments (7.2%) were excluded because of small distal coronary vessel segments and/or motion artifacts. The overall number of segments with non-diagnostic image quality was similar in both groups of patients. Of the 2,687 evaluated segments, 157 (5.8%) were significantly diseased, and 144 of them were correctly detected by MDCT. Diagnostic evaluation showed that the sensitivity, positive predictive value, specificity, and negative predictive value were 89.5%, 62.5%, 96.0%, and 99.2%, respectively in group A, and 95.2%, 64.8%, 97.5%, and 99.8% in group B, respectively. In addition, detailed segment-based analyses in coronary segments with non-calcified, mixed and calcified plaques in both groups were similar diagnostic accuracy.ConclusionsVery high diagnostic accuracy observed in this study suggests that MDCT coronary angiography could be a suitable diagnostic tool for not only younger patients but also for older patients.

Lidocaine bolus may facilitate computed tomographic coronary angiography in patients with frequent premature ventricular contractions

IntroductionHeart rate irregularities are the major limitations of computed tomographic coronary angiography (CTCA) due to severe motion artifacts.AimTo evaluate the safety and efficacy of a lidocaine intravenous bolus in preserving good image quality by the transient suppression of premature ventricular contractions (PVC) during the CTCA scan.Material and methodsThe study group comprised 67 consecutive patients with sinus rhythm and numerous PVC scheduled for CTCA. Intravenous boluses of 25–50 mg lidocaine were given after calcium score assessment and immediately before CTCA. The control group comprised 67 patients with sinus rhythm without PVC matched according to the body mass index (BMI), age, sex, and calcium score. All coronary vessel segments were assessed for image quality and presence of significant stenosis.ResultsAs compared with calcium score assessment, after administration of lidocaine and during the CTCA scan PVC were completely suppressed in 22 (40%), reduced in 10 (18%), unchanged in 18 (32%), and intensified in 5 (10%) patients. Overall, there were 32 (58%) patients with sinus rhythm during CTCA as compared with only 11 (20%) patients free from PVC during calcium score assessment (p < 0.001). Image quality in 871 coronary segments including both the study group and control patients was worse in patients with PVC (p < 0.0001). However, there was no statistically significant difference in the number of patients with at least one segment of non-diagnostic quality (6% vs. 12%, p = 0.36; respectively).ConclusionsSingle lidocaine bolus given prior to CTCA is safe, may temporarily eliminate or reduce the intensity of arrhythmia, and hence results in improved quality of CTCA in patients with numerous PVC.

Abstract 18728: Plasma Heparin Cofactor II Activity is a Predictor of Diffuse Coronary Artery Narrowing in Patients with Diabetes Mellitus

Background: Heparin cofactor II (HCII) is a major physiological inhibitor of thrombin action. We previously reported high plasma HCII was protective against in-stent restenosis after coronary stenting and carotid atherosclerosis. Furthermore, we reported that HCII was a predictor of cardiovascular events in patients with coronary heart disease. Kataoka et al reported patients with DM had smaller coronary vessel and longer diseased segment than those with impaired glucose tolerance (IGT) and normal glucose tolerance. HCII and morphologic characteristics of diabetic coronary was unknown. Methods: We enrolled 20 diabetic patients examined by coronary angiography, sequentially (13 males and 7 females; age, 67.9+/-10.4yrs, HbA1c&gt;/=7.0%). Coronary cinearteriography was analyzed by quantitative coronary angiography (QCA) with Cardiovascular Measurement System (CCIP-310/W). Segment diameter&lt;1.5mm as diseased lesions were determined. And diseased lesion length and HCII activity were analyzed, statistically. Results: The mean plasma level of HCII activities was 101.4+/-18.6%. And the mean of diseased lesion length was 13.7+/-12.0mm. Multivariate analysis showed age, low-HDLemia, smoking habit and disease period of DM were positively correlated with ALL, and HCII activity was negatively correlated. Univariate analysis showed HCII activity was negatively correlated with ALL, and disease period of DM was positively. That is, HCII activity and disease period of DM were correlated with lesion length of coronary artery diffuse-narrowing in diabetic patients. . Conclusions: The results suggested that low level of plasma HCII activity was correlated with diffuse coronary artery narrowing in patients with diabetic mellitus.