Feasibility of measuring pericoronary fat from precontrast scans: Effect of iodinated contrast on pericoronary fat attenuation

Coronary inflammation on chest computed tomography and COVID-19 mortality

OBJECTIVES

The main factors associated with coronavirus disease-19 (COVID-19) mortality are age, comorbidities, pattern of inflammatory response, and SARS-CoV-2 lineage involved in infection. However, the clinical course of the disease is extremely heterogeneous, and reliable biomarkers predicting adverse prognosis are lacking. Our aim was to elucidate the prognostic role of a novel marker of coronary artery disease inflammation, peri-coronary adipose tissue attenuation (PCAT), available from high-resolution chest computed tomography (HRCT) in COVID-19 patients with severe disease requiring hospitalization.

METHODS

Two distinct groups of patients were admitted to Parma University Hospital in Italy with COVID-19 in March 2020 and March 2021 (first- and third-wave peaks of the COVID-19 pandemic in Italy, with the prevalence of wild-type and B.1.1.7 SARS-CoV-2 lineage, respectively) were retrospectively enrolled. The primary endpoint was in-hospital mortality. Demographic, clinical, laboratory, HRCT data, and coronary artery HRCT features (coronary calcium score and PCAT attenuation) were collected to show which variables were associated with mortality.

RESULTS

Among the 769 patients enrolled, 555 (72%) were discharged alive, and 214 (28%) died. In multivariable logistic regression analysis age (p < 0.001), number of chronic illnesses (p < 0.001), smoking habit (p = 0.006), P/F ratio (p = 0.001), platelet count (p = 0.002), blood creatinine (p < 0.001), non-invasive mechanical ventilation (p < 0.001), HRCT visual score (p < 0.001), and PCAT (p < 0.001), but not the calcium score, were independently associated with in-hospital mortality.

CONCLUSION

Coronary inflammation, measured with PCAT on non-triggered HRCT, appeared to be independently associated with higher mortality in patients with severe COVID-19, while the pre-existent coronary atherosclerotic burden was not associated with adverse outcomes after adjustment for covariates.

CLINICAL RELEVANCE STATEMENT

The current study demonstrates that a relatively simple measurement, peri-coronary adipose tissue attenuation (PCAT), available ex-post from standard high-resolution computed tomography, is strongly and independently associated with in-hospital mortality.

KEY POINTS

• Coronary inflammation can be measured by the attenuation of peri-coronary adipose tissue (PCAT) on high-resolution CT (HRCT) without contrast media. • PCAT is strongly and independently associated with in-hospital mortality in SARS-CoV-2 patients. • PCAT might be considered an independent prognostic marker in COVID-19 patients if confirmed in other studies.

Pericoronary Adipose Tissue Density, Inflammation, and Subclinical Coronary Artery Disease Among People With HIV in the REPRIEVE Cohort

BACKGROUND

Pericoronary adipose tissue (PCAT) may influence plaque development through inflammatory mechanisms. We assessed PCAT density, as a measure of pericoronary inflammation, in relationship to coronary plaque among people with human immunodeficiency virus (HIV [PWH]) and to a matched control population.

METHODS

In this baseline analysis of 727 participants of the Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) Mechanistic Substudy, we related computed tomography-derived PCAT density to presence and extent (Leaman score) of coronary artery disease (CAD), noncalcified plaque, coronary artery calcium (CAC), and vulnerable plaque features using multivariable logistic regression analyses. We further compared the PCAT density between PWH and age, sex, body mass index, CAC score, and statin use-matched controls from the community-based Framingham Heart Study (N = 464), adjusting for relevant clinical covariates.

RESULTS

Among 727 REPRIEVE participants (age 50.8 ± 5.8 years; 83.6% [608/727] male), PCAT density was higher in those with (vs without) coronary plaque, noncalcified plaque, CAC >0, vulnerable plaque, and high CAD burden (Leaman score >5) (P < .001 for each comparison). PCAT density related to prevalent coronary plaque (adjusted odds ratio [per 10 HU]: 1.44; 95% confidence interval, 1.22-1.70; P < .001), adjusted for clinical cardiovascular risk factors, body mass index, and systemic immune/inflammatory biomarkers. Similarly, PCAT density related to CAC >0, noncalcified plaque, vulnerable plaque, and Leaman score >5 (all P ≤ .002). PCAT density was greater among REPRIEVE participants versus Framingham Heart Study (-88.2 ± 0.5 HU versus -90.6 ± 0.4 HU; P < .001).

CONCLUSIONS

Among PWH in REPRIEVE, a large primary cardiovascular disease prevention cohort, increased PCAT density independently associated with prevalence and severity of coronary plaque, linking increased coronary inflammation to CAD in PWH.

Validation and clinical impact of novel pericoronary adipose tissue measurement on ECG-gated non-contrast chest CT

BACKGROUND AND AIMS

We aimed to develop a method for quantifying pericoronary adipose tissue (PCAT) on electrocardiogram (ECG)-gated non-contrast CT (NC-PCAT) and validate its efficacy and prognostic value.

METHODS

We retrospectively studied two independent cohorts. PCAT was quantified conventionally. NC-PCAT was defined as the mean CT value of epicardial fat tissue adjacent to right coronary artery ostium on ECG-gated non-contrast CT. In cohort 1 (n = 300), we evaluated the correlation of two methods and the association between NC-PCAT and CT-verified high-risk plaque (HRP). We dichotomized cohort 2 (n = 333) by the median of NC-PCAT, and assessed the prognostic value of NC-PCAT for primary endpoint (all-cause death and non-fatal myocardial infarction) by Cox regression analysis. The median duration of follow-up was 2.9 years.

RESULTS

NC-PCAT was correlated with PCAT (r = 0.68, p<0.0001). In multivariable logistic regression analysis, high NC-PCAT (OR:1.06; 95%CI:1.03-1.10; p = 0.0001), coronary artery calcium score (CACS) (OR:1.01 per 10 CACS increase, 95%CI:1.00-1.02; p = 0.013), and current smoking (OR:2.58; 95%CI:1.03-6.49; p = 0.044) were independent predictors of HRP. Among patients with CACS>0 (n = 193), NC-PCAT (OR:1.06; 95%CI:1.03-1.10; p = 0.0002), current smoking (OR:3.02; 95%CI:1.17-7.82; p = 0.027), and male sex (OR:2.81; 95%CI:1.06-7.48; p = 0.028) were independent predictors of HRP, whereas CACS was not (p = 0.15). Multivariable Cox regression analysis revealed high NC-PCAT as an independent predictor of the primary endpoint, even after adjustment for sex and age (HR:4.3; 95%CI:1.2-15.2; p = 0.012).

CONCLUSIONS

There was a positive correlation between NC-PCAT and PCAT, with high NC-PCAT significantly associated with worse clinical outcome (independent of CACS) as well as presence of HRP.

Evaluation of pericoronary adipose tissue attenuation on CT

Pericoronary adipose tissue (PCAT) is the fat deposit surrounding coronary arteries. Although PCAT is part of the larger epicardial adipose tissue (EAT) depot, it has different pathophysiological features and roles in the atherosclerosis process. While EAT evaluation has been studied for years, PCAT evaluation is a relatively new concept. PCAT, especially the mean attenuation derived from CT images may be used to evaluate the inflammatory status of coronary arteries non-invasively. The most commonly used measure, PCAT_MA, is the mean attenuation of adipose tissue of 3 mm thickness around the proximal right coronary artery with a length of 40 mm. PCAT_MA can be analyzed on a per-lesion, per-vessel or per-patient basis. Apart from PCAT_MA, other measures for PCAT have been studied, such as thickness, and volume. Studies have shown associations between PCAT_MA and anatomical and functional severity of coronary artery disease. PCAT_MA is associated with plaque components and high-risk plaque features, and can discriminate patients with flow obstructing stenosis and myocardial infarction. Whether PCAT_MA has value on an individual patient basis remains to be determined. Furthermore, CT imaging settings, such as kV levels and clinical factors such as age and sex affect PCAT_MA measurements, which complicate implementation in clinical practice. For PCAT_MA to be widely implemented, a standardized methodology is needed. This review gives an overview of reported PCAT methodologies used in current literature and the potential use cases in clinical practice.

Segmentation and volume quantification of epicardial adipose tissue in computed tomography images

BACKGROUND

Many cardiovascular diseases are closely related to the composition of epicardial adipose tissue (EAT). Accurate segmentation of EAT can provide a reliable reference for doctors to diagnose the disease. The distribution and composition of EAT often have significant individual differences, and the traditional segmentation methods are not effective. In recent years, deep learning method has been gradually introduced into EAT segmentation task.

PURPOSE

The existing EAT segmentation methods based on deep learning have a large amount of computation and the segmentation accuracy needs to be improved. Therefore, the purpose of this paper is to develop a lightweight EAT segmentation network, which can obtain higher segmentation accuracy with less computation and further alleviate the problem of false positive segmentation.

METHODS

Firstly, the obtained Computed Tomography (CT) was preprocessed. That is, the threshold range of EAT was determined to be (-190, -30) HU according to prior knowledge, and the non-adipose pixels were excluded by threshold segmentation to reduce the difficulty of training. Secondly, the image obtained after thresholding was input into the lightweight RDU-Net network to perform the training, validating, and testing process. RDU-Net uses a residual multi-scale dilated convolution block in order to extract a wider range of information without changing the current resolution. At the same time, the form of residual connection is adopted to avoid the problem of gradient expansion or gradient explosion caused by too deep network, which also makes the learning easier. In order to optimize the training process, this paper proposes PNDiceLoss, which takes both positive and negative pixels as learning targets, fully considers the class imbalance problem and appropriately highlights the status of positive pixels.

RESULTS

In this paper, 50 CCTA images were randomly selected from the hospital, and the commonly used Dice similarity coefficient (DSC), Jaccard similarity (JS), Accuracy (ACC), Specificity (SP), Precision (PC), and Pearson correlation coefficient are used as evaluation metrics. Bland-Altman analysis results show that the extracted EAT volume is consistent with the actual volume. Compared with the existing methods, the segmentation results show that the proposed method achieves better performance on these metrics, achieving the DSC of 0.9262. The number of false positive pixels has been reduced by more than half. Pearson correlation coefficient reached 0.992, and linear regression coefficient reached 0.977 when measuring the volume of EAT obtained. In order to verify the effectiveness of the proposed method, experiments are carried out in the cardiac fat database of VisualLab. On this database, the proposed method also achieved good results, and the DSC value reached 0.927 in the case of only 878 slices.

CONCLUSIONS

A new method to segment and quantify EAT is proposed. Comprehensive experiments show that compared with some classical segmentation algorithms, the proposed method has the advantages of shorter time-consuming, less memory required for operations, and higher segmentation accuracy. The code is available at https://github.com/lvanlee/EAT_Seg/tree/main/EAT_seg. This article is protected by copyright. All rights reserved.

Association between high oxidized high-density lipoprotein levels and increased pericoronary inflammation determined by coronary computed tomography angiography

BACKGROUND

Impaired high-density lipoprotein (HDL) function is a risk factor for cardiac mortality. We aimed to investigate the association between oxidized HDL (oxHDL) and pericoronary adipose tissue (PCAT) attenuation, a novel imaging biomarker of pericoronary inflammation, by using coronary computed tomography angiography (CTA).

METHODS

A total of 287 outpatients with suspected coronary artery disease who had undergone both oxHDL measurement and coronary CTA were examined. PCAT attenuation values were assessed at the proximal 10-50 mm segments of the right coronary artery on coronary CTA. The presence of significant stenosis (luminal narrowing of >50 %) and high-risk plaque characteristics were also evaluated. Patients were then classified into tertiles according to their oxHDL level: low (n = 95), moderate (n = 96), and high (n = 96) groups.

RESULTS

PCAT attenuation in the high oxHDL group was significantly higher than that in other groups after adjusting for age and apolipoprotein-A-I. Multivariate linear regression analysis revealed that oxHDL was significantly associated with PCAT attenuation in the right coronary artery (β = 3.832, p < 0.001), whereas HDL cholesterol was not. Furthermore, subgroup analyses demonstrated that the association between oxHDL and PCAT attenuation remained significant in older patients (β = 6.367, p < 0.001) and in those with hypertension (β = 4.922, p < 0.011), dyslipidemia (β = 3.264, p = 0.010), diabetes mellitus (β = 4.284, p = 0.015), and significant stenosis (β = 3.075, p = 0.021).

CONCLUSIONS

High oxHDL levels were significantly associated with increased pericoronary inflammation, as assessed using coronary CTA. Our results may explain the association between impaired HDL function and the development of coronary atherosclerosis.

Measurement of epicardial adipose tissue using non-contrast routine chest-CT: a consideration of threshold adjustment for fatty attenuation

Background

Epicardial adipose tissue (EAT) is known as an important imaging indicator for cardiovascular risk stratification. The present study aimed to determine whether the EAT volume (EV) and mean EAT attenuation (mEA) measured by non-contrast routine chest CT (RCCT) could be more consistent with those measured by coronary CT angiography (CCTA) by adjusting the threshold of fatty attenuation.

Methods

In total, 83 subjects who simultaneously underwent CCTA and RCCT were enrolled. EV and mEA were quantified by CCTA using a threshold of (N30) (− 190 HU, − 30 HU) as a reference and measured by RCCT using thresholds of N30, N40 (− 190 HU, − 40 HU), and N45 (− 190 HU, − 45 HU). The correlation and agreement of EAT metrics between the two imaging modalities and differences between patients with coronary plaques (plaque ( +)) and without plaques (plaque ( −)) were analyzed.

Results

EV obtained from RCCT showed very strong correlation with the reference (r = 0.974, 0.976, 0.972 (N30, N40, N45), P < 0.001), whereas mEA showed a moderate correlation (r = 0.516, 0.500, 0.477 (N30, N40, N45), P < 0.001). Threshold adjustment was able to reduce the bias of EV, while increase the bias of mEA. Data obtained by CCTA and RCCT both demonstrated a significantly larger EV in the plaque ( +) group than in the plaque ( −) group (P < 0.05). A significant difference in mEA was shown only by RCCT using a threshold of N30 (plaque ( +) vs ( −): − 80.0 ± 4.4 HU vs − 78.0 ± 4.0 HU, P = 0.030). The mEA measured on RCCT using threshold of N40 and N45 showed no significant statistical difference between the two groups (P = 0.092 and 0.075), which was consistent with the result obtained on CCTA (P = 0.204).

Conclusion

Applying more negative threshold, the consistency of EV measurements between the two techniques improves and a consistent result can be obtained when comparing EF measurements between groups, although the bias of mEA increases. Threshold adjustment is necessary when measuring EF with non-contrast RCCT.

Non-contrast CT-based radiomic signature of pericoronary adipose tissue for screening non-calcified plaque

Objective. To develop two combined clinical-radiomics models of pericoronary adipose tissue (PCAT) for the presence and characterization of non-calcified plaques on non-contrast CT scan. Approach. Altogether, 431 patients undergoing Coronary Computed Tomography Angiography from March 2019 to June 2021 who had complete data were enrolled, including 173 patients with non-calcified plaques of the right coronary artery(RCA) and 258 with no abnormality. PCAT was segmented around the proximal RCA on non-contrast CT scan (calcium score acquisition). Two best models were established by screening features and classifiers respectively using FeAture Explorer software. Model 1 distinguished normal coronary arteries from those with non-calcified plaques, and model 2 distinguished vulnerable plaques in non-calcified plaques. Performance was assessed by the area under the receiver operating characteristic curve (AUC-ROC). Main results. 4 and 9 features were selected for the establishment of the radiomics model respectively through Model 1 and 2. In the test group, the AUC values, sensitivity, specificity and accuracy were 0.833%, 78.3%, 80.8%, 76.6% and 0.7467%, 75.0%, 77.8%, 73.5%, respectively. The combined model including radiomics features and independent clinical factors yielded an AUC, sensitivity, specificity and accuracy of 0.896%, 81.4%, 86.5%, 77.9% for model 1 and 0.752%, 75.0%, 77.8%, 73.5% for model 2. Significance. The combined clinical-radiomics models based on non-contrast CT images of PCAT had good diagnostic efficacy for non-calcified and vulnerable plaques.

Influence of Different Segmentations on the Diagnostic Performance of Pericoronary Adipose Tissue

Objective

To investigate the influence of different segmentations on the diagnostic performance of pericoronary adipose tissue (PCAT) CT attenuation and radiomics features for the prediction of ischemic coronary artery stenosis.

Methods

From June 2016 to December 2018, 108 patients with 135 vessels were retrospectively analyzed in the present study. Vessel-based PCAT was segmented along the 40 mm-long proximal segments of three major epicardial coronary arteries, while lesion-based PCAT was defined around coronary lesions. CT attenuation and radiomics features derived from two segmentations were calculated and extracted. The diagnostic performance of PCAT CT attenuation or radiomics models in predicting ischemic coronary stenosis were also compared between vessel-based and lesion-based segmentations.

Results

The mean PCAT CT attenuation was −75.7 ± 9.1 HU and −76.1 ± 8.1 HU (p = 0.395) for lesion-based and vessel-based segmentations, respectively. A strong correlation was found between vessel-based and lesion-based PCAT CT attenuation for all cohort and subgroup analyses (all p < 0.01). A good agreement for all cohort and subgroup analyses was also detected between two segmentations. The diagnostic performance was comparable between vessel-based and lesion based PCAT CT attenuation in predicting ischemic stenosis. The radiomics features of PCAT based on vessel or lesion segmentation can both adequately identify the ischemic stenosis. However, no significant difference was detected between the two segmentations.

Conclusions

The quantitative evaluation of PCAT can be reliably measured both from vessel-based and lesion-based segmentation. Furthermore, the radiomics analysis of PCAT may potentially help predict hemodynamically significant coronary artery stenosis.

Epicardial Adipose Tissue Attenuation and Fat Attenuation Index: Phantom Study and In-Vivo Measurements With Photon-Counting CT

Background: Epicardial adipose tissue (EAT) attenuation is a vascular inflammation marker predictive of adverse cardiac events. Fat attenuation index (FAI) assesses fat attenuation for predefined coronary segments. Photon-counting detector (PCD) CT employs routine virtual monoenergetic image (VMI) reconstructions. VMI energy level may impact EAT attenuation and FAI measurements. Objective: To assess EAT attenuation and FAI measurements at different monoenergetic keV levels in patients undergoing coronary CTA using a first-generation whole-body dual-source PCD CT scanner. Methods: An anthropomorphic phantom at two sizes with a fat-insert was imaged on a first-generation dual-source PCD CT scanner and, as reference, on a conventional energy-integrating detector (EID) CT scanner at 120 kV. Thirty patients (11 women, 19 men; mean age, 48±10 years; Agatston score ≤60) who underwent an ECG-gated unenhanced calcium-scoring scan and contrast-enhanced coronary CTA by PCD CT were retrospectively evaluated. VMI from 55 to 80 keV at 5 keV increments were reconstructed. EAT attenuation was manually measured on unenhanced and contrast-enhanced images. FAI was calculated using semiautomated software. Results: The phantom fat-insert attenuation was -69 HU for the reference EID CT; closest attenuation for PCT CT was observed at 70 keV for small (-69 HU) and large (-70 HU) phantoms. In patients, EAT attenuation increased for unenhanced acquisition from -111±11 HU at 55 keV to -82±9 HU at 80 keV, and for contrast-enhanced acquisition from -104±11 HU at 55 keV to -81±9 HU at 80 keV. Mean attenuation difference between unenhanced and contrast-enhanced scans decreased with increasing keV level (from 7±12 HU to 1±10 HU). FAI increased from -89±8 HU at 55 keV to -77±12 HU at 80 keV for right coronary artery, -95±11 HU at 55 keV to -85±11 HU at 80 keV for left anterior descending artery, and -87±10 HU at 55 keV to -80±12 HU at 80 keV for circumflex artery. Conclusion: EAT attenuation and FAI measurements using PCD CT are impacted by keV level and contrast enhancement. Use of 70 keV provides fat attenuation approximating conventional polychromatic measurements. Clinical impact: The findings may help standardize evaluation of pericoronary inflammation by PCT CT as a measure of patients' cardiac risk.

Current methods to assess mitral annular calcification and its risk factors

INTRODUCTION

Mitral annulus calcification (MAC) is a chronic, non-inflammatory, degenerative mechanism of the fibrous base of the mitral valve. While MAC was originally thought to be an age-related degenerative process, there is evidence that other mechanisms, such as atherosclerosis and abnormal calcium phosphorus metabolism, also contribute to the development of MAC.

AREAS COVERED

This paper summarizes, existing perception of clinically valid definition of MAC and the pathophysiological processes that lead to the development of MAC and the diagnostic implications of this disease entity.

EXPERT OPINION

Minimal evidence exists on the natural history and progression of MAC. Characterization of MAC progression and identification of predisposing risk factors can help to validate hypotheses. MAC is most commonly asymptomatic and incidental finding. Echocardiography is the primary imaging modality for identification and characterization of MAC and associated mitral valve (MV) disease. For patients with an indication for MV surgery, computed tomography (CT) is a complementary imaging modality for MAC. MAC is generally recognized by its characteristic density, location, and shape on echocardiography and CT, unusual variants are sometimes confused with other lesions.

The Emerging Role of CT-Based Imaging in Adipose Tissue and Coronary Inflammation

A large body of evidence arising from recent randomized clinical trials demonstrate the association of vascular inflammatory mediators with coronary artery disease (CAD). Vascular inflammation localized in the coronary arteries leads to an increased risk of CAD-related events, and produces unique biological alterations to local cardiac adipose tissue depots. Coronary computed tomography angiography (CTA) provides a means of mapping inflammatory changes to both epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT) as independent markers of coronary risk. Radiodensity or attenuation of PCAT on coronary CTA, notably, provides indirect quantification of coronary inflammation and is emerging as a promising non-invasive imaging implement. An increasing number of observational studies have shown robust associations between PCAT attenuation and major coronary events, including acute coronary syndrome, and 'vulnerable' atherosclerotic plaque phenotypes that are associated with an increased risk of the said events. This review outlines the biological characteristics of both EAT and PCAT and provides an overview of the current literature on PCAT attenuation as a surrogate marker of coronary inflammation.

Imaging of the Pericoronary Adipose Tissue (PCAT) Using Cardiac Computed Tomography: Modern Clinical Implications

Modern coronary computed tomography angiography (CTA) is the gold standard to visualize the epicardial adipose tissue (EAT) and pericoronary adipose tissue (PCAT). The EAT is a metabolic active fat depot enclosed by the visceral pericardium and surrounds the coronary arteries. In disease states with increased EAT volume and dysfunctional adipocytes, EAT secretes an increased amount of adipocytokines and the resulting imbalance of proinflammatory and anti-inflammatory mediators potentially causes atherogenic effects on the coronary vessel wall in a paracrine way ("outside-to-inside" signaling). These EAT-induced atherogenic effects are reported to increase the risk for the development of coronary artery disease, myocardial ischemia, high-risk plaque features, and future major adverse cardiac events. Coronary inflammation plays a key role in the development and progression of coronary artery disease; however, its noninvasive detection remains challenging. In future, this clinical dilemma might be changed by the CTA-derived analysis of the PCAT. On the basis of the concept of an "inside-to-outside" signaling between the inflamed coronary vessel wall and the surrounding PCAT recent evidence demonstrates that PCAT computed tomography attenuation especially around the right coronary artery derived from routine CTA is a promising imaging biomarker and "sensor" to noninvasively detect coronary inflammation. This review summarizes the biological and technical principles of CTA-derived PCAT analysis and highlights its clinical implications to improve modern cardiovascular prevention strategies.

The Journal of Cardiovascular Computed Tomography: 2020 Year in review

The purpose of this review is to highlight the most impactful, educational, and frequently downloaded articles published in the Journal of Cardiovascular Computed Tomography (JCCT) for the year 2020. The JCCT reached new records in 2020 for the number of research submissions, published manuscripts, article downloads and social media impressions. The articles in this review were selected by the Editorial Board of the JCCT and are comprised predominately of original research publications in the following categories: Coronavirus disease 2019 (COVID-19), coronary artery disease, coronary physiology, structural heart disease, and technical advances. The Editorial Board would like to thank each of the authors, peer-reviewers and the readers of JCCT for making 2020 one of the most successful years in its history, despite the challenging circumstances of the global COVID-19 pandemic.