Objectives To determine normal pericoronary adipose tissue mean attenuation (PCATMA) values for left the anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA) in patients without plaques on coronary CT angiography (cCTA), taking into account tube voltage influence. Methods This retrospective study included 192 patients (76 (39.6%) men; median age 49 years (range, 19–79)) who underwent cCTA with third-generation dual-source CT for the suspicion of CAD between 2015 and 2017. We selected patients without plaque on cCTA. PCATMA was measured semi-automatically on cCTA images in the proximal segment of the three main coronary arteries with 10 mm length. Paired t-testing was used to compare PCATMA between combinations of two coronary arteries within each patient, and one-way ANOVA testing was used to compare PCATMA in different kV groups. Results The overall mean ± standard deviation (SD) PCATMA was − 90.3 ± 11.1 HU. PCATMA in men was higher than that in women: − 88.5 ± 10.5 HU versus − 91.5 ± 11.3 HU (p = 0.001). PCATMA of LAD, LCX, and RCA was − 92.4 ± 11.6 HU, − 88.4 ± 9.9 HU, and − 90.2 ± 11.4 HU, respectively. Pairwise comparison of the arteries showed significant difference in PCATMA: LAD and LCX (p < 0.001), LAD and RCA (p = 0.009), LCX and RCA (p = 0.033). PCATMA of the 70 kV, 80 kV, 90 kV, 100 kV, and 120 kV groups was − 95.6 ± 9.6 HU, − 90.2 ± 11.5 HU, − 87.3 ± 9.9 HU, − 82.7 ± 6.2 HU, and − 79.3 ± 6.8 HU, respectively (p < 0.001). Conclusions In patients without plaque on cCTA, PCATMA varied by tube voltage, with minor differences in PCATMA between coronary arteries (LAD, LCX, RCA). PCATMA values need to be interpreted taking into account tube voltage setting. Key Points • In patients without plaque on cCTA, PCATMAdiffers slightly by coronary artery (LAD, LCX, RCA). • Tube voltage of cCTA affects PCATMAmeasurement, with mean PCATMAincreasing linearly with increasing kV. • For longitudinal cCTA analysis of PCATMA, the use of equal kV setting is strongly recommended.
Objectives To investigate the association of pericoronary adipose tissue mean attenuation (PCATMA) with coronary artery disease (CAD) characteristics on coronary computed tomography angiography (CCTA). Methods We retrospectively investigated 165 symptomatic patients who underwent third-generation dual-source CCTA at 70kVp: 93 with and 72 without CAD (204 arteries with plaque, 291 without plaque). CCTA was evaluated for presence and characteristics of CAD per artery. PCATMA was measured proximally and across the most severe stenosis. Patient-level, proximal PCATMA was defined as the mean of the proximal PCATMA of the three main coronary arteries. Analyses were performed on patient and vessel level. Results Mean proximal PCATMA was −96.2 ± 7.1 HU and −95.6 ± 7.8HU for patients with and without CAD (p = 0.644). In arteries with plaque, proximal and lesion-specific PCATMA was similar (−96.1 ± 9.6 HU, −95.9 ± 11.2 HU, p = 0.608). Lesion-specific PCATMA of arteries with plaque (−94.7 HU) differed from proximal PCATMA of arteries without plaque (−97.2 HU, p = 0.015). Minimal stenosis showed higher lesion-specific PCATMA (−94.0 HU) than severe stenosis (−98.5 HU, p = 0.030). Lesion-specific PCATMA of non-calcified, mixed, and calcified plaque was −96.5 HU, −94.6 HU, and −89.9 HU (p = 0.004). Vessel-based total plaque, lipid-rich necrotic core, and calcified plaque burden showed a very weak to moderate correlation with proximal PCATMA. Conclusions Lesion-specific PCATMA was higher in arteries with plaque than proximal PCATMA in arteries without plaque. Lesion-specific PCATMA was higher in non-calcified and mixed plaques compared to calcified plaques, and in minimal stenosis compared to severe; proximal PCATMA did not show these relationships. This suggests that lesion-specific PCATMA is related to plaque development and vulnerability. Key Points • In symptomatic patients undergoing CCTA at 70 kVp, PCATMAwas higher in coronary arteries with plaque than those without plaque. • PCATMAwas higher for non-calcified and mixed plaques compared to calcified plaques, and for minimal stenosis compared to severe stenosis. • In contrast to PCATMAmeasurement of the proximal vessels, lesion-specific PCATMAshowed clear relationships with plaque presence and stenosis degree.
Background The aim of this study was to investigate whether increased severity of coronary artery calcium (CAC), an imaging biomarker of subclinical coronary atherosclerosis, is associated with worse cognitive function independent of cardiovascular risk factors in a large population‐based Dutch cohort with broad age range. Methods and Results A cross‐sectional analysis was performed in 4988 ImaLife participants (aged 45–91 years, 58.3% women) without history of cardiovascular disease. CAC scores were obtained using nonenhanced cardiac computed tomography scanning. The CogState Brief Battery was used to assess 4 cognitive domains: processing speed, attention, working memory, and visual learning based on detection task, identification task, 1‐back task, and 1‐card‐learning task, respectively. Differences in mean scores of each cognitive domain were compared among 4 CAC categories (0, 1–99, 100–399, ≥400) using analysis of covariates to adjust for classical cardiovascular risk factors. Age‐stratified analysis (45–54, 55–64, and ≥65 years) was performed to assess whether the association of CAC severity with cognitive function differed by age. Overall, higher CAC was associated with worse performance on 1‐back task after adjusting for classical cardiovascular risk factors, but CAC was not associated with the other cognitive tasks. Age‐stratified analyses revealed that the association of CAC severity with working memory persisted in participants aged 45 to 54 years, while in the elderly this association lost significance. Conclusions In this Dutch population of ≥45 years, increased CAC severity was associated with worse performance of working memory, independent of classical cardiovascular risk factors. The inverse relationship of CAC score categories with working memory was strongest in participants aged 45 to 54 years.
Pericoronary adipose tissue(PCAT) is the fat deposit surrounding coronary arteries. Although PCATis part of the largerepicardial adipose tissue (EAT) depot, it has different pathophysiological features and roles in the atherosclerosis process.While EAT evaluation has been studied for years, PCAT evaluationis a relatively new concept. PCAT, especially the mean attenuationderived from CT imagesmay be used to evaluate the inflammatory status of coronary arteries non-invasively.The most commonly used measure, PCATMA, is the mean attenuation of adipose tissue of 3 mm thickness around the proximal RCA with a length of 40 mm. PCATMAcan be analyzed on a per-lesion, per-vessel or per-patient basis. Apart from PCATMA, other measures for PCAT have been studied, such asthickness, and volume.Studies have shown associations between PCATMAandanatomicaland functional severity of CAD. PCATMA is associated withplaque components and high-risk plaque features,and can discriminate patients with flow obstructing stenosis and myocardial infarction.Whether PCATMAhas value on an individual patient basis remains to be determined. Furthermore, CT imagingsettings, such as kV levelsand clinical factors such as age and sex affect PCATMAmeasurements, whichcomplicate implementation in clinical practice. For PCATMAto be widely implemented, a standardized methodologyis needed. This review gives an overview of reported PCATmethodologies used in current literature and the potential use cases in clinical practice.
Background The distribution of coronary artery calcium (CAC) across the coronary system increases the ability to predict coronary events compared to traditional CAC scoring alone. Reference values for regional distribution of CAC by age and sex are not yet available for a general European population. Purpose To investigate the distribution of CAC across the coronary arteries by age and sex in the population-based ImaLife study. Methods ImaLife is part of Lifelines, a multi-generational, prospective cohort study with over 167,000 participants from the northern Netherlands. From 2017–2019, 5,531 participants aged 45–84 years underwent non-contrast cardiac CT using third-generation dual-source CT. Total and vessel-specific CAC scores (Agatston's method) were acquired semi-automatically using dedicated software. Participants with a positive CAC score were classified into three groups: total CAC score 1–100, 101–300 and >300. The diffusivity index (equation: 1 – [highest one-vessel CAC/total CAC]) was calculated. The diffusivity index is an expression of the relative distribution of CAC across the coronary arteries. Data were analyzed for the whole population and by sex and age groups. Mann-Whitney U test was used to analyze the diffusity index in men and women. Kruskal-Wallis H tests were performed to test the diffusivity index in different age groups. Results In total 2,376 men (mean age 56.4±7.7 years) and 3,155 women (mean age 56.0±7.5 years) were analyzed. In participants with CAC, 1, 2, 3 or 4 vessels were affected in 523 (22.0%), 560 (17.7%), 371 (15.6%) and 257 (8.1%) of men, respectively, and in 385 (16.2%), 175 (5.5%), 185 (7.8%) and 81 (2.6%) of women, respectively (P<0.001). The number of 1, 2, 3 or 4 vessels affected were significantly different by age (p<0.001). In age category 45–49 years, CAC in 1, 2, 3, and 4 vessels was present in 60.1%, 21.6%, 15.5%, and 2.9%, respectively; for age 74+ years, these percentages were 19.3%, 19.3%, 31.1% and 30.3%, respectively. The number of affected vessels were significantly different in different CAC categories (p<0.001), see Figure. More vessels were affected in higher CAC categories. The median diffusivity index was higher in men than in women (0.10 (IQR: 0–0.36) vs 0 (IQR: 0–0.24), p<0.001) and increased by increasing age. For age categories of 45–49, 50–54, 55–59,60–64, 65–69, 70–74, and >74 years, diffusivity indexs were 0 (IQR: 0–0.12), 0 (IQR: 0–0.22), 0.02 (IQR: 0–0.28), 0.10 (IQR: 0–0.35), 0.16 (IQR: 0–0.42), 0.20 (IQR: 0–0.44), and 0.28 (IQR: 0.03–0.45) (p<0.001). Conclusions In this Dutch population-based study, male participants had higher prevalence of CAC with higher number of involved vessels, and a higher diffusivity index compared to women. For both sexes, involved vessels and diffusivity index increased with age. The reference values of this regional distribution of CAC in a European population can assist in risk categorization of cardiovascular events. The CAC distribution in ImaLife Funding Acknowledgement Type of funding source: Other. Main funding source(s): Siemens Healthineers
Background: Epicardial adipose tissue (EAT) locates between the visceral pericardium and myocardium and the EAT volume is correlated with cardiovascular risk. Nowadays, many deep learning-based automated EAT segmentation and quantification methods in the U-net family were developed to reduce the workload for radiologists. However, most of the works were based on private or small dataset with di↵erent label types. Thus, their reproducibility is relatively low and comparison of their performance is difficult. Methods: In this work, we comparably studied and evaluated the state-of-the-art segmentation methods, and o↵ered future directions. Our work is based on a dataset of 154 non-contrast CT scans from the ROBINSCA study with two types of labels: (a) region inside the pericardium and (b) pixel-wise EAT labels. We selected four advanced methods from the U-net family: 3D U-net, 3D attention U-net, an extended 3D attention U-net, and U-net++. For evaluation, we did both four-fold cross-validation and hold-out tests. Agreement between the automatic segmentation/quantification and the manual quantification was evaluated with the Pearson correlation and the Bland-Altman analysis.Results: Generally, the models trained with label type (a) showed better performance compared to models trained with label type (b). The U-net++ model trained with label type (a) showed the best performance of segmentation and quantification. The U-net++ model trained with label type (a) efficiently provides better EAT segmentation results(Hold-out test: DCS=80.18 ± 0.20%, mIoU=67.13 ± 0.39%, sensitivity=81.47 ± 0.43%, specificity=99.64 ± 0.00%, Pearson correlation=0.9405) and EAT volume compared to the other U-net-based networks and the recent EAT segmentation method.Conclusions: 3D convolutional neural networks do not always perform better than 2D convolutional neural networks in the EAT segmentation and quantification.And labels of the region inside the pericardium are helpful to train more accurate EAT segmentation models. Deep learning-based methods have the potential to provide good EAT segmentation and quantification.
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