Coronary Arterial Tree Research Articles

Diagnostic performance of quantitative coronary artery disease assessment using computed tomography in patients with aortic stenosis undergoing transcatheter aortic-valve implantation

BackgroundComputed tomography angiography (CTA) is a cornerstone in the pre- transcatheter aortic valve replacement (TAVI) assessment. We evaluated the diagnostic performance of CTA and coronary artery calcium score (CACS) for CAD evaluation compared to invasive coronary angiography in a cohort of TAVI patients.MethodsIn consecutive TAVI patients without prior coronary revascularization and device implants, CAD was assessment by quantitative analysis in CTA. (a) Patients with non-evaluable segments were classified as obstructive CAD. (b) In patients with non-evaluable segments a CACS cut-off of 100 was applied for obstructive CAD. The reference standard was quantitative invasive coronary angiography (QCA, i.e. ≥ 50% stenosis).Results100 consecutive patients were retrospectively included, age was 82.3 ± 6.5 years and 30% of patients had CAD. In 16% of the patients, adequate visualization of the entire coronary tree (all 16 segments) was possible with CTA, while 84% had at least one segment which was not evaluable for CAD analysis due to impaired image quality. On a per-patient analysis, where patients with low image quality were classified as CAD, CTA showed a sensitivity of 100% (95% CI 88.4–100.0), specificity of 11.4% (95% CI 5.1–21.3), PPV of 32.6% (95% CI 30.8–34.5), NPV of 100% and diagnostic accuracy of 38% (95% CI 28.5–48.3) for obstructive CAD. When applying a combined approach of CTA (in patients with good image quality) and CACS (in patients with low image quality), the sensitivity and NPV remained at 100% and obstructive CAD could be ruled out in 20% of the TAVI patients, versus 8% using CTA alone.ConclusionIn routinely acquired pre-TAVI CTA, the image quality was insufficient in a high proportion of patients for the assessment of the entire coronary artery tree. However, when adding CACS in patients with low image quality to quantitative CTA assessment in patients with good image quality, obstructive CAD could be ruled-out in 1/5 of the patients and may therefore constitute a strategy to streamline pre-procedural workup, and reduce risk, radiation and costs in selected TAVI patients without prior coronary revascularization or device implants.

Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography.

Coronary computed tomography angiography (CCTA) has rapidly developed in the coronary artery disease (CAD) field. However, manual coronary artery tree segmentation and reconstruction are time-consuming and tedious. Deep learning algorithms have been successfully developed for medical image analysis to process extensive data. Thus, we aimed to develop a deep learning tool for automatic coronary artery reconstruction and an automated CAD diagnosis model based on a large, single-centre retrospective CCTA cohort. Automatic CAD diagnosis consists of two subtasks. One is a segmentation task, which aims to extract the region of interest (ROI) from original images with U-Net. The second task is an identification task, which we implemented using 3DNet. The coronary artery tree images and clinical parameters were input into 3DNet, and the CAD diagnosis result was output. We built a coronary artery segmentation model based on CCTA images with the corresponding labelling. The segmentation model had a mean Dice value of 0.771 ± 0.021. Based on this model, we built an automated diagnosis model (classification model) for CAD. The average accuracy and area under the receiver operating characteristic curve (AUC) were 0.750 ± 0.056 and 0.737, respectively. Herein, using a deep learning algorithm, we realized the rapid classification and diagnosis of CAD from CCTA images in two steps. Our deep learning model can automatically segment the coronary artery quickly and accurately and can deliver a diagnosis of ≥ 50% coronary artery stenosis. Artificial intelligence methods such as deep learning have the potential to elevate the efficiency in CCTA image analysis considerably. • The deep learning model rapidly achieved a high Dice value (0.771 ± 0.0210) in the autosegmentation of coronary arteries using CCTA images. • Based on the segmentation model, we built a CAD autoclassifier with the 3DNet algorithm, which achieved a good diagnostic performance (AUC) of 0.737. • The deep neural network could be used in the image postprocessing of coronary computed tomography angiography to achieve a quick and accurate diagnosis of CAD.

Automated segmentation of normal and diseased coronary arteries - The ASOCA challenge.

Cardiovascular disease is a major cause of death worldwide. Computed Tomography Coronary Angiography (CTCA) is a non-invasive method used to evaluate coronary artery disease, as well as evaluating and reconstructing heart and coronary vessel structures. Reconstructed models have a wide array of for educational, training and research applications such as the study of diseased and non-diseased coronary anatomy, machine learning based disease risk prediction and in-silico and in-vitro testing of medical devices. However, coronary arteries are difficult to image due to their small size, location, and movement, causing poor resolution and artefacts. Segmentation of coronary arteries has traditionally focused on semi-automatic methods where a human expert guides the algorithm and corrects errors, which severely limits large-scale applications and integration within clinical systems. International challenges aiming to overcome this barrier have focussed on specific tasks such as centreline extraction, stenosis quantification, and segmentation of specific artery segments only. Here we present the results of the first challenge to develop fully automatic segmentation methods of full coronary artery trees and establish the first large standardized dataset of normal and diseased arteries. This forms a new automated segmentation benchmark allowing the automated processing of CTCAs directly relevant for large-scale and personalized clinical applications.

This Looks Exactly Like Something I’ve Never Seen Before … or Does It?

The constellation of semilunar valve atresia, important ipsilateral ventricular hypoplasia, intact ventricular septum, and associated anomalies of the coronary arterial tree is rare but well described. Although it occurs with aortic valve atresia, it is more commonly seen with pulmonary valve atresia and is especially difficult to manage if the coronary arteries are obstructed proximally to such an extent as to cause myocardial ischemia. In such cases, the term right ventricular–dependent coronary circulation (RVDCC) is used.

Automatic construction of coronary artery tree structure based on vessel blood flow tracking.

We sought to propose an innovative vessel blood flow tracking (VBFT) method to extract coronary artery tree (CAT) and to assess the effectiveness of this VBFT versus the single-frame method. Construction of a CAT from a segmented artery is the basis of artificial intelligence-aided angiographic diagnosis. However, construction of a CAT using a single frame remains challenging, due to bifurcations and overlaps in two-dimensional angiograms. Overall, 13,222 angiograms, including 28,539 vessels, were retrospectively collected from 3275 patients and were then annotated. Coronary arteries were automatically segmented by a previously established deep neural networks (DNNs), and the skeleton lines were then extracted from segmentation images to construct CAT using the single-frame method and the VBFT method. Additionally, 1322 angiograms with 2201 vessels were used to test these two methods. Compared to the single-frame method, the VBFT method can significantly improve the accuracy of CAT as (84.3% vs. 72.3%; p < 0.001). Overlap (OV) was higher in the VBFT group than that in the Single-Frame group (91.1% vs. 87.5%; p < 0.001). The VBFT method significantly reduced the incidence of the lack of branching (7.30% vs. 13.9%, p < 0.001), insufficient length (6.70% vs. 11.0%, p < 0.001), and redundant branches (1.60% vs. 3.10%, p < 0.001). The VBFT method improved the extraction of a CAT structure, which will facilitate the development of artificial intelligence-aided angiographic diagnosis. Cardiologists can efficiently diagnose CAD using this method.

Two new stenosis detection methods of coronary angiograms.

Coronary angiography is the "gold standard" for diagnosing coronary artery disease. At present, the methods for detecting and evaluating coronary artery stenosis cannot satisfy the clinical needs, e.g., there is no prior study of detecting stenoses in prespecified vessel segments, which is necessary in clinical practice. Two vascular stenosis detection methods are proposed to assist the diagnosis. The first one is an automatic method, which can automatically extract the entire coronary artery tree and mark all the possible stenoses. The second one is an interactive method. With this method, the user can choose any vessel segment to do further analysis of its stenoses. Experiments show that the proposed methods are robust for angiograms with various vessel structures. The precision, sensitivity, and [Formula: see text] score of the automatic stenosis detection method are 0.821, 0.757, and 0.788, respectively. Further investigation proves that the interactive method can provide a more precise outcome of stenosis detection, and our quantitative analysis is closer to reality. The proposed automatic method and interactive method are effective and can complement each other in clinical practice. The first method can be used for preliminary screening, and the second method can be used for further quantitative analysis. We believe the proposed solution is more suitable for the clinical diagnosis of CAD.

Abstract 10238: Quantification of Myocardial Ischemia and Subtended Myocardial Mass at Adenosine Stress Cardiac Computed Tomography. A Feasibility Study

Introduction: Combination of computed tomography angiography (CTA) and adenosine stress CT myocardial perfusion (CTP) allows for coronary artery lesion assessment as well as myocardial ischemia. Nowadays, ischemia on CTP is assessed semi-quantitatively by visual analysis. The aim of this study was to fully quantify myocardial ischemia and the subtended myocardial mass on CTP. Methods: We included 33 patients referred for a combined CTA and adenosine stress CTP with good or excellent imaging quality on CTP. Firstly, the coronary artery tree was automatically extracted from CTA and the relevant coronary artery lesions (≥ 50%) were manually defined (fig 1A). Secondly, epi- and endocardial contours along with CTP deficits were manually defined in short-axis images (fig 1D, 1E). Thirdly, a Voronoi-based algorithm was used to quantify the subtended myocardial mass (fig 1B). Fourthly, the perfusion defect and subtended myocardial mass were spatially registered to the CTA and measured in grams (fig 1F, 1C). Finally, this can be used to quantitatively correlate the perfusion defect to the subtended myocardial mass. To assess reproducibility, left ventricular epicardial and endocardial contours along with perfusion defects were re-drawn. Results: Voronoi-based segmentation was successful in all cases. We assessed a total of 64 relevant coronary artery lesions. Average values for left ventricular mass, total subtended mass and perfusion defect mass were 118, 69 and 7 grams respectively. In 19/33 patients (58%) the total perfusion defect mass could be distributed over the relevant coronary artery lesion(s). Results were highly reproducible (r≥0.822, p&lt;0.01). Conclusions: Quantification of myocardial ischemia and subtended myocardial mass using a Voronoi-based segmentation algorithm seem feasible at adenosine stress CTP and allows for quantitative correlation of coronary artery lesions to corresponding areas of myocardial hypoperfusion.

A validated reduced-order dynamic model of nitric oxide regulation in coronary arteries

Nitric Oxide (NO) provides myocardial oxygen demands of the heart during exercise and cardiac pacing and also prevents cardiovascular diseases such as atherosclerosis and platelet adhesion and aggregation. However, the direct in vivo measurement of NO in coronary arteries is still challenging. To address this matter, a mathematical model of dynamic changes of calcium and NO concentration in the coronary artery was developed for the first time. The model is able to simulate the effect of NO release in coronary arteries and its impact on the hemodynamics of the coronary arterial tree and also to investigate the vasodilation effects of arteries during cardiac pacing. For these purposes, flow rate, time-averaged wall shear stress, dilation percent, NO concentration, and Calcium (Ca2+) concentration within coronary arteries were obtained. In addition, the impact of hematocrit on the flow rate of the coronary artery was studied. It was seen that the behavior of flow rate, wall shear stress, and Ca2+ is biphasic, but the behavior of NO concentration and the dilation percent is triphasic. Also, by increasing the Hematocrit, the blood flow reduces slightly. The results were compared with several experimental measurements to validate the model qualitatively and quantitatively. It was observed that the presented model is well capable of predicting the behavior of arteries after releasing NO during cardiac pacing. Such a study would be a valuable tool to understand the mechanisms underlying vessel damage, and thereby to offer insights for the prevention or treatment of cardiovascular diseases.

Quantification of myocardial ischemia and subtended myocardial mass at adenosine stress cardiac computed tomography. A feasibility study

Abstract Introduction Combination of computed tomography angiography (CTA) and adenosine stress CT myocardial perfusion (CTP) allows for coronary artery lesion assessment as well as myocardial ischemia. Nowadays, ischemia on CTP is assessed semi-quantitatively by visual analysis. The aim of this study was to fully quantify myocardial ischemia and the subtended myocardial mass on CTP. Methods We included 33 patients referred for a combined CTA and adenosine stress CTP with good or excellent imaging quality on CTP. Firstly, the coronary artery tree was automatically extracted from CTA and the relevant coronary artery lesions (≥50%) were manually defined (Fig. 1A). Secondly, epi- and endocardial contours along with CTP deficits were manually defined in short-axis images (Fig. 1D, 1E). Thirdly, a Voronoi-based algorithm was used to quantify the subtended myocardial mass (Fig. 1B). Fourthly, the perfusion defect and subtended myocardial mass were spatially registered to the CTA and measured in grams (Fig. 1F, 1C). Finally, this can be used to quantitatively correlate the perfusion defect to the subtended myocardial mass. Results Voronoi-based segmentation was successful in all cases. We assessed a total of 64 relevant coronary artery lesions. Average values for left ventricular mass, total subtended mass and perfusion defect mass were 118, 69 and 7 grams respectively. In 19/33 patients (58%) the total perfusion defect mass could be distributed over the relevant coronary artery lesion(s). Conclusions Quantification of myocardial ischemia and subtended myocardial mass using a Voronoi-based segmentation algorithm seem feasible at adenosine stress CTP and allows for quantitative correlation of coronary artery lesions to corresponding areas of myocardial hypoperfusion. Funding Acknowledgement Type of funding sources: None. Figure 1

CatLet score and Clinical CatLet score as predictors of long-term outcomes for patients with acute myocardial infarction undergoing delayed percutaneous coronary intervention

Abstract Background We have recently developed a Coronary Artery Tree description and Lesion EvaluaTion (CatLet) angiographic scoring system based on the 17 myocardial segment model, law of competitive blood supply, and law of flow conservation. This novel angiographic scoring system can be utilized to account for the variable coronary anatomy, the severity and complexity of coronary artery lesions, and to standardize the collection of angiographic data. Our preliminary study demonstrated that the CatLet score better predicted clinical outcomes than the SYNTAX score for patients with acute myocardial infarction (AMI) undergoing primary percutaneous coronary intervention (PCI). The current study aimed at assessing whether the CatLet score (CS) and the Clinical CatLet score (CCS) predicted clinical outcomes for AMI patients undergoing delayed PCI. Methods The CS was calculated in 1018 consecutively prospectively enrolled AMI patients. Primary end point was major adverse cardiac events (MACEs), a composite of myocardial infarction, cardiac death, and ischemia-driven revascularization. CCS was calculated multiplying the CS by the ACEF score constructed using age, creatinine, and LVEF. Tertile partitioning of CS and CCS were as follows: CSlow ≤12, CSmid 13–18, and CStop ≥19; CCSlow ≤13, CCSmid 14–24, and CCStop ≥25, respectively. Cox regression analysis was employed to identify the associations between predictors and clinical outcomes. Results Four-year MACE rates were significantly higher with CStop compared with CSlow [4.82 (3.39–6.85), P&amp;lt;0.001], which was also the case for all-cause death, cardiac death, myocardial infarction, and ischemia-driven revascularization; for all these endpoints, there were a significant trend (P&amp;lt;0.001) for higher event rates with the increasing CS tertiles as shown in Figure 1. Stratifying outcomes across CCS tertiles resulted in similar results for the comparisons between CCStop and CCSlow. For MACEs, CCS had a significantly increased C-index [(C-index (95% CI), 0.725 (0.692–0.759) vs. 0.693 (0.659–0.727), P=0.001] compared with CS, which was also the case for all-cause death, cardiac death, and myocardial infarction. However, for ischemia-driven revascularization, CCS had a slightly decreased C-index [0.655 (0.609–0.701) vs. 0.670 (0.625–0.716), P=0.235] compared with CS, without a significant difference. In terms of calibration, CCS was comparable to CS for MACEs, all-cause death, cardiac death, and myocardial infarction. However, calibration for ischemia-driven revascularization worsened for CCS compared with CS (χ2=22.07, P=0.004 vs. χ2=7.79, P=0.454). Both scores remained to be independent predictors of clinical outcomes (in separate models) after adjustment for a broad spectrum of risk factors. Conclusions CS and to a greater extent CCS were able to risk-stratify long-term outcomes in AMI patients undergoing delayed PCI. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): 2020YFC2004705 &amp; SYS2019040 Figure 1. Kaplan-Meier curves for endpoints

Design of a 3D printed coronary artery model for CT optimization

IntroductionTo design a custom phantom of the coronary arteries to optimize CT coronary angiography (CCTA) protocols. MethodsCharacteristics of the left and right coronary arteries (mean Hounsfield Unit (HU) values and diameters) were collected from consecutive CCTA examinations (n = 43). Four different materials (two mixtures of glycerine, gelatine and water, pig hearts, Ecoflex™ silicone) were scanned inside a Lungman phantom using the CCTA protocol to find the closest model to in vivo data. A 3D printed model of the coronary artery tree was created using CCTA data by exporting a CT volume rendering into Autodesk Meshmixer™ software. The model was placed in an acid bath for 5 h, then covered in Ecoflex™, which was removed after drying. Both the Ecoflex™ and pig heart were later filled with a mixture of contrast (Visipaque 320 mg I/ml), NaCl and gelatin and scanned with different levels of tube current and iterative reconstruction (ASiR-V). Objective (HU, noise and size (vessel diameter) and subjective analysis were performed on all scans. ResultsThe gelatine mixtures had HU values of 130 and 129, Ecoflex™ 65 and the pig heart 56. At the different mA/ASiR-V levels the contrast filled Ecoflex™ had a mean HU 318 ± 4, noise 47±7HU and diameter of 4.4 mm. The pig heart had a mean HU of 209 ± 5, noise 38±4HU and a diameter of 4.4 mm. With increasing iterative reconstruction level the visualisation of the pig heart arteries decreased so no measurements could be performed. ConclusionThe use of a 3D printed model of the arteries and casting with the Ecoflex™ silicone is the most suitable solution for a custom-designed phantom. Implications for practiceCustom designed phantoms using 3D printing technology enable cost effective optimisation of CT protocols.

Intelligent Algorithms-Based CT Image Segmentation in Patients with Cardiovascular Diseases and Realization of Visualization Algorithms

The study focused on the intelligent algorithms-based segmentation of computed tomography (CT) images of patients with cardiovascular diseases (CVD) and the realization of visualization algorithms. The first step was to design a method for precise segmentation under the cylinder model based on the coronary body data of the coarse segmentation, and then the principles of different visualization algorithms were discussed. The results showed that the precise segmentation method can effectively eliminate most of the branches and calcified lesions; curved planar reformation (CPR) and straightened CPR can display the entire blood vessel on one image; and spherical CPR can display the complete coronary artery tree on an image, so that a problem with a certain blood vessel can be quickly found. In conclusion, the precise segmentation of CT images of CVD and visualization algorithm based on the cylinder model have clinical significance in the diagnosis of CVD.

Measurement of compensatory arterial remodelling over time with serial coronary computed tomography angiography and 3D metrics.

The magnitude of alterations in which coronary arteries remodel and narrow over time is not well understood. We aimed to examine changes in coronary arterial remodelling and luminal narrowing by three-dimensional (3D) metrics from serial coronary computed tomography angiography (CCTA). From a multicentre registry of patients with suspected coronary artery disease who underwent clinically indicated serial CCTA (median interscan interval = 3.3 years), we quantitatively measured coronary plaque, vessel, and lumen volumes on both scans. Primary outcome was the per-segment change in coronary vessel and lumen volume from a change in plaque volume, focusing on arterial remodelling. Multivariate generalized estimating equations including statins were calculated comparing associations between groups of baseline percent atheroma volume (PAV) and location within the coronary artery tree. From 1245 patients (mean age 61 ± 9 years, 39% women), a total of 5721 segments were analysed. For each 1.00 mm3 increase in plaque volume, the vessel volume increased by 0.71 mm3 [95% confidence interval (CI) 0.63 to 0.79 mm3, P < 0.001] with a corresponding reduction in lumen volume by 0.29 mm3 (95% CI -0.37 to -0.21 mm3, P < 0.001). Serial 3D arterial remodelling and luminal narrowing was similar in segments with low and high baseline PAV (P ≥ 0.496). No differences were observed between left main and non-left main segments, proximal and distal segments and side branch and non-side branch segments (P ≥ 0.281). Over time, atherosclerotic coronary plaque reveals prominent outward arterial remodelling that co-occurs with modest luminal narrowing. These findings provide additional insight into the compensatory mechanisms involved in the progression of coronary atherosclerosis.

ANMCO Position paper: Prognostic and therapeutic relevance of non-obstructive coronary atherosclerosis

Atherosclerosis often affects the coronary arterial tree. Very often the disease does not translate in significant narrowing of the vessels, thus determining only a non-obstructive disease. This condition that is described as non-obstructive coronary artery disease (NobsCAD) should be distinguished from the absence of disease (i.e. smooth coronary arteries) as it carries a specific prognostic value. The detection and reporting of NobsCAD should prompt preventive measures that can be individualized upon the degree of the underlying burden of disease. The accompanying clinical condition, the other cardiovascular risk factors present, and the description of the severity and extent of NobsCAD should provide the framework for an individualized treatment that should also consider the best available evidences and scientific guidelines. The description of NobsCAD represents an important information to be collected whenever a coronary angiogram (both invasive and non-invasive) is performed. Treating the patient according to the presence and extent of NobsCAD offers prognostic benefits well beyond those offered by considering only the traditional cardiovascular risk factors. In order to reach this goal, NobsCAD should not be confused with the absence of coronary atherosclerosis or even ignored when detected as if it was a trivial information.

Age, Serum Creatinine, and Left Ventricular Ejection Fraction Improved the Performance of the CatLet Angiographic Scoring System in Terms of Outcome Predictions for Patients with Acute Myocardial Infarction: A Median 4.3-Year Follow-Up Study

Background: We recently developed the Coronary Artery Tree description and Lesion EvaluaTion (CatLet) angiographic scoring system. Our preliminary study demonstrated that the CatLet score better predicted clinical outcomes than the SYNTAX score. The current study aimed at assessing whether 3 clinical variables (CVs) – age, serum creatinine, and left ventricular ejection fraction (LVEF) – improved the performance of the CatLet score in outcome predictions in patients with acute myocardial infarction (AMI). Methods: This study was a post hoc study of the CatLet score validation trial. Primary endpoint was major adverse cardiac or cerebrovascular events (MACCEs), and secondary endpoints were all-cause deaths and cardiac deaths. Results: Over 1,185 person-years (median [interquartile range], 4.3 [3.8–4.9] years), there were 64 MACCEs (20.8%), 56 all-cause deaths (18.2%), and 47 cardiac deaths (15.2%). The addition of the 3 CVs to the stand-alone CatLet score significantly increased the Harrell’s C-index by 0.0967 (p = 0.002) in MACCEs, by 0.1354 (p < 0.001) in all-cause deaths, and by 0.1187 (p = 0.001) in cardiac deaths. When compared with the stand-alone CatLet score, improved discrimination and better calibration led to a significantly refined risk stratification, particularly at the intermediate-risk category. Conclusions: CatLet score had a predicting value for clinical outcome in AMI patients. This predicting value can be improved through a combination with age, serum creatinine, and LVEF (http://www.chictr.org.cn; unique identifier: ChiCTR-POC-17013536).

ANMCO POSITION PAPER: Prognostic and therapeutic relevance of non-obstructive coronary atherosclerosis.

Atherosclerosis often affects the coronary arterial tree. Frequently the disease does not translate in significant narrowing of the vessels, thus determining only a non-obstructive disease. This condition that is described as non-obstructive coronary artery disease (NobsCAD) should be distinguished from the absence of disease (i.e. smooth coronary arteries) as it carries a specific prognostic value. The detection and reporting of NobsCAD should prompt preventive measures that can be individualized upon the degree of the underlying burden of disease. The accompanying clinical condition, the other cardiovascular risk factors present, and the description of the severity and extent of NobsCAD should provide the framework for an individualized treatment that should also consider the best available scientific evidence and guidelines. The description of NobsCAD represents important information to be collected whenever a coronary angiogram (both invasive and non-invasive) is performed. Treating the patient according to the presence and extent of NobsCAD offers prognostic benefits well beyond those offered by considering only the traditional cardiovascular risk factors. In order to reach this goal, NobsCAD should not be confused with the absence of coronary atherosclerosis or even ignored when detected as if it was a trivial information to provide.

Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography.

This paper presents an in-depth study and analysis of the 3D arterial centerline in spiral CT coronary angiography, and constructs its detection and extraction technique. The first time, the distance transform is used to complete the boundary search of the original figure; the second time, the distance transform is used to calculate the value of the distance transform of all voxels, and according to the value of the distance transform, unnecessary voxels are deleted, to complete the initial contraction of the vascular region and reduce the computational consumption in the next process; then, the nonwitnessed voxels are used to construct the maximum inner joint sphere model and find the skeletal voxels that can reflect the shape of the original figure. Finally, the skeletal lines were optimized on these initially extracted skeletal voxels using a dichotomous-like principle to obtain the final coronary artery centerline. Through the evaluation of the experimental results, the algorithm can extract the coronary centerline more accurately. In this paper, the segmentation method is evaluated on the test set data by two kinds of indexes: one is the index of segmentation result evaluation, including dice coefficient, accuracy, specificity, and sensitivity; the other is the index of clinical diagnosis result evaluation, which is to refine the segmentation result for vessel diameter detection. The results obtained in this paper were compared with the physicians' labeling results. In terms of network performance, the Dice coefficient obtained in this paper was 0.89, the accuracy was 98.36%, the sensitivity was 93.36%, and the specificity was 98.76%, which reflected certain advantages in comparison with the advanced methods proposed by previous authors. In terms of clinical evaluation indexes, by performing skeleton line extraction and diameter calculation on the results obtained by the segmentation method proposed in this paper, the absolute error obtained after comparing with the diameter of the labeled image was 0.382 and the relative error was 0.112, which indicates that the segmentation method in this paper can recover the vessel contour more accurately. Then, the results of coronary artery centerline extraction with and without fine branch elimination were evaluated, which proved that the coronary artery centerline has higher accuracy after fine branch elimination. The algorithm is also used to extract the centerline of the complete coronary artery tree, and the results prove that the algorithm has better results for the centerline extraction of the complete coronary vascular tree.

Association of Statin Treatment With Progression of Coronary Atherosclerotic Plaque Composition

The density of atherosclerotic plaque forms the basis for categorizing calcified and noncalcified morphology of plaques. To assess whether alterations in plaque across a range of density measurements provide a more detailed understanding of atherosclerotic disease progression. This cohort study enrolled 857 patients who underwent serial coronary computed tomography angiography 2 or more years apart and had quantitative measurements of coronary plaques throughout the entire coronary artery tree. The study was conducted from 2013 to 2016 at 13 sites in 7 countries. The main outcome was progression of plaque composition of individual coronary plaques. Six plaque composition types were defined on a voxel-level basis according to the plaque attenuation (expressed in Hounsfield units [HU]): low attenuation (-30 to 75 HU), fibro-fatty (76-130 HU), fibrous (131-350 HU), low-density calcium (351-700 HU), high-density calcium (701-1000 HU), and 1K (>1000 HU). The progression rates of these 6 compositional plaque types were evaluated according to the interaction between statin use and baseline plaque volume, adjusted for risk factors and time interval between scans. Plaque progression was also examined based on baseline calcium density. Analysis was performed among lesions matched at baseline and follow-up. Data analyses were conducted from August 2019 through March 2020. In total, 2458 coronary lesions in 857 patients (mean [SD] age, 62.1 [8.7] years; 540 [63.0%] men; 548 [63.9%] received statin therapy) were included. Untreated coronary lesions increased in volume over time for all 6 compositional types. Statin therapy was associated with volume decreases in low-attenuation plaque (β, -0.02; 95% CI, -0.03 to -0.01; P = .001) and fibro-fatty plaque (β, -0.03; 95% CI, -0.04 to -0.02; P < .001) and greater progression of high-density calcium plaque (β, 0.02; 95% CI, 0.01-0.03; P < .001) and 1K plaque (β, 0.02; 95% CI, 0.01-0.03; P < .001). When analyses were restricted to lesions without low-attenuation plaque or fibro-fatty plaque at baseline, statin therapy was not associated with a change in overall calcified plaque volume (β, -0.03; 95% CI, -0.08 to 0.02; P = .24) but was associated with a transformation toward more dense calcium. Interaction analysis between baseline plaque volume and calcium density showed that more dense coronary calcium was associated with less plaque progression. The results suggest an association of statin use with greater rates of transformation of coronary atherosclerosis toward high-density calcium. A pattern of slower overall plaque progression was observed with increasing density. All findings support the concept of reduced atherosclerotic risk with increased densification of calcium.

Correlation of coronary artery geometry with the complexity and severity of coronary atherosclerosis: rationale and design of the GEOMETRY-CTA study

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning 2014-2020» in the context of the project “Correlation of coronary artery tree geometry and clinical parameters with the distribution, complexity and severity of coronary artery disease: a pilot study using multislice CT coronary angiography” (MIS 5047882) Background Development and progression of coronary atherosclerosis is complex and not yet fully understood. Accumulative evidence supports the notion that coronary artery disease (CAD) results from an interaction of systemic factors, such as dyslipidemia, with local hemodynamic factors that regulate the site-specific predilection of atherosclerosis, particularly low endothelial shear stress (ESS). Since low ESS develops in geometrically irregular regions of the coronary tree for a given arterial blood flow, geometrical factors affect atherosclerotic plaque distribution and vulnerability. However, the correlation of coronary geometric features with the complexity and severity of CAD has not been fully elucidated. Purpose GEOMETRY-CTA is a prospective, non-randomized, observational study that aims to investigate the correlation of coronary artery geometrical features with the complexity and severity of CAD using Coronary Computed Tomography Angiography (CCTA). The prognostic value of a CCTA-derived geometric risk score will also be assessed. Methods The study aims to recruit 100 consecutive patients with suspected CAD and low/intermediate pre-test probability. Coronary geometrical characteristics such as angulation of coronary bifurcations, tortuosity, coronary artery volume index (CAVi) and vessel-length will be assessed with multi-planar reformation and volume rendering techniques, and integrated into a single geometric risk score. The extent and vulnerability of plaque burden will be calculated using several anatomical scoring systems such as the Leiden CTA risk score, CT-SYNTAX score, and CT-adapted Gensini score. Patients will be prospectively followed for 12 months after enrollment. Results The primary objective of the study is to evaluate the correlation of the geometric risk score with CAD severity and plaque vulnerability. We also hypothesize that wider bifurcation angles and lower CAVi augmenting the atherosclerosis-prone environment and predict higher CAD extent and complexity. Conclusion The present study aims to introduce a quantitative, non-invasive imaging biomarker expressing CAD severity and investigate its prognostic value in regards to adverse cardiovascular events. The derived index will be available for incorporation in larger national prospective studies for further cardiovascular risk stratification.

Quantification of myocardial ischemia and subtended myocardial mass at adenosine stress cardiac computed tomography: a feasibility study

Combination of coronary computed tomography angiography (CCTA) and adenosine stress CT myocardial perfusion (CTP) allows for coronary artery lesion assessment as well as myocardial ischemia. However, myocardial ischemia on CTP is nowadays assessed semi-quantitatively by visual analysis. The aim of this study was to fully quantify myocardial ischemia and the subtended myocardial mass on CTP. We included 33 patients referred for a combined CCTA and adenosine stress CTP protocol, with good or excellent imaging quality on CTP. The coronary artery tree was automatically extracted from the CCTA and the relevant coronary artery lesions with a significant stenosis (≥ 50%) were manually defined using dedicated software. Secondly, epicardial and endocardial contours along with CT perfusion deficits were semi-automatically defined in short-axis reformatted images using MASS software. A Voronoi-based segmentation algorithm was used to quantify the subtended myocardial mass, distal from each relevant coronary artery lesion. Perfusion defect and subtended myocardial mass were spatially registered to the CTA. Finally, the subtended myocardial mass per lesion, total subtended myocardial mass and perfusion defect mass (per lesion) were measured. Voronoi-based segmentation was successful in all cases. We assessed a total of 64 relevant coronary artery lesions. Average values for left ventricular mass, total subtended mass and perfusion defect mass were 118, 69 and 7 g respectively. In 19/33 patients (58%) the total perfusion defect mass could be distributed over the relevant coronary artery lesion(s). Quantification of myocardial ischemia and subtended myocardial mass seem feasible at adenosine stress CTP and allows to quantitatively correlate coronary artery lesions to corresponding areas of myocardial hypoperfusion at CCTA and adenosine stress CTP.