Coronary Computed Tomography Angiography Examination Research Articles

ROLE OF CORONARY COMPUTED TOMOGRAPHY ANGIOGRAPHY (CCTA) IN SPONTANEOUS CORONARY ARTERY DISSECTION (SCAD)

Abstract Spontaneous Coronary Artery Dissection (SCAD) is a rare but insidious cause of myocardial infarction and sudden cardiac death especially in those who lacks of traditional cardiovascular risk factors. Although the most appropriate management of patients with SCAD is not clearly defined, it‘s essential to make an early diagnosis in order to follow a conservative approach. Nowadays, the role of Coronary Computed Tomography Angiography (CCTA) is consolidated in the study of atherosclerotic coronary pathology but its contribution in the diagnosis and follow–up of patients with SCAD does not apperar to be defined. The purpose of this study was to retrospectively investigate the diagnostic performance of CCTA and its role in the follow–up of patients with diagnosis of SCAD. We examined 307 patients admitted to emergency department for acute chest pain and an ECG diagnosis of STEMI from March 2021 to July 2023. 1 patient was diagnosed with type 2 SCAD on invasive coronary angiography (ICA) and 2 patients received a suspected diagnosis of SCAD at ICA and CCTA examination was performed to confirm the suspicion. CCTA examination showed the acute presence of Intramural hematoma (IMH) of the wall of left anterior descending artery (LAD) in 2 patients, confirming the angiographic suspect of SCAD type 2. All 3 patients were treated with conservatie therapy and subsequentely underwent CCTA of follow–up. The examination performed respectively 3 months, 45 days and 1 month after the acute event demonstrated the complete resolution of IMH in all patients and the patency of the vessel lumens. CCTA seems to be a feasible and effective imaging modality to detect the main findings of SCAD. It may play a crucial role in the management of patients with acute chest pain at low–intermediate risk of ACS in order to reduce the number of misdiagnoses. Furthermore, CCTA could facilitate follow–up of patients with diagnosis of SCAD given the high recurrency rate of this pathology. However, further large prospective studies are needed to clarify the role of CCTA in the diagnosis and in the follow–up of SCAD.

NoiseNet, a fully automatic noise assessment tool that can identify non-diagnostic CCTA examinations.

Image noise and vascular attenuation are important factors affecting image quality and diagnostic accuracy of coronary computed tomography angiography (CCTA). The aim of this study was to develop an algorithm that automatically performs noise and attenuation measurements in CCTA and to evaluate the ability of the algorithm to identify non-diagnostic examinations. The algorithm, "NoiseNet", was trained and tested on 244 CCTA studies from the Swedish CArdioPulmonary BioImage Study. The model is a 3D U-Net that automatically segments the aortic root and measures attenuation (Hounsfield Units, HU), noise (standard deviation of HU, HUsd) and signal-to-noise ratio (SNR, HU/HUsd) in the aortic lumen, close to the left coronary ostium. NoiseNet was then applied to 529 CCTA studies previously categorized into three subgroups: fully diagnostic, diagnostic with excluded parts and non-diagnostic. There was excellent correlation between NoiseNet and manual measurements of noise (r = 0.948; p < 0.001) and SNR (r = 0.948; <0.001). There was a significant difference in noise levels between the image quality subgroups: fully diagnostic 33.1 (29.8-37.9); diagnostic with excluded parts 36.1 (31.5-40.3) and non-diagnostic 42.1 (35.2-47.7; p < 0.001). Corresponding values for SNR were 16.1 (14.0-18.0); 14.0 (12.4-16.2) and 11.1 (9.6-14.0; p < 0.001). ROC analysis for prediction of a non-diagnostic study showed an AUC for noise of 0.73 (CI 0.64-0.83) and for SNR of 0.80 (CI 0.71-0.89). In conclusion, NoiseNet can perform noise and SNR measurements with high accuracy. Noise and SNR impact image quality and automatic measurements may be used to identify CCTA studies with low image quality.

Prediction model based on artificial intelligence for identifying risk of coronary atherosclerotic heart disease in computed tomography

ObjectiveTo analyze the application value of artificial intelligence (AI) in coronary computed tomography angiography (CCTA) image processing and diagnosis of coronary atherosclerotic heart disease (CHD). MethodsA total of 80 patients with suspected CHD in our hospital were selected for CCTA examination and blood lipid examination. The convolutional neural networks (CNN) model of coronary artery plaque detection was constructed, and the data set was randomly divided into training set and test set after pretreatment of lipid characteristics and image characteristics. The prediction efficiency and accuracy of the model were evaluated. ResultsIn the data set, the lipid indexes LDL-C, TC, and TG of patients in the CHD group were significantly higher than those in the Non-CHD group (P < 0.05). The average processing and diagnosis time of the AI model was (187.19 ± 18.79) s, which was significantly shorter than the average time of doctors (989.07 ± 50.40) s, and the difference was statistically significant (P < 0.05). There was no significant difference in the detection of calcified plaque, non-calcified plaque, and mixed plaque between doctors and AI models (P > 0.05). However, 5 plaques were misdiagnosed in the AI model (3.38%). The area under the curve (AUC) value of the CNN recognition model-based AI and manual recognition of doctors for the CHD were 0.870 (95% CI: 0.698–0.931) and 0.870 (95% CI: 0.691–0.926) (P < 0.001). ConclusionAI integrated with lipid parameters has certain clinical value in CCTA image processing efficiency and plaque diagnosis, and can be used as an effective auxiliary tool to analyze and diagnose CHD.

Prognostic significance of CAD-RADS for patients with suspected coronary artery disease: A systematic review and meta-analysis

Abstract Background Coronary Artery Disease-Reporting and Data System (CAD-RADS) is a standardized reporting system that offers a structured method for interpreting and reporting results obtained through coronary computed tomography angiography. It has been deemed useful in the prognostication of patients with suspected coronary artery disease (CAD). Purpose The present meta-analysis sought to assess the prognostic value of CAD-RADS in individuals with suspected CAD. Materials and Methods We conducted a systematic search of the electronic databases of PubMed, Web of Science Core Collection, Cochrane advanced search, and EMBASE. A random-effects model was implemented to evaluate the pooled hazard ratio (HR) for each CAD-RADS category and area under the receiver operating characteristics curve for predicting major adverse cardiovascular events. Results Data from 37 596 coronary computed tomography angiography examinations from 13 studies were analyzed. During follow-up, 2,536 (6.7%) adverse events were observed. Pooled HRs for prediction of adverse events were significant for all CAD-RADS categories, with incremental increase in HRs with progressively higher categories. For prediction of all-cause mortality, all categories except CAD-RADS 1 showed significant HR compared with CAD-RADS 0. Combination of CAD-RADS to conventional clinical risk factors and CAC resulted in a high predictive capacity for adverse events (pooled area under the receiver operating characteristics curve: 0.82 ([95% confidence interval, 0.73-0.91]). Conclusion The CAD-RADS category imparts information on the CAD severity and shows incremental increase in HR for adverse events with progressively higher categories, especially beyond CAD-RADS4b.

Diagnostic performance of 3.0 T unenhanced Dixon water-fat separation coronary MR angiography in patients with low-to-intermediate risk of coronary artery disease

PurposeTo evaluate the diagnostic performance of 3.0 T unenhanced compressed-sensing sensitivity encoding (CS-SENSE) Dixon water-fat separation coronary MR angiography (CMRA) in patients with low-to-intermediate risk of coronary artery disease (CAD) and its ability to grade the severity of CAD based on Coronary Artery Disease Reporting and Data System (CAD-RADS). MethodsA total of 55 patients who was clinically evaluated as low-to-intermediate risk of CAD were finally included to undergo both 3.0 T CS-SENSE water-fat separation CMRA and coronary computed tomography angiography (CCTA), and 11 of them also underwent X-ray coronary angiography (CAG). The severity of coronary artery disease was graded in patients who had completed both CCTA and CMRA examinations by the use of CAD-RADS reports for the patients with stable chest pain, and the diagnostic consistency between the two approaches was evaluated. Diagnostic performance of CMRA was assessed using the combination of CCTA and CAG as the reference standard for excluding or confirming CAD respectively. ResultsThe sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of 3.0 T unenhanced water-fat separation coronary MRA were 90.0%, 95.0%, 81.8%, 97.4% and 94.0% for a patient-based analysis respectively. In comparison with CCTA, 3.0 T Dixon water-fat separation CMRA demonstrated excellent consistency in grading the severity of coronary heart disease according to CAD-RADS (0.77 for kappa value). ConclusionIn the group of low-to-intermediate probability for CAD, 3.0 T unenhanced CS-SENSE Dixon water-fat separation CMRA can present satisfactory diagnostic performance for the exclusion of CAD with high sensitivity and negative predictive value as well as the evaluation of grading the severity of coronary artery disease.

관상동맥 CT와 심근관류 CT 검사를 이용한 고농도 조영제와 저농도 조영제 간 비교 평가

With the advancement of CT technology, the use of low tube voltages, and the clinical efficacy of low-concentration contrast media are gradually being demonstrated. In this study, we compared the differences between high- and low-concentration contrast media using Coronary Computed Tomography Angiography (CCTA) and Computed Tomography-Myocardial Perfusion Imaging (CT-MPI). These imaging techniques can simultaneously evaluate the patient's hemodynamic distribution and myocardium. The study results indicated that the quantitative analysis of tissues and blood vessels in CCTA showed that high-concentration contrast media led to a 5 to 10% increase in Hounsfield Units (HU), but the probability of significance was greater than 0.05. As a result of myocardial evaluation using CCTA and CT-MPI, the difference between the two contrast agents was within 5%, and the probability of significance was greater than 0.05. Qualitative analysis utilized nominal measurements in five categories and did not reveal significant differences between low- and high-concentration contrast media with a significance probability greater than 0.05. This study differed from existing studies by utilizing high- and low-concentration contrast media for CCTA and CT-MPI examinations. Analysis using CCTA and CT-MPI shows that low-concentration images are not inferior to images obtained using high-concentration contrast media.

Optimizing Coronary Computed Tomography Angiography Using a Novel Deep Learning-Based Algorithm.

Coronary computed tomography angiography (CCTA) is an essential part of the diagnosis of chronic coronary syndrome (CCS) in patients with low-to-intermediate pre-test probability. The minimum technical requirement is 64-row multidetector CT (64-MDCT), which is still frequently used, although it is prone to motion artifacts because of its limited temporal resolution and z-coverage. In this study, we evaluate the potential of a deep-learning-based motion correction algorithm (MCA) to eliminate these motion artifacts.124 64-MDCT-acquired CCTA examinations with at least minor motion artifacts were included. Images were reconstructed using a conventional reconstruction algorithm (CA)and a MCA. Image quality (IQ), according to a 5-point Likert score, was evaluated per-segment, per-artery, and per-patient and was correlated with potentially disturbing factors (heart rate (HR), intra-cycle HR changes, BMI, age, and sex). Comparison was done by Wilcoxon-Signed-Rank test, and correlation by Spearman's Rho.Per-patient, insufficient IQ decreased by 5.26%, and sufficient IQ increased by 9.66% with MCA. Per-artery, insufficient IQ of the right coronary artery (RCA) decreased by 18.18%, and sufficient IQ increased by 27.27%. Per-segment, insufficient IQ in segments 1 and 2 decreased by 11.51% and 24.78%, respectively, and sufficient IQ increased by 10.62% and 18.58%, respectively. Total artifacts per-artery decreased in the RCA from 3.11 ± 1.65 to 2.26 ± 1.52. HR dependence of RCA IQ decreased to intermediate correlation in images with MCA reconstruction.The applied MCA improves the IQ of 64-MDCT-acquired images and reduces the influence of HR on IQ, increasing 64-MDCT validity in the diagnosis of CCS.

Relationship Analysis Between Pericoronary Fat Attenuation Index and Parameters of Single Plaque

Objective The aim of the study is to investigate the relationship between plaque parameters and pericoronary fat attenuation index (FAI). Methods A retrospective collection was performed on 227 patients with coronary heart disease who underwent coronary computed tomography angiography examinations in our hospital from May 2021 to April 2023, with a total of 254 right coronary or left anterior descending coronary arteries exhibiting solitary plaques within the FAI measurement area. Based on whether the proximal coronary FAI value was ≥ −70.0 HU, patients and coronary arteries were divided into FAI-positive group (67 cases, 73 coronary arteries) and FAI-negative group (160 cases, 181 coronary arteries). Quantitative parameters of coronary solitary plaques were collected, including stenosis severity, plaque length, plaque volume, plaque composition ratios, minimal luminal area, and calcification score, as well as qualitative parameters such as plaque types and high-risk plaques. Differences in plaque parameters between the FAI-positive and FAI-negative groups were compared. Results The proportion of positive remodeling in the FAI-positive group (73 coronary arteries) was higher than that in the FAI-negative group (181 coronary arteries) with statistical significance (89.0% vs 78.5%, P = 0.049). Multivariate analysis revealed that positive remodeling was a risk factor for abnormal FAI values in solitary plaques (odds ratio, 2.271, P = 0.049). Conclusions The FAI-positive group had a higher proportion of positive remodeling, and positive remodeling was an independent risk factor for positive FAI values.

Highly Inflamed Non-Calcified Coronary Plaques Sealed with Stents in Patients with Zero Calcium Score – a Case Series and Review of the Literature

Abstract The modern management of coronary artery disease (CAD) uses coronary computed tomography angiography (CCTA) to enhance plaque evaluation and cardiovascular risk assessment. CCTA identifies high-risk plaques, and the latest CT technologies based on calculation of fat attenuation index (FAI) allow assessment of inflammation at the level of the target coronary artery. We present a series of case studies with chest pain and positive CCTA, in whom a significant stenosis was detected in the left anterior descendent coronary artery, and the existence of high-risk, inflamed plaques was documented even in the context of a zero calcium score. A severe narrowing of the left anterior descending artery, exhibiting the pattern of high-risk anatomy, was associated with a very high inflammation depicted by FAI analysis in all three cases, an association that may be extremely dangerous. In this case series, CCTA examination led to immediate stenting of the obstructive stenosis, sealing the dangerous plaque.

A study on the first-order interaction of diagnostic performance of coronary CT angiography-derived fractional flow reserve

Objective: To investigate the effect of image quality, degree of stenosis, calcification, and their first-order interactions on diagnostic performance of coronary computed tomography (CT) angiography-derived fractional flow reserve (CT-FFR). Methods: This is a reanalysis of data from a multi-center retrospective cross-sectional study of CT-FFR in China. A total of 522 patients with suspected or known coronary heart disease [mean age: 61.6 (34.0-83.0) years, 71.8% (354/493) were male] from 11 medical centers including the General Hospital of Eastern Theater Command from May 2015 to October 2019 were enrolled. All patients underwent coronary CT angiography (CCTA), CT-FFR, and invasive FFR examination. Subjective image quality scores of target vessels were recorded on CCTA images, and stenosis was visually assessed at the lesion level. Calcification arc and calcification remodeling index (CRI) were recorded for each lesion. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were compared. Two-way analysis of variance was used to analyze the first-order interaction effects of image quality, degree of stenosis, and calcification. Results: A total of 493 patients with 629 lesions with invasive FFR as a reference were included in the study. The overall sensitivity, specificity, and accuracy of CT-FFR were 80.4%, 93.8%, and 88.6%, respectively. The specificity (95.0% vs. 87.3%, χ2=4.11, P=0.043); accuracy (90.1% vs. 81.9%, χ2=6.22, P=0.013); and NPV (89.7% vs. 80.9%, χ2=4.25, P=0.039) of the group with image quality ≥3 was higher than the group with image quality <3. The degree of stenosis affected the sensitivity, PPV, and NPV of CT-FFR and the calcification arc affected the specificity of CT-FFR (all P>0.05). The specificity (95.8% vs. 90.5%, χ2=4.23, P=0.040); accuracy (91.0% vs. 86.1%, χ2=4.01, P=0.045); and NPV (91.1% vs. 83.8%, χ2=5.10, P=0.024) of the group with CRI<1 were higher than that of the group with CRI≥1. In the subgroup of mild and severe stenosis, no calcification, and CRI<1, the accuracy of CT-FFR with image quality ≥3 points were higher than that with image quality <3 points. The accuracy of CT-FFR in the moderate stenosis group was mainly affected by CRI; the accuracy of CT-FFR in the group with CRI<1 was higher than that in the group with CRI≥1 (after Bonferroni correction, P values between groups were statistically significant). Conclusion: Subjective image quality, degree of stenosis, calcification of lesions, and their first-order interactions can all negatively affect the diagnostic performance of CT-FFR.

Severe periodontal disease is associated with a high-risk phenotype of coronary plaques and a higher severity of coronary atherosclerosis, as assessed by Coronary Computed Tomography Angiography

Abstract Introduction Although incompletely understood, the relationship between coronary heart disease (CHD) and periodontal disease (PD) is well known. A variety of indices can be used to identify periodontal disease. The lack of strict criteria in defining PD represents a challenge in studying the link with CHD. Data from the literature regarding the relationship between different periodontal indices and CHD are extremely limited. Purpose To investigate the link between the PD indices, coronary calcifications and plaque features in patients with CHD who underwent coronary computed tomography angiography (CCTA). Methods The study included 52 patients with PD in whom (CCTA) revealed CHD. All patients underwent a complex oral examination of the periodontal status and the following indices were assessed: gingival index (GI), plaque index (PqI), calculus index (CI), furcation defects (FI), papillary bleeding index (PBI), tooth mobility, clinical attachment loss (CAL) and probing pocket depth (PPD). The indices were determined for the tooth’s 1.6, 2.1, 2.4, 3.6, 4.1, 4.4 and the final score for each index was obtained by summing the values from the six teeth. The association between the periodontal indices and CCTA features (total coronary artery calcium and vulnerability markers) was evaluated using Graph Pad InStat 3.10 software. The p value was set at 0.05 for statistical significance. Results The mean age of the study population was 54.48 ± 12.83 years old with a male to female ratio of 2.46. At the CCTA examination, mean total coronary artery calcium score was 284.20 ± 418.00. An association between the total coronary artery calcium score and GI (r2=0.12, p=0.01), PqI (r2=0.07, p=0.04), CI (r2=0.10, p=0.01), PBI (r2=0.12, p=0.01), PPD (r2=0.08, p=0.03) was found. Assessment of CCTA features of vulnerability at the level of atherosclerotic plaques indicated a strong association between periodontal indexes and plaque vulnerability: p&amp;lt;0.01 for association between CAL and spotty calcification, positive remodeling and napkin ring sign, p=0.009 for association between PPD and spotty calcification, and p=0.01 for association between tooth mobility and positive remodeling. Conclusions In patients with concomitant PD and CHD, the severity of periodontal disease seems to be associated with severity of atherosclerotic disease, assessed by the total coronary artery calcium score. At the same time, severity of PD is associated with a more expressed vulnerability of coronary plaques, indicating a high-risk phenotype of atheromatous plaques in patients with concomitant PD, and systemic inflammation is most likely the link between the two conditions.

Risk rate of major adverse cardiovascular event and charlson comorbidity index in patients undergoing coronary CTA with symptoms suggesting chronic coronary syndrome compared to a reference population

Abstract Background Approximately 12,000 patients annually undergo a coronary computed tomography angiography (CTA) due to symptoms suggestive of chronic coronary syndrome (CCS) in the Western part of Denmark (3.2 million inhabitants) (1). The purpose of this study was to examine the burden of comorbidity and the risk of a major adverse cardiovascular events (MACE)(2) in a large population of patients investigated using coronary CTA compared to a reference population. Methods Patients undergoing first-time coronary CTA from 2008 to October 2022 with symptoms suggestive of CCS were included from the Western Denmark Heart Registry (WDHR). A positive coronary CTA was defined as having a diameter stenosis of ≥ 50% stenosis(1). A reference population was generated matching the CTA population 1:5 based on birth year, gender and municipality using data from national registries. Results are presented in 4 groups; as patients with a positive coronary CTA and their reference population and patients with a negative coronary CTA and their reference population. Information on MACE was obtained from the national patient registry as a composite of acute myocardial infarct (AMI), stroke, revascularization, cardiovascular mortality and all-cause mortality and calculated as risk rates per 1000 persons one year after the coronary CTA examination. A Charlson comorbidity index (CCI)(3) was also calculated. Results From the WDHR 94,207 patients, (Mean age 58±11, 48% men) with a first-time coronary CTA on indication CCS were included. Among all conducted coronary CTA scans 18,290 (19.4%) were positive (&amp;gt;50% stenosis). In the analyses of CCI 90,074 patients from the WDHR were matched with 450,345 controls. Patients with both a positive and negative coronary CTA examination had significantly higher CCI than their matching reference population (Table 1). In table 2 risk rates of a MACE are presented after one year following the coronary CTA examination; in this analysis 53,610 patients from the WDHR were matched with 268,040 controls. The risk of AMI was higher among both patients with and without suspected stenosis compared to their respective reference population., However, the risk of all-cause mortality was higher in both the reference populations. The risk rate of revascularization after a positive coronary CTA was, as could be expected, much higher compared to all other groups as this was a result of the examination. Conclusion In this large study of patients referred to coronary CTA we found a higher incidence of AMI and revascularization in both patients with and with suspected stenoses compared to a reference population. However, we also found a lower mortality rate in both groups compared to a reference population. This could indicate a more well treated population, but a direct causality cannot be concluded from this study and further research is needed to better understand this association.Charlson comorbidity indexMACE events

Exploring the Associations Between Non-Traditional Lipid Parameters and Epicardial Adipose Tissue Volume.

The aim of this retrospective study was to determine the relationship between non-traditional lipid parameters and epicardial adipose tissue (EAT). A total of 770 patients with coronary computed tomography angiography examinations were included. The non-traditional lipid parameters included the atherogenic index of plasma (AIP), the atherogenic coefficient (AC), monocyte to high-density lipoprotein cholesterol (HDL-C) ratio (MHR), and lipoprotein combined index (LCI). To investigate the association between non-conventional lipid markers and the EAT-volume (EAT-v), a univariate and multivariate analyses were conducted. The receiver operating characteristic (ROC) analysis was used to compare the predictive ability among the four non-traditional lipid parameters. In the univariate analysis, we identified factors that might have effects on EAT-v (all P<.05) and adjusted for these in the multivariate analysis. We found that except for MHR, other non-traditional lipid parameters were still associated with high EAT-v after adjustment (all P<.05). In the ROC analysis, the area under the curve (AUC) of AIP was greater than that of other non-traditional lipid parameters and lipid profiles. There was an association between both non-traditional lipid parameters and EAT-v. After adjustment, the AIP remained an independent predictor of EAT-v and it outperformed other non-traditional lipid parameters.

Analysis of coronary computed tomography angiography-derived pericoronary fat attenuation index characteristics in the diagnostic assessment of patients with Takayasu arteritis.

The clinical value of pericoronary adipose tissue in assessing Takayasu arteritis (TAK) with coronary artery involvement (CAI) is yet to be determined. The purpose of this study was to investigate the characteristics of pericoronary fat attenuation index (FAI) derived from coronary computed tomography angiography (CTA) in patients with TAK. This is a retrospective study involving enrollment of 111 consecutive patients (mean age, 33.92±12.48 years) who were diagnosed as TAK, of which 52 patients had coronary artery involvement (TAK-CAI) and 59 patients without coronary artery involvement (TAK-nonCAI). Based on the extent of coronary artery lesion, the TAK-CAI group was further classified into localized group (n=25) and diffused group (n=27). Furthermore, patients with TAK were divided into active group (n=33) and inactive group (n=78). Meanwhile, 51 gender-matched individuals with normal appearance in coronary CTA examination were enrolled as the control group. The pericoronary FAI was quantitatively evaluated on each coronary CTA examination groups. The diagnostic value of pericoronary FAI was determined using the area under the curve (AUC) of the receiver operating characteristic. A higher pericoronary FAI was found in TAK-nonCAI group than control group with normal coronary arteries (P<0.001). Multivariate analysis showed that the FAI is an independent risk factor for coronary involvement in TAK patients [odds ratio (OR): 1.23, 95% confidence interval (CI): 1.13-1.35, P<0.001]. With the best cut-off value of -86.50, the pericoronary FAI identified coronary involvement with 67.8% sensitivity and 74.5% specificity (AUC: 0.794, 95% CI: 0.713-0.875, P<0.001). Multivariate analysis showed that the pericoronary FAI is an independent risk factor for determination of active TAK patients (OR: 1.57, 95% CI: 1.25-1.97, P<0.001). With the best cut-off value of -79.50, the pericoronary FAI identified active inflammation with 93.9% sensitivity and 74.4% specificity (AUC: 0.911, 95% CI: 0.860-0.962, P<0.001). Coronary CTA-derived FAI is significantly increased in patients with TAK and can be used as a reliable biomarker to distinguish TAK patients from those with normal coronary arteries, and determine the extent of TAK inflammation.

Radiation Exposure Dose Reduction according to ECG Window Acquisition Range Setting in Coronary Artery CT Angiography

This study was to reduce the radiation exposure dose of recipients by setting the ECG window acquisition range appropriately during Coronary artery CT Angiography (CCTA) examination. Group 1 (control group) opened ECG window acquisition range to 65% to 75% using the relative delay scan (RDS), which is divided by percentage (%) based on the R-R peak interval of recipient's Average HR, and tested by setting pitch to auto. Group 2 fixed the pitch according to the recipient's HR under the same conditions as Group 1. Group 3 used absolute delay scan (ADS), which is divided by milliseconds (ms) based on the R-R peak interval of recipient's Average HR and fixed the ECG window acquisition range by 60 ms intervals. Group 4 narrowed the scan range from 68% to 73% under the same conditions as Group 1. The values of the mean, minimum, maximum, the volume CT dose index (CTDIvol), and dose length product (DLP) were compared. The exposure dose was reduced by more than 10% when acquired with ADS, not RDS mainly obtained from CCTA. Also, compared to the existing RDS, when the scan range was reduced by half, it was reduced by more than 20%. In conclusion, In CCTA testing, setting the ECG window acquisition range to 5% or 60 ms can reduce radiation exposure without compromising the best phase image acquisition and image quality.

Development and Validation of CCTA-based Radiomics Signature for Predicting Coronary Plaques With Rapid Progression.

Rapid plaque progression (RPP) is associated with a higher risk of acute coronary syndromes compared with gradual plaque progression. We aimed to develop and validate a coronary computed tomography angiography (CCTA)-based radiomics signature (RS) of plaques for predicting RPP. A total of 214 patients who underwent serial CCTA examinations from 2 tertiary hospitals (development group, 137 patients with 164 lesions; validation group, 77 patients with 101 lesions) were retrospectively enrolled. Conventional CCTA-defined morphological parameters (eg, high-risk plaque characteristics and plaque burden) and radiomics features of plaques were analyzed. RPP was defined as an annual progression of plaque burden ≥1.0% on lesion-level at follow-up CCTA. RS was built to predict RPP using XGBoost method. RS significantly outperformed morphological parameters for predicting RPP in both the development group (area under the receiver operating characteristic curve, 0.82 versus 0.74; P=0.04) and validation group (area under the receiver operating characteristic curve, 0.81 versus 0.69; P=0.04). Multivariable analysis identified RS (odds ratio, 2.35 [95% CI, 1.32-4.46]; P=0.005) as an independent predictor of subsequent RPP in the validation group after adjustment of morphological confounders. Unlike unchanged RS in the non-RPP group, RS increased significantly in the RPP group at follow-up in the whole dataset (P<0.001). The proposed CCTA-based RS had a better discriminative value to identify plaques at risk of rapid progression compared with conventional morphological plaque parameters. These data suggest the promising utility of radiomics for predicting RPP in a low-risk group on CCTA.

Risk factors for high CAD-RADS scoring in CAD patients revealed by machine learning methods: a retrospective study.

This study aimed to investigate a variety of machine learning (ML) methods to predict the association between cardiovascular risk factors and coronary artery disease-reporting and data system (CAD-RADS) scores. This is a retrospective cohort study. Demographical, cardiovascular risk factors and coronary CT angiography (CCTA) characteristics of the patients were obtained. Coronary artery disease (CAD) was evaluated using CAD-RADS score. The stenosis severity component of the CAD-RADS was stratified into two groups: CAD-RADS score 0-2 group and CAD-RADS score 3-5 group. CAD-RADS scores were predicted with random forest (RF), k-nearest neighbors (KNN), support vector machines (SVM), neural network (NN), decision tree classification (DTC) and linear discriminant analysis (LDA). Prediction sensitivity, specificity, accuracy and area under the curve (AUC) were calculated. Feature importance analysis was utilized to find the most important predictors. A total of 442 CAD patients with CCTA examinations were included in this study. 234 (52.9%) subjects were CAD-RADS score 0-2 group and 208 (47.1%) were CAD-RADS score 3-5 group. CAD-RADS score 3-5 group had a high prevalence of hypertension (66.8%), hyperlipidemia (50%) and diabetes mellitus (DM) (35.1%). Age, systolic blood pressure (SBP), mean arterial pressure, pulse pressure, pulse pressure index, plasma fibrinogen, uric acid and blood urea nitrogen were significantly higher (p<0.001), and high-density lipoprotein (HDL-C) lower (p<0.001) in CAD-RADS score 3-5 group compared to the CAD-RADS score 0-2 group. Nineteen features were chosen to train the models. RF (AUC = 0.832) and LDA (AUC = 0.81) outperformed SVM (AUC = 0.772), NN (AUC = 0.773), DTC (AUC = 0.682), KNN (AUC = 0.707). Feature importance analysis indicated that plasma fibrinogen, age and DM contributed most to CAD-RADS scores. ML algorithms are capable of predicting the correlation between cardiovascular risk factors and CAD-RADS scores with high accuracy.

Deep Learning-Based Motion Correction in Projection Domain for Coronary Computed Tomography Angiography: A Clinical Evaluation.

This study aimed to evaluate the clinical performance of a deep learning-based motion correction algorithm (MCA) in projection domain for coronary computed tomography angiography (CCTA). A total of 192 patients who underwent CCTA examinations were included and divided into 2 groups based on the average heart rate (HR): group 1, 82 patients with HR of <75 beats per minute; group 2, 110 patients with HR of ≥75 beats per minute. The CCTA images were reconstructed with and without MCA. The subjective image quality was graded in terms of vessel visualization, sharpness, diagnostic confidence, and overall image quality using a 5-point scale, where cases with all scores of ≥3 were deemed interpretable. Objective image quality was measured through signal-to-noise ratio and contrast-to-noise ratio in regions relative to the vessels. The image quality scores for 2 reconstructions and effective dose between 2 groups were compared. The mean effective dose was similar between 2 groups. Neither group showed significant difference on objective image quality for 2 reconstructions. Images reconstructed with and without MCA were both found interpretable for group 1, whereas the subjective image quality was significantly improved by the MCA for all 4 metrics in group 2, with the interpretability increased from 80.91% to 99.09%. Compared with group 1, group 2 showed similar interpretability and diagnostic confidence, despite inferior overall image quality. In CCTA examinations, the deep learning-based MCA is capable of improving the image quality and diagnostic confidence for patients with increased HR to a similar level as for those with low HR.

Predicting coronary plaque progression with conventional plaque parameters and radiomics features derived from coronary CT angiography.

To determine the value of combining conventional plaque parameters and radiomics features derived from coronary computed tomography angiography (CCTA) for predicting coronary plaque progression. Clinical data and CCTA images of 400 patients who underwent at least two CCTA examinations between January 2009 and August 2020 were analyzed retrospectively. Diameter stenosis, total plaque volume and burden, calcified plaque volume and burden, noncalcified plaque volume and burden (NCPB), pericoronary fat attenuation index (FAI), and other conventional plaque parameters were recorded. The patients were assigned to a training cohort (n = 280) and a validation cohort (n = 120) in a 7:3 ratio using a stratified random splitting method. The area under the receiver operating characteristics curve (AUC) was used to evaluate the predictive abilities of conventional parameters (model 1), radiomics features (model 2), and their combination (model 3). FAI and NCPB were identified as independent risk factors for coronary plaque progression in the training cohort. Both model 2 (training cohort AUC: 0.814, p < 0.001; validation cohort AUC: 0.729, p = 0.288) and model 3 (training cohort AUC: 0.824, p < 0.001; validation cohort AUC: 0.758, p = 0.042) had better diagnostic performances in predicting plaque progression than model 1 (training cohort AUC: 0.646; validation cohort AUC: 0.654). Moreover, model 3 was slightly higher than model 2, although not statistically significant. The combination of conventional coronary plaque parameters and CCTA-derived radiomics features had a better ability to predict plaque progression than conventional parameters alone. The conventional coronary plaque characteristics such as noncalcified plaque burden, pericoronary fat attenuation index, and radiomics features derived from CCTA can identify plaques prone to progression, which is helpful for further clinical decision-making of coronary artery disease. • FAI and NCPB were identified as independent risk factors for predicting plaque progression. • Coronary plaque radiomics features were more advantageous than conventional parameters in predicting plaque progression. • The combination of conventional coronary plaque parameters and radiomics features could significantly improve the predictive ability of plaque progression over conventional parameters alone.

Assessment of Image Quality of Coronary CT Angiography Using Deep Learning-Based CT Reconstruction: Phantom and Patient Studies.

In coronary computed tomography angiography (CCTA), the main issue of image quality is noise in obese patients, blooming artifacts due to calcium and stents, high-risk coronary plaques, and radiation exposure to patients. To compare the CCTA image quality of deep learning-based reconstruction (DLR) with that of filtered back projection (FBP) and iterative reconstruction (IR). This was a phantom study of 90 patients who underwent CCTA. CCTA images were acquired using FBP, IR, and DLR. In the phantom study, the aortic root and the left main coronary artery in the chest phantom were simulated using a needleless syringe. The patients were classified into three groups according to their body mass index. Noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured for image quantification. A subjective analysis was also performed for FBP, IR, and DLR. According to the phantom study, DLR reduced noise by 59.8% compared to FBP and increased SNR and CNR by 121.4% and 123.6%, respectively. In a patient study, DLR reduced noise compared to FBP and IR. Furthermore, DLR increased the SNR and CNR more than FBP and IR. In terms of subjective scores, DLR was higher than FBP and IR. In both phantom and patient studies, DLR effectively reduced image noise and improved SNR and CNR. Therefore, the DLR may be useful for CCTA examinations.