Repeatability of quantitative pericoronary adipose tissue attenuation and coronary plaque burden from coronary CT angiography

BACKGROUND: High pericoronary adipose tissue (PCAT) attenuation and non-calcified plaque burden (NCP) measured from coronary CT angiography (CTA) have been implicated in future cardiac events. We aimed to evaluate the interobserver and intraobserver repeatability of PCAT attenuation and NCP burden measurement from CTA, in a sub-study of the prospective SCOT-HEART trial. METHODS: Fifty consecutive CTAs from participants of the CT arm of the prospective SCOT-HEART trial were included. Two experienced observers independently measured PCAT attenuation and plaque characteristics throughout the whole coronary tree from CTA using semi-automatic quantitative software. RESULTS: We analyzed proximal segments in 157 vessels. Intraobserver mean differences in PCAT attenuation and NCP plaque burden were −0.05HU and 0.92% with limits of agreement (LOA) of ±1.54 and ±5.97%. Intraobserver intraclass correlation coefficients (ICC) for PCAT attenuation and NCP burden were excellent (0.999 and 0.978). Interobserver mean differences in PCAT attenuation and NCP plaque burden were 0.13HU [LOA ±1.67HU] and −0.23% (LOA ±9.61%). Interobserver ICC values for PCAT attenuation and NCP burden were excellent (0.998 and 0.944). CONCLUSION: PCAT attenuation and NCP burden on CTA has high intraobserver and interobserver repeatability, suggesting they represent a repeatable and robust method of quantifying cardiovascular risk

Aortic valve imaging using 18F-sodium fluoride: impact of triple motion correction

BACKGROUND: Current (18)F-NaF assessments of aortic valve microcalcification using (18)F-NaF PET/CT are based on evaluations of end-diastolic or cardiac motion-corrected (ECG-MC) images, which are affected by both patient and respiratory motion. We aimed to test the impact of employing a triple motion correction technique (3 × MC), including cardiorespiratory and gross patient motion, on quantitative and qualitative measurements. MATERIALS AND METHODS: Fourteen patients with aortic stenosis underwent two repeat 30-min PET aortic valve scans within (29 ± 24) days. We considered three different image reconstruction protocols; an end-diastolic reconstruction protocol (standard) utilizing 25% of the acquired data, an ECG-gated (four ECG gates) reconstruction (ECG-MC), and a triple motion-corrected (3 × MC) dataset which corrects for both cardiorespiratory and patient motion. All datasets were compared to aortic valve calcification scores (AVCS), using the Agatston method, obtained from CT scans using correlation plots. We report SUV(max) values measured in the aortic valve and maximum target-to-background ratios (TBR(max)) values after correcting for blood pool activity. RESULTS: Compared to standard and ECG-MC reconstructions, increases in both SUV(max) and TBR(max) were observed following 3 × MC (SUV(max): Standard = 2.8 ± 0.7, ECG-MC = 2.6 ± 0.6, and 3 × MC = 3.3 ± 0.9; TBR(max): Standard = 2.7 ± 0.7, ECG-MC = 2.5 ± 0.6, and 3 × MC = 3.3 ± 1.2, all p values ≤ 0.05). 3 × MC had improved correlations (R(2) value) to the AVCS when compared to the standard methods (SUV(max): Standard = 0.10, ECG-MC = 0.10, and 3 × MC = 0.20; TBR(max): Standard = 0.20, ECG-MC = 0.28, and 3 × MC = 0.46). CONCLUSION: 3 × MC improves the correlation between the AVCS and SUV(max) and TBR(max) and should be considered in PET studies of aortic valves using (18)F-NaF. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40658-022-00433-7

Machine-learning with 18F-sodium fluoride PET and quantitative plaque analysis on CT angiography for the future risk of myocardial infarction

Coronary (18)F-sodium fluoride ((18)F-NaF) PET and CT angiography–based quantitative plaque analysis have shown promise in refining risk stratification in patients with coronary artery disease. We combined both of these novel imaging approaches to develop an optimal machine-learning model for the future risk of myocardial infarction in patients with stable coronary disease. Methods: Patients with known coronary artery disease underwent coronary (18)F-NaF PET and CT angiography on a hybrid PET/CT scanner. Machine-learning by extreme gradient boosting was trained using clinical data, CT quantitative plaque analysis, measures and (18)F-NaF PET, and it was tested using repeated 10-fold hold-out testing. Results: Among 293 study participants (65 ± 9 y; 84% male), 22 subjects experienced a myocardial infarction over the 53 (40–59) months of follow-up. On univariable receiver-operator-curve analysis, only (18)F-NaF coronary uptake emerged as a predictor of myocardial infarction (c-statistic 0.76, 95% CI 0.68–0.83). When incorporated into machine-learning models, clinical characteristics showed limited predictive performance (c-statistic 0.64, 95% CI 0.53–0.76) and were outperformed by a quantitative plaque analysis-based machine-learning model (c-statistic 0.72, 95% CI 0.60–0.84). After inclusion of all available data (clinical, quantitative plaque and (18)F-NaF PET), we achieved a substantial improvement (P = 0.008 versus (18)F-NaF PET alone) in the model performance (c-statistic 0.85, 95% CI 0.79–0.91). Conclusion: Both (18)F-NaF uptake and quantitative plaque analysis measures are additive and strong predictors of outcome in patients with established coronary artery disease. Optimal risk stratification can be achieved by combining clinical data with these approaches in a machine-learning model

Three-hour delayed imaging improves assessment of coronary 18F-sodium fluoride PET.

Coronary 18F-sodium fluoride (18F-NaF) PET identifies ruptured plaques in patients with recent myocardial infarction and localizes to atherosclerotic lesions with active calcification. Most studies to date have performed the PET acquisition 1 h after injection. Although qualitative and semiquantitative analysis is feasible with 1-h images, residual blood-pool activity often makes it difficult to discriminate plaques with 18F-NaF uptake from noise. We aimed to assess whether delayed PET performed 3 h after injection improves image quality and uptake measurements. Methods: Twenty patients (67 ± 7 y old, 55% male) with stable coronary artery disease underwent coronary CT angiography (CTA) and PET/CT both 1 h and 3 h after the injection of 266.2 ± 13.3 MBq of 18F-NaF. We compared the visual pattern of coronary uptake, maximal background (blood pool) activity, noise, SUVmax, corrected SUVmax (cSUVmax), and target-to-background (TBR) ratio in lesions defined by CTA on 1-h versus 3-h 18F-NaF PET. Results: On 1-h PET, 26 CTA lesions with 18F-NaF PET uptake were identified in 12 (60%) patients. On 3-h PET, we detected 18F-NaF PET uptake in 7 lesions that were not identified on 1-h PET. The median cSUVmax and TBRs of these lesions were 0.48 (interquartile range [IQR], 0.44-0.51) and 1.45 (IQR, 1.39-1.52), respectively, compared with -0.01 (IQR, -0.03-0.001) and 0.95 (IQR, 0.90-0.98), respectively, on 1-h PET (both P < 0.001). Across the entire cohort, 3-h PET SUVmax was similar to 1-h PET measurements (1.63 [IQR, 1.37-1.98] vs. 1.55 [IQR, 1.43-1.89], P = 0.30), and the background activity was lower (0.71 [IQR, 0.65-0.81] vs. 1.24 [IQR, 1.05-1.31], P < 0.001). On 3-h PET, TBR, cSUVmax, and noise were significantly higher (respectively: 2.30 [IQR, 1.70-2.68] vs. 1.28 [IQR, 0.98-1.56], P < 0.001; 0.38 [IQR, 0.27-0.70] vs. 0.90 [IQR, 0.64-1.17], P < 0.001; and 0.10 [IQR, 0.09-0.12] vs. 0.07 [IQR, 0.06-0.09], P = 0.02). Median cSUVmax and TBR increased by 92% (range, 33%-225%) and 80% (range, 20%-177%), respectively. Conclusion: Blood-pool activity decreases on delayed imaging, facilitating the assessment of 18F-NaF uptake in coronary plaques. Median TBR increases by 80%, leading to the detection of more plaques with significant uptake than are detected using the standard 1-h protocol. A greater than 1-h delay may improve the detection of 18F-NaF uptake in coronary artery plaques.restrictio

Triple-gated motion and blood pool clearance corrections improve reproducibility of coronary 18F-NaF PET

PurposeTo improve the test-retest reproducibility of coronary plaque 18F-sodium fluoride (18F-NaF) positron emission tomography (PET) uptake measurements.MethodsWe recruited 20 patients with coronary artery disease who underwent repeated hybrid PET/CT angiography (CTA) imaging within 3&nbsp;weeks. All patients had 30-min PET acquisition and CTA during a single imaging session. Five PET image-sets with progressive motion correction were reconstructed: (i) a static dataset (no-MC), (ii) end-diastolic PET (standard), (iii) cardiac motion corrected (MC), (iv) combined cardiac and gross patient motion corrected (2 × MC) and, (v) cardiorespiratory and gross patient motion corrected (3 × MC). In addition to motion correction, all datasets were corrected for variations in the background activities which are introduced by variations in the injection-to-scan delays (background blood pool clearance correction, BC). Test-retest reproducibility of PET target-to-background ratio (TBR) was assessed by Bland-Altman analysis and coefficient of reproducibility.ResultsA total of 47 unique coronary lesions were identified on CTA. Motion correction in combination with BC improved the PET TBR test-retest reproducibility for all lesions (coefficient of reproducibility: standard = 0.437, no-MC = 0.345 (27% improvement), standard&nbsp;+&nbsp;BC = 0.365 (20% improvement), no-MC + BC = 0.341 (27% improvement), MC + BC = 0.288 (52% improvement), 2 × MC + BC = 0.278 (57% improvement) and 3 × C + BC = 0.254 (72% improvement), all p &lt; 0.001). Importantly, in a sub-analysis of 18F-NaF-avid lesions with gross patient motion &gt;&nbsp;10&nbsp;mm following corrections, reproducibility was improved by 133% (coefficient of reproducibility: standard = 0.745, 3 × MC = 0.320).ConclusionJoint corrections for cardiac, respiratory, and gross patient motion in combination with background blood pool corrections markedly improve test-retest reproducibility of coronary 18F-NaF PET

Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study

BACKGROUND: Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity. METHODS: This international, multicentre study included nine cohorts of patients undergoing CCTA at 11 sites, who were assigned into training and test sets. Data were retrospectively collected on patients with a wide range of clinical presentations of coronary artery disease who underwent CCTA between Nov 18, 2010, and Jan 25, 2019. A novel deep learning convolutional neural network was trained to segment coronary plaque in 921 patients (5045 lesions). The deep learning network was then applied to an independent test set, which included an external validation cohort of 175 patients (1081 lesions) and 50 patients (84 lesions) assessed by intravascular ultrasound within 1 month of CCTA. We evaluated the prognostic value of deep learning-based plaque measurements for fatal or non-fatal myocardial infarction (our primary outcome) in 1611 patients from the prospective SCOT-HEART trial, assessed as dichotomous variables using multivariable Cox regression analysis, with adjustment for the ASSIGN clinical risk score. FINDINGS: In the overall test set, there was excellent or good agreement, respectively, between deep learning and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0·964) and percent diameter stenosis (ICC 0·879; both p<0·0001). When compared with intravascular ultrasound, there was excellent agreement for deep learning total plaque volume (ICC 0·949) and minimal luminal area (ICC 0·904). The mean per-patient deep learning plaque analysis time was 5·65 s (SD 1·87) versus 25·66 min (6·79) taken by experts. Over a median follow-up of 4·7 years (IQR 4·0–5·7), myocardial infarction occurred in 41 (2·5%) of 1611 patients from the SCOT-HEART trial. A deep learning-based total plaque volume of 238·5 mm(3) or higher was associated with an increased risk of myocardial infarction (hazard ratio [HR] 5·36, 95% CI 1·70–16·86; p=0·0042) after adjustment for the presence of deep learning-based obstructive stenosis (HR 2·49, 1·07–5·50; p=0·0089) and the ASSIGN clinical risk score (HR 1·01, 0·99–1·04; p=0·35). INTERPRETATION: Our novel, externally validated deep learning system provides rapid measurements of plaque volume and stenosis severity from CCTA that agree closely with expert readers and intravascular ultrasound, and could have prognostic value for future myocardial infarction

2,2-Diamino-4-[(3,5-di-O-acetyl-2-deoxy-.beta.-D-erythro- pentofuranosyl)amino]-5-(2H)-oxazolone: a Novel and Predominant Radical Oxidation Product of 3',5'-Di-O-acetyl-2'-deoxyguanosine

International audienc

Whole-vessel coronary 18F-sodium fluoride PET for assessment of the global coronary microcalcification burden

Purpose18F-sodium fluoride (18F-NaF) has shown promise in assessing disease activity in coronary arteries, but currently used measures of activity - such as maximum target to background ratio (TBRmax) - are defined by single pixel count values. We aimed to develop a novel coronary-specific measure of 18F-NaF PET reflecting activity throughout the entire coronary vasculature (coronary microcalcification activity [CMA]).MethodsPatients with recent myocardial infarction and multi-vessel coronary artery disease underwent 18F-NaF PET and coronary CT angiography. We assessed the association between coronary 18F-NaF uptake (both TBRmax and CMA) and coronary artery calcium scores (CACS) as well as low attenuation plaque (LAP, attenuation &lt; 30 Hounsfield units) volume.ResultsIn 50 patients (64% males, 63 ± 7&nbsp;years), CMA and TBRmax were higher in vessels with LAP compared to those without LAP (1.09 [0.02, 2.34] versus 0.0 [0.0, 0.0], p &lt; 0.001 and 1.23 [1.16, 1.37] versus 1.04 [0.93, 1.11], p &lt; 0.001). Compared to a TBRmax threshold of 1.25, CMA &gt; 0 had a higher diagnostic accuracy for detection of LAP: sensitivity of 93.1 (83.3-98.1)% versus 58.6 (44.9-71.4)% and a specificity of 95.7 (88.0-99.1)% versus 80.0 (68.7-88.6)% (both p &lt; 0.001). 18F-NaF uptake assessed by CMA correlated more closely with LAP (r = 0.86, p &lt; 0.001) than the CT calcium score (r = 0.39, p &lt; 0.001), with these associations outperforming those observed for TBRmax values (LAP r = 0.63, p &lt; 0.001; CT calcium score r = 0.30, p &lt; 0.001).ConclusionsAutomated assessment of disease activity across the entire coronary vasculature is feasible using 18F-NaF CMA, providing a single measurement that has closer agreement with CT markers of plaque vulnerability than more traditional measures of plaque activity