Coronary Tree Research Articles

Coronary Artery Tree Description and Lesion Evaluation (CatLet) score for functional evaluation of coronary stenosis: a comparison study with pressure wire fractional flow reserve.

Pressure wire fractional flow reserve (FFR) and its derivatives, such as quantitative flow ratio (QFR), computational pressure flow-derived FFR (caFFR), coronary angiography-derived FFR (FFRangio), and computed tomography-derived FFR (FFRCT), have been validated for identifying functionally significant stenosis and guiding revascularization strategy. The limitations of using these methods include the side effects of hyperemia-induced agents, additional costs, and vulnerability to microvascular resistance. FFR is related both to the degree of a stenotic coronary artery and to its subtended myocardial territory. Coronary Artery Tree Description and Lesion Evaluation (CatLet) score (also known as Hexu) is a product of the degree of a stenosis and the weighting of the affected coronary artery (myocardial territory). Hence, we hypothesized that the CatLet score could predict hemodynamically significant coronary stenosis. We retrospectively enrolled consecutive patients with stable coronary artery disease. They attended Sichuan Science City Hospital with at least one lesion of 50-90% diameter stenosis in a coronary artery of 2 mm or larger. FFR measurement was obtained during invasive coronary angiography. The CatLet score was obtained by multiplying a fixed value of 2.0 (for non-occlusive lesions) and the weight of the affected coronary artery. The primary endpoint was the CatLet score's diagnostic accuracy in identifying hemodynamically significant coronary stenosis, with a pressure wire FFR of ≤0.80 being used as reference. We analyzed the FFR and CatLet scores from 206 vessels in 175 patients with stable coronary disease and intermediate coronary lesions. The per-vessel analysis revealed an overall good correlation between the CatLet score and the FFR [r=-0.61; 95% confidence interval (95% CI): -0.69 to -0.52; P<0.01]. We also noted a significant CatLet score-FFR correlation for each of the left anterior descending artery (LAD), left circumflex (LCX), and right coronary artery (RCA). With a CatLet score ≥10 as a predictor of FFR ≤0.80, the overall diagnostic accuracy included a sensitivity of 78.80% (95% CI: 67.00-87.90%), a specificity of 85.00% (95% CI: 78.00-90.50%), a positive likelihood ratio of 5.25, a negative likelihood ratio of 0.25, and an area under the curve of 0.90 (95% CI: 0.85-0.94). Equivalent vessel-specific results were also achieved for each of the LAD, LCX, and RCA. The CatLet score, solely based on visual estimation of the results of coronary angiography, demonstrated good diagnostic performance with respect to myocardial ischemia. Its clinical values in guiding revascularization warrant further investigation.

A parametric study of the effect of 3D plaque shape on local hemodynamics and implications for plaque instability

The vast majority of heart attacks occur when vulnerable plaques rupture, releasing their lipid content into the blood stream leading to thrombus formation and blockage of a coronary artery. Detection of these unstable plaques before they rupture remains a challenge. Hemodynamic features including wall shear stress (WSS) and wall shear stress gradient (WSSG) near the vulnerable plaque and local inflammation are known to affect plaque instability. In this work, a computational workflow has been developed to enable a comprehensive parametric study detailing the effects of 3D plaque shape on local hemodynamics and their implications for plaque instability. Parameterized geometric 3D plaque models are created within a patient-specific coronary artery tree using a NURBS (non-uniform rational B-splines)-based vascular modeling pipeline. Realistic blood flow features are simulated by using a Navier–Stokes solver within an isogeometric finite-element analysis framework. Near wall hemodynamic quantities such as WSS and WSSG are quantified, and vascular distribution of an inflammatory marker (VCAM-1) is estimated. Results show that proximally skewed eccentric plaques have the most vulnerable combination of high WSS and high positive spatial WSSG, and the presence of multiple lesions increases risk of rupture. The computational tool developed in this work, in conjunction with clinical data, -could help identify surrogate markers of plaque instability, potentially leading to a noninvasive clinical procedure for the detection of vulnerable plaques before rupture.

Role of dynamic road-mapping in renal denervation procedures.

Dynamic road-mapping (DRM) (Dynamic Coronary Roadmap; Philips) offers a real-time, dynamic overlay of the coronary tree on fluoroscopy.

Efficacy of Coronary Calcium Score in Predicting Coronary Artery Morphology in Patients With Obstructive Coronary Artery Disease

BackgroundCoronary artery calcium score (CACS) is an established marker of coronary artery disease (CAD) and has been extensively used to stratify risk in asymptomatic individuals. However, the value of CACS in predicting plaque morphology in patients with advanced CAD is less established. The present analysis aims to assess the association between CACS and plaque characteristics detected by near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) imaging in patients with obstructive CAD. MethodsSeventy patients with obstructive CAD underwent coronary computed tomography angiography (CTA) and 3-vessel NIRS-IVUS imaging were included in the present analysis. The CTA data were used to measure the CACS in the entire coronary tree and the segments assessed by NIRS-IVUS, and these estimations were associated with the NIRS-IVUS measurements at a patient and segment level. ResultsIn total, 65 patients (188 segments) completed the study protocol and were included in the analysis. A weak correlation was noted between the CACS, percent atheroma volume (r = 0.271, P = .002), and the calcific burden measured by NIRS-IVUS (r = 0.648, P < .001) at patient-level analysis. Conversely, there was no association between the CACS and the lipid content, or the incidence of high-risk plaques detected by NIRS. Linear regression analysis at the segment level demonstrated an association between the CACS and the total atheroma volume (coefficient, 0.087; 95% CI, 0.024-0.149; P = .008) and the calcific burden (coefficient, 0.117; 95% CI, 0.048-0.186; P = .001), but there was no association between the lipid content or the incidence of high-risk lesions. ConclusionsIn patients with obstructive CAD, the CACS is not associated with the lipid content or plaque phenotypes. These findings indicate that the CACS may have a limited value for screening or stratifying cardiovascular risk in symptomatic patients with a high probability of CAD.

Towards effects from stent implantation into coronary bifurcation stenosis: experiment and simulation

Coronary artery disease is a widespread cause of death and disability in the population. Angioplasty of the coronary arteries is one of the most common methods of eliminating the cause of ischemia – stenosis of the coronary arteries. As a result of stent installation, a change in the angle of vascular bifurcation occurs usually, as well as a redistribution of volumetric blood flow in the coronary artery system. Considering the high variability of the branching angioarchitecture of these arteries, as well as the structure of their environment, the problem of predicting the specific redistribution of blood flow in these arteries remains unsolved; the main ways of its implementation are computational and experimental hemodynamics. Material and methods. This paper uses an experimental approach to explore the effect of stent placement in a model of coronary artery stenosis, and also provides an analysis of the current level of awareness of the scientific community on this issue. Results and discussion. The experiment showed that the throughput of the model increases by 14 % compared to the model with stenosis, and the redistribution of flows in the model depends not on diameters but on the anatomy of a particular vascular network. The data of the performed mathematical modeling are generally consistent with the results of the experiment before stent installation, when the coronary tree consists of several load-bearing branches, but have quantitative differences for the distal branches of the coronary artery model in the presence of an installed stent. Conclusions. The results of the work can be used to accumulate an experimental data array on the restructuring of blood flow during angioplasty, and can also be used to verify the numerical hemodynamics of the coronary arteries during the virtual installation of a stent in them to resolve stenosis.

Anatomical Variants and Anomalies of Coronary Artery Tree as Seen in Slice Multidetector Computed Tomography (MDCT)

Anatomical Variants and Anomalies of Coronary Artery Tree as Seen in Slice Multidetector Computed Tomography (MDCT)

Morphological Correlation between the Diameters of the Left Main Coronary Artery and its Branches Measured by QCA and Derived by Finet's Law.

Coronary artery diseases are the leading cause of death worldwide. Stenting or angioplasty of coronary arteries as interventional management requires knowledge about the morphology of the coronary tree, including luminal diameters. This work aimed to study the diameters of the left main coronary artery and its branches measured by QCA in relation to the diameters derived by Finet's law. This was a cross-sectional, retrospective, hospital-based study. The number of angiograms used was 357. The diameters of the left main coronary artery (LM1), left anterior interventricular artery (LAD1), and left circumflex artery (LCx1) were measured by QCA. The diameter of LM1 was measured by 5 mm before its termination, and the diameters of LAD1 and LCx1 were obtained by 5 mm from their origins. Finet's law was used to calculate the diameters of LM2, LAD2 and LCx2 using the QCA measurements. The mean age of participants was 53.3±8.8 years. Female patients represented 58.9%. The mean diameter of the left main coronary using QCA was 3.75±0.85 mm, and the diameter calculated using Finet's law was 3.89±0.80 mm. The diameters of LAD1 and LCx1 were larger than those calculated with Finet's law. The Z-test showed a significant difference between the diameter of the LM1 calculated by Finet's law; both diameters were positively associated. The diameters of LAD1 and LAD2 showed a non-significant correlation (r = 0.0653, P = 0.259526) and a negative correlation between LCx1 and LCX2 (r = -0.2659, P = 0.00001). The Z-test showed a significant difference in the diameter of LAD and LCx measured by QCA and Finet's law. An association was found between the diameter of LM measured by QCA and calculated with Finet's law; the diameter calculated by Finet's law was larger. The diameters of LAD and LCx measured by QCA were larger than those calculated by Finet's law. A positive correlation existed between the diameters measured by QCA and Finet's law, and they had significant differences. Finet's law can assist in the selection of stent size. Despite the literature about Finet's law, generalising its use requires more studies on different ethnicities.

Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization.

Coronary artery disease is a leading cause of death worldwide. Major adverse cardiac events are associated not only with coronary luminal stenosis but also with atherosclerotic plaque components. Coronary computed tomography angiography (CCTA) enables non-invasive evaluation of atherosclerotic plaque along the entire coronary tree. However, precise and efficient assessment of plaque features on CCTA is still a challenge for physicians in daily practice. Artificial intelligence (AI) refers to algorithms that can simulate intelligent human behavior to improve clinical work efficiency. Recently, cardiovascular imaging has seen remarkable advancements with the use of AI. AI-assisted CCTA has the potential to facilitate the clinical workflow, offer objective and repeatable quantitative results, accelerate the interpretation of reports, and guide subsequent treatment. Several AI algorithms have been developed to provide a comprehensive assessment of atherosclerotic plaques. This review serves to highlight the cutting-edge applications of AI-assisted CCTA in atherosclerosis plaque characterization, including detecting obstructive plaques, assessing plaque volumes and vulnerability, monitoring plaque progression, and providing risk assessment. Finally, this paper discusses the current problems and future directions for implementing AI in real-world clinical settings.

COACT: Coronary artery centerline tracker.

The curved planar reformation (CPR) technique is one of the most commonly used methods in clinical practice to locate coronary arteries in medical images. The artery centerline is the cornerstone for the generation of the CPR image. Here, we describe the development of a new fully automatic artery centerline tracker with the aim of increasing the efficiency and accuracy of the process. We propose a COronary artery Centerline Tracker (COACT) framework which consists of an ostium point finder (OPFinder) model, an intersection point detector (IPDetector) model and a set of centerline tracking strategies. The output of OPFinder is the ostium points. The function of the IPDetector is to predict the intersections of a sample sphere and the centerlines. The centerline tracking process starts from two ostium points detected by the OPFinder, and combines the results of the IPDetector with a series of strategies to gradually reconstruct the coronary artery centerline tree. Two coronary CT angiography (CCTA) datasets were used to validate the models. Dataset1 contains 160 cases (32 for test and 128 for training) and dataset2 contains 70 cases (20 for test and 50 for training). The results show that the average distance between the ostium points predicted by the OPFinder and the manually annotated ostium points was 0.88mm, which is similar to the differences between the results obtained by two observers (0.85mm). For the IPDetector, the average overlap of the predicted and ground truth intersection points was 97.82% and this is also close to the inter-observer agreement of 98.50%. For the entire coronary centerline tree, the overlap between the results obtained by COACT and the gold standard was 94.33%, which is slightly lower than the inter-observer agreement, 98.39%. We have developed a fully automatic centerline tracking method for CCTA scans and achieved a satisfactory result. The proposed algorithms are also incorporated in the medical image analysis platform TIMESlice (https://slice-doc.netlify.app) for further studies.

Isolated coronary artery ectasia presenting as inferior-posterior STEMI—a case-based state-of-the-art review of the current literature

Abstract Background This review is based on the case of a 71-year-old female patient, with cardiovascular risk factors, who was referred for emergency cardiological evaluation after experiencing severe anterior chest pain that had lasted for more than 5 hours at the time of admission. A diagnosis of inferior-posterior ST-segment elevation myocardial infarction was established, and emergency coronary angiography was performed. It revealed severe, diffuse dilation of the entire arterial coronary tree with slow flow and the presence of a large quantity of thrombotic material in the right coronary artery. Thromboaspiration was deemed unfit since the operator believed that there was a high risk of distal embolization with subsequent no-reflow, when passing with the catheter. As such, the patient started receiving anticoagulant therapy, and after one month she was scheduled for a control angiogram. The follow-up coronary angiography identified complete dissolution of the thrombus from the right coronary artery and a moderate stenosis in the distal segment, so no stent implantation was required. Rationale for review After performing a complete and comprehensive differential diagnosis, presence of isolated coronary artery ectasia was established. Coronary artery ectasia is a rare disease, usually found incidentally during coronary angiograms performed for other indications (chronic and acute coronary syndromes) with a vast etiology and complex pathophysiology. Because there are common morphopathologic pathways between coronary artery ectasia and atherosclerotic coronary artery disease, the clinical presentation of the former might have similar characteristics with the latter, but the management and prognosis of these patients is much less studied compared to the patients suffering from different phenotypes of coronary atherosclerosis. This review aims to summarize the etiopathogenesis, clinical presentation, diagnostic modalities, and different management pathways of coronary artery ectasia.

Developing and Validating a Simple Risk Score for Patients with Acute Myocardial Infarction.

Mortality from acute myocardial infarction (AMI) remains substantial. The current study is aimed at developing a novel simple risk score for AMI. The Coronary Artery Tree description and Lesion EvaluaTion (CatLet) extended validation trial (ChiCTR2000033730) and the CatLet validation trial (ChiCTR-POC-17013536), both being registered with <ext-link ext-link-type="uri" xlink:href="http://chictr.org" xmlns:xlink="http://www.w3.org/1999/xlink">chictr.org</ext-link>, served as the derivation and validation datasets, respectively. Both datasets included 1,018 and 308 patients, respectively. They all suffered from AMI and underwent percutaneous intervention (PCI). The endpoint was 4-year all-cause death. Lasso regression analysis was used for covariate selection and coefficient estimation. Of 26 candidate predictor variables, the four strongest predictors for 4-year mortality were included in this novel risk score with an acronym of BACEF (serum alBumin, Age, serum Creatinine, and LVEF). This score was well calibrated and yielded an AUC (95% CI) statistics of 0.84 (0.80-0.87) in internal validation, 0.89 (0.83-0.95) in internal-external (temporal) validation, and 0.83 (0.77-0.89) in external validation. Notably, it outperformed the ACEF, ACEF II, GRACE scores with respect to 4-year mortality prediction. A simple risk score for 4-year mortality risk stratification was developed, extensively validated, and calibrated in patients with AMI. This novel BACEF score may be a useful risk stratification tool for patients with AMI.

Lipid core burden in relation to the severity of calcium burden assessed by computed tomographic coronary angiography in patients with chronic coronary syndrome

Abstract Background Coronary calcium scores (CACS) obtained by computed tomographic coronary angiography (CTCA) enables a noninvasive assessment of coronary calcium burden both in a single vessel and in whole coronary tree. Although higher CACS in coronary tree is known as an atherosclerotic cardiovascular disease risk marker, coronary calcification is also known as a sign of plaque stabilization. Exploring the relationship between coronary calcification and lipid core burden may help understanding a role of calcium in coronary tree. Purpose We aimed to examine the relevance of lipid components assessed by near-infrared spectroscopy (NIRS) in accordance with different degrees of CACS assessed by preprocedural CTCA imaging. Methods We investigated consecutive patients who underwent elective percutaneous coronary intervention (PCI) and pre-PCI CTCA imaged within 3 months before PCI. Patients who had stents in target vessels and who required plaque modification before NIRS imaging were excluded. Target vessels were divided into tertiles based on target vessel CACS (CACSv) and total amount of 3-vessel CACS (CACSt), respectively. Lipid components of the target vessel were evaluated by maximum lipid core burden index in 4mm (maxLCBI4mm). MaxLCBI4mm was compared among the tertiles of CACSv and CACSt. Results A total of 95 target vessels in 95 patients were evaluated for the final dataset. CACSv categories were defined as follows: 1st tertile (CACSv &amp;lt;66), 2nd tertile (CACSv &amp;gt;66 and &amp;lt;250), and 3rd tertile (CACSv &amp;gt;250). CACSt categories were defined as showing CACSt &amp;lt;190 (1st tertile), CACSt &amp;gt;190 and &amp;lt;700 (2nd tertile), and CACSt &amp;gt;700 (3rd tertile). MaxLCBI4mm increased in proportion to an extent of CACv tertiles (306 [129-463], 280 [125-460], and 444 [345-587], Ptrend &amp;lt;0.01) and an extent of CACt tertiles (265 [99-488], 356 [202-458], and 448 [318-574], Ptrend &amp;lt;0.01). Conclusions In patients who underwent elective PCI, higher maxLCBI4mm in the PCI target vessel was associated with both higher CACS in the target vessel and total amount of 3-vessel CACS.

Abstract 16606: Diagnostic Performance of AI-QCT ISCHEMIA for Abnormal FFR Across Calcified Plaque Subgroups

Introduction: Coronary atherosclerosis and vascular morphology imaging by quantitative CT angiography (AI-QCT) provides detailed plaque and vessel measures of the coronary tree. These features combined in a tool (AI-QCT ISCHEMIA ) accurately estimate vessel level ischemia. We investigated the diagnostic performance of AI-QCT ISCHEMIA for abnormal invasive fractional flow reserve (FFR) across subgroups of calcified plaque. Methods: Symptomatic patients within the CREDENCE (N=305) and PACIFIC (N=208) studies underwent CCTA, myocardial perfusion imaging (MPI; single photon emission computed tomography [SPECT] and/or positron emission tomography [PET]), fractional flow reserve by CT (FFR-CT ) and invasive coronary angiography in conjunction with unbiased 3-vessel invasive FFR measurements as reference standard. The diagnostic performance of the non-invasive tests was compared on vessel-level across the tertiles of calcified plaque volume, on an intention-to-diagnose basis. Results: Within the CREDENCE study, AI-QCT ISCHEMIA had an area under the receiver operating characteristics curve (AUC ROC) of 0.887, 0.850, 0.816 across tertile 1-3 of calcified plaque, respectively, which was significantly higher than FFR-CT and MPI. Within the PACIFIC study, the AUC ROC was 0.811, 0.860, 0.817 across the calcified plaque tertiles 1-3, which was significantly better than SPECT, and comparable to FFR-CT ad PET. The specificity of AI-QCT ISCHEMIA was lower with increased calcified plaque, being approximately 70% for the most calcified vessels amongst both cohorts. Conclusion AI-QCT ISCHEMIA , a tool to assess coronary ischemia based on quantitative coronary plaque and vascular morphology retained good diagnostic performance for invasive FFR across calcified plaque subgroups.

CCTA-based CABG SYNTAX Score: a tool to evaluate completeness of coronary segment revascularization after bypass surgery

To describe the updated coronary computed tomographic angiography (CCTA)-based coronary artery bypass graft (CABG) anatomic SYNTAX Score (aSS) and assess its utility and reproducibility for assessing the completeness of revascularization after CABG. The CCTA-CABG aSS is a visual assessment using CCTA post-CABG which quantifies the failure in effectively grafting stenotic coronary segments, and therefore assesses the completeness of surgical revascularization. It is calculated by subtracting the aSS of successfully anastomosed coronary segments from the aSS of the native coronary tree. The inter-observer reproducibility of the CCTA-CABG aSS was evaluated in 45 consecutive patients with three-vessel disease with or without left main disease who underwent a CCTA 30 days (± 7 days) after CABG. The CCTA-CABG aSS was evaluated in 45 consecutive patients with 117 bypass grafts and 152 anastomoses. The median native coronary aSS was 35.0 [interquartile range (IQR) 27.0–41.0], whilst the median CCTA-CABG aSS was 13.0 (IQR 9.0–20.5). The inter-observer level of agreement for the native coronary aSS and the CCTA-CABG aSS were both substantial with respective Kappas of 0.67 and 0.61. The CCTA-CABG aSS was feasible in all patients who underwent CABG for complex coronary artery disease with substantial inter-observer reproducibility, and therefore can be used to quantify the completeness of revascularization after CABG.

A multi-stage neural network approach for coronary 3D reconstruction from uncalibrated X-ray angiography images

We present a multi-stage neural network approach for 3D reconstruction of coronary artery trees from uncalibrated 2D X-ray angiography images. This method uses several binarized images from different angles to reconstruct a 3D coronary tree without any knowledge of image acquisition parameters. The method consists of a single backbone network and separate stages for vessel centerline and radius reconstruction. The output is an analytical matrix representation of the coronary tree suitable for downstream applications such as hemodynamic modeling of local vessel narrowing (i.e., stenosis). The network was trained using a dataset of synthetic coronary trees from a vessel generator informed by both clinical image data and literature values on coronary anatomy. Our multi-stage network achieved sub-pixel accuracy in reconstructing vessel radius (RMSE = 0.16 ± 0.07 mm) and stenosis radius (MAE = 0.27 ± 0.18 mm), the most important feature used to inform diagnostic decisions. The network also led to 52% and 38% reduction in vessel centerline reconstruction errors compared to a single-stage network and projective geometry-based methods, respectively. Our method demonstrated robustness to overcome challenges such as vessel foreshortening or overlap in the input images. This work is an important step towards automated analysis of anatomic and functional disease severity in the coronary arteries.

AVDNet: Joint coronary artery and vein segmentation with topological consistency

Coronary CT angiography (CCTA) is an effective and non-invasive method for coronary artery disease diagnosis. Extracting an accurate coronary artery tree from CCTA image is essential for centerline extraction, plaque detection, and stenosis quantification. In practice, data quality varies. Sometimes, the arteries and veins have similar intensities and locate closely, which may confuse segmentation algorithms, even deep learning based ones, to obtain accurate arteries. However, it is not always feasible to re-scan the patient for better image quality. In this paper, we propose an artery and vein disentanglement network (AVDNet) for robust and accurate segmentation by incorporating the coronary vein into the segmentation task. This is the first work to segment coronary artery and vein at the same time. The AVDNet consists of an image based vessel recognition network (IVRN) and a topology based vessel refinement network (TVRN). IVRN learns to segment the arteries and veins, while TVRN learns to correct the segmentation errors based on topology consistency. We also design a novel inverse distance weighted dice (IDD) loss function to recover more thin vessel branches and preserve the vascular boundaries. Extensive experiments are conducted on a multi-center dataset of 700 patients. Quantitative and qualitative results demonstrate the effectiveness of the proposed method by comparing it with state-of-the-art methods and different variants. Prediction results of the AVDNet on the Automated Segmentation of Coronary Artery Challenge dataset are avaliabel at https://github.com/WennyJJ/Coronary-Artery-Vein-Segmentation for follow-up research.

Deep Learning-Based Automated Labeling of Coronary Segments for Structured Reporting of Coronary Computed Tomography Angiography in Accordance With Society of Cardiovascular Computed Tomography Guidelines.

To evaluate a novel deep learning (DL)-based automated coronary labeling approach for structured reporting of coronary artery disease according to the guidelines of the Society of Cardiovascular Computed Tomography (CT) on coronary CT angiography (CCTA). A retrospective cohort of 104 patients (60.3 ± 10.7y, 61% males) who had undergone prospectively electrocardiogram-synchronized CCTA were included. Coronary centerlines were automatically extracted, labeled, and validated by 2 expert readers according to Society of Cardiovascular CT guidelines. The DL algorithm was trained on 706 radiologist-annotated cases for the task of automatically labeling coronary artery centerlines. The architecture leverages tree-structured long short-term memory recurrent neural networks to capture the full topological information of the coronary trees by using a two-step approach: a bottom-up encoding step, followed by a top-down decoding step. The first module encodes each sub-tree into fixed-sized vector representations. The decoding module then selectively attends to the aggregated global context to perform the local assignation of labels. To assess the performance of the software, percentage overlap was calculated between the labels of the algorithm and the expert readers. A total number of 1491 segments were identified. The artificial intelligence-based software approach yielded an average overlap of 94.4% compared with the expert readers' labels ranging from 87.1% for the posterior descending artery of the right coronary artery to 100% for the proximal segment of the right coronary artery. The average computational time was 0.5 seconds per case. The interreader overlap was 96.6%. The presented fully automated DL-based coronary artery labeling algorithm provides fast and precise labeling of the coronary artery segments bearing the potential to improve automated structured reporting for CCTA.

Association of serum cystatin C level with coronary atherosclerotic plaque burden: a comprehensive analysis of observational studies and genetic study

Background and AimsEpidemiological studies show that high circulating cystatin C is associated with risk of cardiovascular disease (CVD), independent of creatinine-based renal function measurements. However, the relationship between serum cystatin C level and coronary atherosclerotic plaque burden is limited. We aimed to evaluate the relationship between circulating cystatin C and coronary atherosclerotic plaque burden.MethodsThis study was a cross-sectional study based on China community population. Measurements of plaque burden were based on the segment-involvement score (SIS) and segment stenosis score (SSS), which derived from the Coronary Artery Tree Model Depicting Coronary Artery Plaque Scores. Logistic regression model was used to demonstrate the association between cystatin C level and coronary artery plaque burden. Mendelian randomization (MR) analyses were conducted to assess the causal effect of cystatin C level on coronary atherosclerosis risk.ResultsA total of 3,043 objects were included in the present study. The odds risks (OR) of severe plaque burden in the highest serum cystatin C levels (OR: 2.50; Cl:1.59–3.91; P < 0.001) and medium-level cystatin C levels (OR: 1.86; 95% Cl: 1.21–2.88; P = 0.005) were significantly higher after fulled adjusted confounders compared with the lowest levels of serum cystatin C by SSS. The MR analysis showed that genetic predicted cystatin C levels was associated with an increased risk of coronary atherosclerosis (OR, 1.004; 95% CI, 1.002–1.006, P < 0.001) .ConclusionElevated serum cystatin C levels were associated with coronary atherosclerotic plaque burden. Cystatin C levels had a causal effect on an increased risk of coronary atherosclerosis at the genetic level.What is already known on this topic?Coronary artery disease is currently the most common cardiovascular disease and the leading global cause of mortality. Previous studies reported that higher serum cystatin C levels were associated with an increased risk for future cardiovascular events, independent of the normal creatinine levels or estimated glomerular filtration rate (eGFR) values. The presence of high-risk coronary atherosclerotic plaque burden is associated with increased risk of cardiovascular events. However, the association between serum cystatin C and coronary atherosclerotic plaque burden is not very clear.What this study adds?Our study demonstrated that the elevated serum cystatin C levels were associated with coronary atherosclerotic plaque burden. In addition, we found that serum cystatin C levels had a causal effect on an increased risk of coronary atherosclerosis at the genetic level.How this study might affect research, practice or policy?Current research finds that serum cystatin C levels were associated with coronary atherosclerosis. The metabolic pathway of cystatin C could be a target for new therapies against CAD.

Frequency and Distribution of Sheet and Nodular Calcification in Coronary Arteries in Japanese Patients.

Whether a nodular calcification (NC), which is the precursor to intracoronary thrombosis, is focally or diffusely distributed in the coronary tree has major implications for ongoing efforts to identify. This study aimed to investigate the frequency and spatial distribution patterns of sheet calcification (SC) and NC in a 3-vessel examination of autopsied human hearts.A total of 323 coronary artery specimens from 110 cadavers were obtained from autopsy cases. After fixation and decalcification, the coronary artery trees were cut every 5 mm into 4-μm transverse cross-sections for histological assessment. An SC was defined as a plate-like calcification of > 1 quadrant of the vessel or > 3 mm in diameter, and NC as nodular calcium deposits separated by fibrin, and a deposit size > 1 mm in diameter.Of the 6,306 histological cross-sections, SCs and NCs were identified in 1,627 (26%) and 233 (4%) cross-sections, respectively. SCs and NCs had a similar distribution pattern in all 3 coronary arteries. In the left anterior descending artery (LAD), NCs were predominantly located in the proximal segment: the first 45 mm from the LAD ostium (72%) and the first 60 mm from the LAD ostium (84%), respectively. However, NCs were evenly distributed throughout the length of the coronary artery in the right coronary artery (RCA) and left circumflex artery (LCX).NCs coexisted with SCs, and tended to cluster in predictable parts within the proximal segments of the LAD, but were evenly distributed throughout the RCA and LCX in coronary arteries from cadavers.

Impact of the confluence of cardiac motion and high spatial resolution on performance of ECG-gated imaging with an investigational photon-counting CT system: A phantom study

PurposePhoton-counting CT (PCCT) has higher spatial resolution that conventional EID CT which improves imaging of stationary coronary plaques and stents.. In this work, we evaluated the relationship between higher spatial resolution and motion acquisition on an investigational PCCT system. MethodsAn investigational photon-counting CT scanner (Siemens CounT) with ECG gating was used to image a coronary tree phantom with models of healthy, stenotic, and stented arteries using a motion simulator. Images were acquired with matched clinical parameters at rest and 60 beats per minute. An additional set of high dose stationary images were averaged to generate a motion-free, reduced noise reference. Scans were completed at standard (0.5 mm2) and high-resolution (0.25 mm2). Motion images were reconstructed at multiple phases. Regions of interest were drawn around vessels and segmented. Percentage difference from the reference standard was evaluated for vessel diameter and circularity. Mutual information between the reference and stationary and motion datasets was used as a measure of volumetric similarity. ResultsThe stenotic vessel showed the most variation from the reference when compared to healthy or stented vessels. Compared to standard resolution, high-resolution images had lower bias for diameter (-0.012 ± 0.19% vs −0.052 ± 0.14%) and lower variability for circularity (-0.13 ± 0.138% vs −0.12 ± 0.144%). Both differences were found to be statistically significant. High-resolution images had a slightly lower mutual information (1.28) than standard resolution (1.31). ConclusionThe higher spatial resolution enabled by photon-counting CT can be harnessed for cardiac imaging as the benefits of high spatial resolution acquisitions remain relevant in the presence of motion.