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Myocardial perfusion imaging (MPI) is frequently used to provide risk stratification, but methods to improve the accuracy of these predictions are needed.We developed an explainable deep learning (DL) model (HARD MACE [major adverse cardiac events]-DL) for the prediction of death or nonfatal myocardial infarction (MI) and validated its performance in large internal and external testing groups.Patients undergoing single-photon emission computed tomography MPI were included, with 20,401 patients in the training and internal testing group (5 sites) and 9,019 in the external testing group (2 different sites). HARD MACE-DL uses myocardial perfusion, motion, thickening, and phase polar maps combined with age, sex, and cardiac volumes. The primary outcome was all-cause mortality or nonfatal MI. Prognostic accuracy was evaluated using area under the receiver-operating characteristic curve (AUC).During internal testing, patients with normal perfusion and elevated HARD-MACE-DL risk were at higher risk than patients with abnormal perfusion and low HARD-MACE-DL risk (annualized event rate, 2.9% vs 1.2%; P 0.001). Patients in the highest quartile of HARD MACE-DL score had an annual rate of death or MI (4.8%) 10-fold higher than patients in the lowest quartile (0.48% per year). In external testing, the AUC for HARD MACE-DL (0.73; 95% CI: 0.71-0.75) was higher than a logistic regression model (AUC: 0.70), stress TPD (AUC: 0.65), and ischemic TPD (AUC: 0.63; all P 0.01). Calibration, a measure of how well predicted risk matches actual risk, was excellent in both groups (Brier score, 0.079 for internal and 0.070 for external).The DL model predicts death or MI directly from MPI, by estimating patient-level risk with good calibration and improved accuracy compared with traditional quantitative approaches. The model incorporates mechanisms to explain to the physician which image regions contribute to the adverse event prediction.

Background Deep learning image reconstructions (DLIR) have been recently introduced as an alternative to filtered back projection (FBP) and iterative reconstruction (IR) algorithms for computed tomography (CT) image reconstruction. The aim of this study was to evaluate the effect of DLIR on image quality and quantification of coronary artery calcium (CAC) in comparison to FBP. Methods One hundred patients were consecutively enrolled. Image quality–associated variables (noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) as well as CAC-derived parameters (Agatston score, mass, and volume) were calculated from images reconstructed by using FBP and three different strengths of DLIR (low (DLIR_L), medium (DLIR_M), and high (DLIR_H)). Patients were stratified into 4 risk categories according to the Coronary Artery Calcium - Data and Reporting System (CAC-DRS) classification: 0 Agatston score (very low risk), 1–99 Agatston score (mildly increased risk), Agatston 100–299 (moderately increased risk), and ≥ 300 Agatston score (moderately-to-severely increased risk). Results In comparison to standard FBP, increasing strength of DLIR was associated with a significant and progressive decrease of image noise (p < 0.001) alongside a significant and progressive increase of both SNR and CNR (p < 0.001). The use of incremental levels of DLIR was associated with a significant decrease of Agatston CAC score and CAC volume (p < 0.001), while mass score remained unchanged when compared to FBP (p = 0.232). The underestimation of Agatston CAC led to a CAC-DRS misclassification rate of 8%. Conclusion DLIR systematically underestimates Agatston CAC score. Therefore, DLIR should be used cautiously for cardiovascular risk assessment. Key Points • In coronary artery calcium imaging, the implementation of deep learning image reconstructions improves image quality, by decreasing the level of image noise. • Deep learning image reconstructions systematically underestimate Agatston coronary artery calcium score. • Deep learning image reconstructions should be used cautiously in clinical routine to measure Agatston coronary artery calcium score for cardiovascular risk assessment.

PURPOSE: Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive testing), the disease probability can be overestimated due to selection bias. We evaluated different strategies for training AI models to improve the calibration (accurate estimate of disease probability), using external testing. METHODS: Deep learning was trained using 828 patients from 3 sites, with MPI and invasive angiography within 6-months. Perfusion was assessed using upright (U-TPD) and supine total perfusion deficit (S-TPD). AI training without data augmentation (Model 1) was compared to training with augmentation (increased sampling) of patients without obstructive CAD (Model 2), and patients without CAD and TPD

Background Myocardial perfusion imaging by positron emission tomography (PET-MPI) is the current gold standard for quantification of myocardial blood flow. F-18-flurpiridaz was recently introduced as a valid alternative to currently used PET-MPI probes. Nonetheless, optimum scan duration and time interval for image analysis are currently unknown. Further, it is unclear whether rest/stress PET-MPI with F-18-flurpiridaz is feasible in mice. Methods Rest/stress PET-MPI was performed with F-18-flurpiridaz (0.6-3.0 MBq) in 27 mice aged 7-8 months. Regadenoson (0.1 mu g/g) was used for induction of vasodilator stress. Kinetic modeling was performed using a metabolite-corrected arterial input function. Image-derived myocardial F-18-flurpiridaz uptake was assessed for different time intervals by placing a volume of interest in the left ventricular myocardium. Results Tracer kinetics were best described by a two-tissue compartment model. K-1 ranged from 6.7 to 20.0 mL center dot cm(-3)center dot min(-1), while myocardial volumes of distribution (V-T) were between 34.6 and 83.6 mL center dot cm(-3). Of note, myocardial F-18-flurpiridaz uptake (%ID/g) was significantly correlated with K-1 at rest and following pharmacological vasodilation for all time intervals assessed. However, while Spearman's coefficients (r(s)) ranged between 0.478 and 0.681, R-2 values were generally low. In contrast, an excellent correlation of myocardial F-18-flurpiridaz uptake with V-T was obtained, particularly when employing the averaged myocardial uptake from 20 to 40 min post tracer injection (R-2 >= 0.98). Notably, K-1 and V-T were similarly sensitive to pharmacological vasodilation induction. Further, mean stress-to-rest ratios of K-1, V-T, and %ID/g F-18-flurpiridaz were virtually identical, suggesting that %ID/g F-18-flurpiridaz can be used to estimate coronary flow reserve (CFR) in mice. Conclusion Our findings suggest that a simplified assessment of relative myocardial perfusion and CFR, based on image-derived tracer uptake, is feasible with F-18-flurpiridaz in mice, enabling high-throughput mechanistic CFR studies in rodents., Journal of Nuclear Cardiology, 30 (1), ISSN:1071-3581, ISSN:1532-6551

OBJECTIVES The aim of this study was to determine Syntax scores based on coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) and to assess whether heavy coronary calcification significantly limits the CCTA evaluation and the impact of severe calcification on heart team’s treatment decision and procedural planning in patients with three-vessel coronary artery disease (CAD) with or without left main disease. METHODS SYNTAX III was a multicentre, international study that included patients with three-vessel CAD with or without left main disease. The heart teams were randomized to either assess coronary arteries with coronary CCTA or ICA. We stratified the patients based on the presence of at least 1 lesion with heavy calcification defined as arc of calcium >180° within the lesion using CCTA. Agreement on the anatomical SYNTAX score and treatment decision was compared between patients with and without heavy calcifications. RESULTS Overall, 222 patients with available CCTA and ICA were included in this trial subanalysis (104 with heavy calcification, 118 without heavy calcification). The mean difference in the anatomical SYNTAX score (CCTA derived—ICA derived) was lower in patients without heavy calcifications [mean (−1.96 SD; +1.96 SD) = 1.5 (−19.3; 22.4) vs 5.9 (−17.5; +29.3), P = 0.004]. The agreement on treatment decision did not differ between patients with (Cohen’s kappa 0.79) or without coronary calcifications (Cohen’s kappa 0.84). The agreement on the treatment planning did not differ between patients with (concordance 80.3%) or without coronary calcifications (concordance 82.8%). CONCLUSIONS An overall good correlation between CCTA- and ICA-derived Syntax score was found. The presence of heavy coronary calcification moderately influenced the agreement between CCTA and ICA on the anatomical SYNTAX score. However, agreement on the treatment decision and planning was high and irrespective of the presence of calcified lesions., Severe calcifications of vessel wall and atherosclerotic plaque hamper visual assessment of coronary arteries with coronary computed tomography angiography (CCTA) due to blooming artefacts.

Pathologic hypertrophy of the cardiac muscle is a commonly encountered phenotype in clinical practice, associated with a variety of structural and non-structural diseases. Coronary microvascular disease is considered to play an important role in the natural history of this pathological phenotype. Non-invasive imaging modalities, most prominently positron emission tomography and cardiac magnetic resonance, have provided insights into the pathophysiological mechanisms of the interplay between hypertrophy and the coronary microvasculature. This article summarizes the current knowledge on coronary microvascular dysfunction in the most frequently encountered forms of pathologic hypertrophy.

Aim The current proof-of-concept study investigates the value of radiomic features from normal 13N-ammonia positron emission tomography (PET) myocardial retention images to identify patients with reduced global myocardial flow reserve (MFR). Methods Data from 100 patients with normal retention 13N-ammonia PET scans were divided into two groups, according to global MFR (i.e., Results A total of 475 radiomic features were extracted per patient. Outcome independent and dependent feature selection resulted in a remainder of 35 features. Discretization at 16 bins (GL16) yielded the highest number of significant predictors of reduced MFR and was chosen for the final analysis. GLRLM_GLNU was the most robust parameter and at a cut-off of 948 yielded an accuracy, sensitivity, specificity, negative and positive predictive value of 67%, 74%, 58%, 64%, and 69%, respectively, to detect diffusely impaired myocardial perfusion. Conclusion A single radiomic feature (GLRLM_GLNU) extracted from visually normal 13N-ammonia PET retention images independently predicts reduced global MFR with moderate accuracy. This concept could potentially be applied to other myocardial perfusion imaging modalities based purely on relative distribution patterns to allow for better detection of diffuse disease.

NeuroLF is a dedicated brain PET system with an octagonal prism shape housed in a scanner head that can be positioned around a patient’s head. Because it does not have MR or CT capabilities, attenuation correction based on an estimation of the attenuation map is a crucial feature. In this article, we demonstrate this method on [18F]FDG PET brain scans performed with a low-resolution proof of concept prototype of NeuroLF called BPET. We perform an affine registration of a template PET scan to the uncorrected emission image, and then apply the resulting transform to the corresponding template attenuation map. Using a whole-body PET/CT system as reference, we quantitively show that this method yields comparable image quality (0.893 average correlation to reference scan) to using the reference µ-map as obtained from the CT scan of the imaged patient (0.908 average correlation). We conclude from this initial study that attenuation correction using template registration instead of a patient CT delivers similar results and is an option for patients undergoing brain PET.

BACKGROUND: Diabetes mellitus (DM) is increasingly prevalent among contemporary populations referred for cardiac stress testing, but its potency as a predictor for major adverse cardiovascular events (MACE) versus other clinical variables is not well delineated. METHODS & RESULTS: From 19,658 patients underwent SPECT-MPI, we identified 3,122 diabetic patients without known coronary artery disease (CAD) (DM+/CAD−) and 3,564 nondiabetics with known CAD (DM−/CAD+). Propensity score matching was used to control for the differences in characteristics between DM+/CAD− and DM−/CAD+ groups. There was comparable MACE in the matched DM+/CAD− and DM−/CAD+ groups (HR:1.15, 95%CI: 0.97–1.37). By Chi-square analysis, type of stress (exercise or pharmacologic), total perfusion deficit (TPD), and left ventricular function were the most potent predictors of MACE, followed by CAD and diabetes status. The combined consideration of mode of stress, TPD, and diabetes provided synergistic stratification, an 8.87-fold (HR:8.87, 95%CI: 7.27–10.82) increase in MACE among pharmacologically stressed patients with diabetes and TPD>10% (versus non-ischemic, exercised stressed patients without diabetes). CONCLUSIONS: Propensity-matched patients with diabetes and no known CAD have similar MACE risk compared to patients with known CAD and no diabetes. Diabetes is synergistic with mode of stress testing and TPD in predicting the risk of cardiac stress test patients.

Cardiovascular diseases (CVD) remain the leading cause of mortality worldwide. Although major diagnostic and therapeutic advances have significantly improved the prognosis of patients with CVD in the past decades, these advances have less benefited women than age-matched men. Noninvasive cardiac imaging plays a key role in the diagnosis of CVD. Despite shared imaging features and strategies between both sexes, there are critical sex disparities that warrant careful consideration, related to the selection of the most suited imaging techniques, to technical limitations, and to specific diseases that are overrepresented in the female population. Taking these sex disparities into consideration holds promise to improve management and alleviate the burden of CVD in women. In this review, we summarize the specific features of cardiac imaging in four of the most common presentations of CVD in the female population including coronary artery disease, heart failure, pregnancy complications, and heart disease in oncology, thereby highlighting contemporary strengths and limitations. We further propose diagnostic algorithms tailored to women that might help in selecting the most appropriate imaging modality.

BACKGROUND: The utility of cardiac stress testing depends on the prevalence of myocardial ischemia within candidate populations. However, a comprehensive assessment of the factors influencing frequency of myocardial ischemia within contemporary populations referred for stress testing has not been performed. METHODS: We assessed 19,690 patients undergoing nuclear stress testing from a multicenter registry. The chi-square test was used to assess the relative importance of features for predicting myocardial ischemia. RESULTS: In the overall cohort, LVEF, male gender, and rest total perfusion deficit (TPD) were the top three predictors of ischemia, followed by CAD status, age, typical angina, and CAD risk factors. Myocardial ischemia was observed in 13.6% of patients with LVEF>55%, in 26.2% of patients with LVEF 45-54%, and in 48.3% among patients with LVEF

BACKGROUND: Stress-only myocardial perfusion imaging (MPI) markedly reduces radiation dose, scanning time, and cost. We developed an automated clinical algorithm to safely cancel unnecessary rest imaging with high sensitivity for obstructive coronary artery disease (CAD). METHODS AND RESULTS: Patients without known CAD undergoing both MPI and invasive coronary angiography from REFINE SPECT were studied. A machine learning score (MLS) for prediction of obstructive CAD was generated using stress-only MPI and pre-test clinical variables. An MLS threshold with a pre-defined sensitivity of 95% was applied to the automated patient selection algorithm. Obstructive CAD was present in 1,309/2,079 (63%) patients. MLS had higher area under the receiver-operator-characteristic curve (AUC) for prediction of CAD than reader diagnosis and TPD (0.84 vs 0.70 vs 0.78, p

AIMS Optimal risk stratification with machine learning (ML) from myocardial perfusion imaging (MPI) includes both clinical and imaging data. While most imaging variables can be derived automatically, clinical variables require manual collection, which is time consuming and prone to error. We determined the fewest manually input and imaging variables required to maintain the prognostic accuracy for major adverse cardiac events (MACE) in patients undergoing single-photon emission computed tomography (SPECT) MPI. METHODS AND RESULTS This study included 20,414 patients from the multicenter REFINE SPECT registry and 2,984 from the University of Calgary for training and external testing of the ML models, respectively. ML models were trained using all variables (ML-All) and all image-derived variables (including age and sex, ML-Image). Next, ML models were sequentially trained by incrementally adding manually input and imaging variables to baseline ML models based on their importance ranking. The fewest variables were determined as the ML models (ML-Reduced, ML-Minimum, and ML-Image-Reduced) that achieved comparable prognostic performance to ML-All and ML-Image. Prognostic accuracy of the ML models was compared with visual diagnosis, stress total perfusion deficit (TPD), and traditional multivariable models using area under the receiver-operating characteristic curve (AUC).ML-Minimum (AUC 0.798) obtained comparable prognostic accuracy to ML-All (AUC 0.798, p = 0.18) by including 12 of 40 manually input variables and 11 of 58 imaging variables. ML-Reduced achieved comparable accuracy (AUC 0.795) with a reduced set of manually input variables and all imaging variables. In external validation, the ML models also obtained comparable or higher prognostic accuracy than traditional multivariable models. CONCLUSION Reduced ML models, including a minimum set of manually collected or imaging variables, achieved slightly lower accuracy compared to a full ML model, but outperformed standard interpretation methods and risk models. ML models with fewer collected variables may be more practical for clinical implementation. TRANSLATIONAL PERSPECTIVE A reduced machine learning model, with 12 out of 40 manually collected variables and 11 of 58 imaging variables, achieved >99% of the prognostic accuracy of the full model. Models with fewer manually collected features require less infrastructure to implement, are easier for physicians to utilize, and are potentially critical to ensuring broader clinical implementation. Additionally, these models can integrate mechanisms to explain patient-specific risk estimates to improve physician confidence in the machine learning prediction.

Purpose Prostate-specific membrane antigen (PSMA) targeted positron emission tomography (PET) imaging significantly improved the detection of recurrent prostate cancer (PCa). However, the value of PSMA PET imaging in patients with advanced hormone-sensitive or hormone-resistant PCa is still largely unknown. The aim of this study was to analyze the detection rate and distribution of lesions using PSMA PET imaging in patients with advanced PCa and ongoing androgen deprivation therapy (ADT). Methods A total of 84 patients diagnosed with hormone-sensitive or hormone-resistant PCa who underwent 68Ga-PSMA-11 PET/magnetic resonance imaging (MRI) or computer tomography (CT) under ongoing ADT were retrospectively analyzed. We assessed the detection of PSMA-positive lesions overall and for three PSA subgroups (0 to 20 ng/mL). In addition, PSMA-positive findings were stratified by localization (prostatic fossa, pelvic, para-aortic, mediastinal/supraclavicular and axillary lymph nodes, bone lesions and visceral lesions) and hormone status (hormone-sensitive vs. hormone-resistant). Furthermore, we assessed how many patients would be classified as having oligometastatic disease (≤ 3 lesions) and theoretically qualify for metastasis-directed radiotherapy (MDRT) in a personalized patient management. Results We detected PSMA-positive lesions in 94.0% (79 of 84) of all patients. In the three PSA subgroups detection rates of 85.2% (0 to n = 27), 97.3% (1 to n = 37) and 100% (> 20 ng/mL, n = 20) were observed, respectively. PSMA-positive visceral metastases were observed only in patients with a PSA > 1 ng/mL. Detection of PSMA-positive lesions did not significantly differ between patients with hormone-sensitive and hormone-resistant PCa. Oligometastatic PCa was detected in 19 of 84 patients (22.6%). Almost all patients, 94.7% (n = 18) would have been eligible for MDRT. Conclusions In this study, we observed an overall very high detection rate of 94% using PSMA PET imaging in patients with advanced PCa and ongoing ADT. Even in a majority of patients with very low PSA values

Purpose To assess the prognostic value of regional quantitative myocardial flow measures as assessed by 13N-ammonia positron emission tomography (PET) myocardial perfusion imaging (MPI) in patients with suspected coronary artery disease (CAD). Methods We retrospectively included 150 consecutive patients with suspected CAD who underwent clinically indicated 13 N-ammonia PET-MPI and who did not undergo revascularization within 90 days of PET-MPI. The presence or absence of a decreased global myocardial flow reserve (i.e., MFR Results Over a median follow-up of 50 months (IQR 38–103), 30 events occurred in 29 patients. Kaplan–Meier analysis showed significantly reduced event-free and overall survival in MFR groups 2 and 3 compared to MFR group 1 (log-rank: p = 0.015 and p = 0.013). In a multivariable Cox regression analysis, decreased regional MFR was an independent predictor for MACE (adjusted HR 3.44, 95% CI 1.17–10.11, p = 0.024) and all-cause death (adjusted HR 4.72, 95% CI 1.07–20.7, p = 0.04). Conclusions A decreased regional MFR as assessed by 13 N-ammonia PET-MPI confers prognostic value by identifying patients at increased risk for future adverse cardiac outcomes and all-cause death.

PURPOSE Ex vivo liver machine perfusion is a promising option to rescue marginal liver grafts mitigating the donated organ shortage. Recently, a novel liver perfusion machine that can keep injured liver grafts alive for 1 week ex vivo was developed and reported in Nature Biotechnology. However, liver viability assessment ex vivo is an unsolved issue and the value of 18F-fluorodeoxyglucose (FDG)-PET/CT for such purpose was explored. MATERIALS AND METHODS Discarded two human and six porcine liver grafts underwent FDG-PET/CT for viability assessment after 1 week of ex vivo perfusion. PET parameters [standardized uptake value (SUV)max, SUVmean, SUVpeak and total lesion glycolysis] were compared between hepatic lobes and between porcine and human livers. The prevalence of FDG-negative organ parts was recorded. The estimated effective radiation dose for PET/CT was calculated. RESULTS All organs were viable with essentially homogeneous FDG uptake. Of note, viability was preserved in contact areas disclosing the absence of pressure necrosis. Four porcine and two human organs had small superficial FDG-negative areas confirmed as biopsy sites. Total lesion glycolysis was significantly higher in the right hepatic lobe (P = 0.012), while there was no significant difference of SUVmax, SUVmean and SUVpeak between hepatic lobes. There was no significant difference in FDG uptake parameters between porcine and human organs. The estimated effective radiation dose was 1.99 ± 1.67 mSv per organ. CONCLUSION This study demonstrates the feasibility of FDG-PET/CT for viability assessment of ex vivo perfused liver grafts after 1 week.

Background In people with human immunodeficiency virus (HIV) (PWH), individual polygenic risk scores (PRSs) are associated with coronary artery disease (CAD) events. Whether PRSs are associated with subclinical CAD is unknown. Methods In Swiss HIV Cohort Study participants of European descent, we defined subclinical CAD as presence of soft, mixed, or high-risk plaque (SMHRP) on coronary computed tomography (CT) angiography, or as participants in the top tertile of the study population's coronary artery calcium (CAC) score, using noncontrast CT. We obtained univariable and multivariable odds ratios (ORs) for subclinical CAD endpoints based on nongenetic risk factors, and validated genome-wide PRSs built from single nucleotide polymorphisms associated with CAD, carotid intima-media thickness (IMT), or longevity in the general population. Results We included 345 genotyped participants (median age, 53 years; 89% male; 96% suppressed HIV RNA); 172 and 127 participants had SMHRP and CAC, respectively. CAD-associated PRS and IMT-associated PRS were associated with SMHRP and CAC (all P < .01), but longevity PRS was not. Participants with unfavorable CAD-PRS (top quintile) had an adjusted SMHRP OR = 2.58 (95% confidence interval [CI], 1.18–5.67), and a CAC OR = 3.95 (95% CI, 1.45–10.77) vs. bottom quintile. Unfavorable nongenetic risk (top vs. bottom quintile) was associated with adjusted SMHRP OR = 24.01 (95% CI, 9.75–59.11), and a CAC-OR = 65.07 (95% CI, 18.48–229.15). Area under the receiver operating characteristic curve increased when we added CAD-PRS to nongenetic risk factors (SMHRP: 0.75 and 0.78, respectively; CAC: 0.80 and 0.83, respectively). Conclusions In Swiss PWH, subclinical CAD is independently associated with an individual CAD-associated PRS. Combining nongenetic and genetic cardiovascular risk factors provided the most powerful subclinical CAD prediction.

BACKGROUND: Accurately predicting which patients will have abnormal perfusion on MPI based on pre-test clinical information may help physicians make test selection decisions. We developed and validated a machine learning (ML) model for predicting abnormal perfusion using pre-test features. METHODS: We included consecutive patients who underwent SPECT MPI, with 20,418 patients from a multi-center (5 sites) international registry in the training population and 9,019 patients (from 2 separate sites) in the external testing population. The ML (extreme gradient boosting) model utilized 30 pre-test features to predict the presence of abnormal myocardial perfusion by expert visual interpretation. RESULTS: In external testing, the ML model had higher prediction performance for abnormal perfusion (area under receiver-operating characteristic curve [AUC] 0.762, 95% CI 0.750 – 0.774) compared to the clinical CAD consortium (AUC 0.689) basic CAD consortium (AUC 0.657), and updated Diamond-Forrester models (AUC 0.658, p

Background A growing body of evidence highlights sex differences in the diagnostic accuracy of cardiovascular imaging modalities. Nonetheless, the role of sex hormones in modulating myocardial perfusion and coronary flow reserve (CFR) is currently unclear. The aim of our study was to assess the impact of female and male sex hormones on myocardial perfusion and CFR. Methods Rest and stress myocardial perfusion imaging (MPI) was conducted by small animal positron emission tomography (PET) with [F-18]flurpiridaz in a total of 56 mice (7-8 months old) including gonadectomized (Gx) and sham-operated males and females, respectively. Myocardial [F-18]flurpiridaz uptake (% injected dose per mL, % ID/mL) was used as a surrogate for myocardial perfusion at rest and following intravenous regadenoson injection, as previously reported. Apparent coronary flow reserve (CFRApp) was calculated as the ratio of stress and rest myocardial perfusion. Left ventricular (LV) morphology and function were assessed by cardiac magnetic resonance (CMR) imaging. Results Orchiectomy resulted in a significant decrease of resting myocardial perfusion (Gx vs. sham, 19.4 +/- 1.0 vs. 22.2 +/- 0.7 % ID/mL, p = 0.034), while myocardial perfusion at stress remained unchanged (Gx vs. sham, 27.5 +/- 1.2 vs. 27.3 +/- 1.2 % ID/mL, p = 0.896). Accordingly, CFRApp was substantially higher in orchiectomized males (Gx vs. sham, 1.43 +/- 0.04 vs. 1.23 +/- 0.05, p = 0.004), and low serum testosterone levels were linked to a blunted resting myocardial perfusion (r = 0.438, p = 0.020) as well as an enhanced CFRApp (r = -0.500, p = 0.007). In contrast, oophorectomy did not affect myocardial perfusion in females. Of note, orchiectomized males showed a reduced LV mass, stroke volume, and left ventricular ejection fraction (LVEF) on CMR, while no such effects were observed in oophorectomized females. Conclusion Our experimental data in mice indicate that sex differences in myocardial perfusion are primarily driven by testosterone. Given the diagnostic importance of PET-MPI in clinical routine, further studies are warranted to determine whether testosterone levels affect the interpretation of myocardial perfusion findings in patients., European Journal of Nuclear Medicine and Molecular Imaging, 49, ISSN:1619-7070, ISSN:1619-7089

Background No methodology is available to distinguish truly reduced myocardial flow reserve (MFR) in positron emission tomography myocardial perfusion imaging (PET MPI) from seemingly impaired MFR due to inadequate adenosine response. The adenosine-induced splenic switch-off (SSO) sign has been proposed as a potential marker for adequate adenosine response in cardiac magnetic resonance (CMR). We assessed the feasibility of detecting SSO in nitrogen-13 ammonia PET MPI using SSO in CMR as the standard of reference. Methods and Results Fifty patients underwent simultaneous CMR and PET MPI on a hybrid PET/MR device with co-injection of a gadolinium-based contrast agent and nitrogen-13 ammonia during rest and adenosine-induced stress. In CMR, SSO was assessed visually (positive vs negative SSO) and quantitatively by calculating the ratio of the peak signal intensity of the spleen during stress over rest (SIR). In PET MPI, the splenic signal activity ratio (SAR) was calculated as the maximal standard uptake value of the spleen during stress over rest. The median SIR was significantly lower in patients with positive versus negative SSO in CMR (0.57 [IQR 0.49 to 0.62] vs 0.89 [IQR 0.76 to 0.98]; P P Conclusion Similarly to CMR, SSO can be detected in nitrogen-13 ammonia PET MPI. This might help distinguish adenosine non-responders from patients with truly impaired MFR due to microvascular dysfunction or multivessel coronary artery disease.

Objectives Coronary artery volume indexed to left myocardial mass (CAVi), derived from coronary computed tomography angiography (CCTA), has been proposed as an indicator of diffuse atherosclerosis. We investigated the association of CAVi with quantitative flow parameters and its ability to predict ischemia as derived from 13N-ammonia positron emission tomography myocardial perfusion imaging (PET-MPI). Methods Sixty patients who underwent hybrid CCTA/PET-MPI due to suspected CAD were retrospectively included. CAVi was defined as total coronary artery lumen volume over myocardial mass, both derived from CCTA. From PET-MPI, quantitative stress and rest myocardial blood flow (MBF) and myocardial flow reserve (MFR) were obtained and correlated with CAVi, and semi-quantitative perfusion images were analyzed for the presence of ischemia. Harrell’s c-statistic and net reclassification improvement (NRI) analysis were performed to evaluate the incremental value of CAVi over the CCTA model (i.e., stenosis > 50% and > 70%). Results CAVi correlated moderately with stress MBF and MFR (R = 0.50, p < 0.001, and R = 0.39, p = 0.002). Mean stress MBF and MFR were lower in patients with low (i.e., ≤ 20.2 mm3/g, n = 24) versus high (i.e., > 20.2 mm3/g, n = 36) CAVi (p < 0.001 for both comparisons). CAVi was independently associated with abnormal stress MBF (OR 0.90, 95% CI 0.82–0.998, p = 0.045). CAVi increased the predictive ability of the CCTA model for abnormal stress MBF and ischemia (c-statistic 0.763 versus 0.596, pdiff < 0.05 and 0.770 versus 0.645, pdiff < 0.05, NRI 0.84, p = 0.001 and 0.96, p < 0.001, respectively). Conclusions CAVi exhibits incremental value to predict both abnormal stress MBF and ischemia over CCTA alone. Key Points • Coronary artery volume indexed to left myocardial mass (CAVi), derived from coronary computed tomography angiography (CCTA), is correlated with myocardial blood flow indices derived from13N-ammonia positron emission tomography myocardial perfusion imaging. • CAVi is independently associated with abnormal stress myocardial blood flow. • CAVi provides incremental diagnostic value over CCTA for both abnormal stress MBF and ischemia.

Purpose Amygdalar metabolic activity was shown to independently predict cardiovascular outcomes. However, little is known about age- and sex-dependent variability in neuronal stress responses among individuals free of cardiac disease. This study sought to assess age- and sex-specific differences of resting amygdalar metabolic activity in the absence of clinical cardiovascular disease. Methods Amygdalar metabolic activity was assessed in 563 patients who underwent multimodality imaging by 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography and echocardiography for the evaluation of cardiac function. Results After exclusion of 294 patients with structural or functional cardiovascular pathologies, 269 patients (128 women) remained in the final population. 18F-FDG amygdalar activity significantly decreased with age in men (r = − 0.278, P = 0.001), but not in women (r = 0.002, P = 0.983). Similarly, dichotomous analysis confirmed a lower amygdalar activity in men ≥ 50 years as compared to those P = 0.007), which was not observed in women (0.81 ± 0.1 vs. 0.82 ± 0.1, P = 0.549). Accordingly, a fully adjusted linear regression analysis identified age as an independent predictor of amygdalar activity only in men (B-coefficient − 0.278, P = 0.001). Conclusion Amygdalar activity decreases with age in men, but not in women. The use of amygdalar activity for cardiovascular risk stratification merits consideration of inherent age- and sex-dependent variability.

Funding Acknowledgements Type of funding sources: None. onbehalf AMICO registry Background. Clinical evidence promotes therapy titration based on patient risk stratification in coronary artery disease (CAD). Purpose. We assessed the prognostic value of myocardial perfusion scintigraphy (MPS) with cadmium-zinc-telluride in addition to clinical and coronary anatomy analysis. Methods and Results. We prospectively enrolled 1464 patients (26% females, 69.5 ± 10.4 years) referred for stress-rest MPS. All the patients underwent invasive coronary angiography (1171, 80%) or coronary computed tomography angiography (293, 20%). We defined a composite endpoint of cardiovascular death and non-fatal MI. After a median follow-up of 3.5 years (interquartile range 2 – 6 years), we observed 127 events (Table). Summed stress score (SSS) had the highest accuracy in predicting primary endpoint with a ROC-derived cut-off of SSS > 8 (>10% myocardium). SSS > 8 portended the lowest survival probability at Kaplan–Meier analysis (p 8 treated with optimal medical therapy (OMT) had the largest area of necrosis, the lower ischemic burden, the most compromised LV systo-diastolic function and the highest LV mass, but received a less aggressive treatment in comparison to early revascularized patients. Survival analysis revealed patients with SSS ≤ 8 had the greater freedom from events, irrespective of the treatment strategy, while the group with SSS > 8 and OMT had the worst outcome, followed by patients with SSS > 8 and early revascolarization (log-rank test: all p 4 and reach a plateau for values >12 (Figure B). Conclusion. The extension of stress perfusion abnormalities constitutes a robust independent predictor of future adverse events after adjustment for multiple clinical parameters and coronary anatomy analysis. MPS could help refine risk stratification of patients with known or suspected CAD. Primary and secondary endpoints Variable Total population (n = 1464) SSS > 8 (n = 591) SSS ≤ 8 (n = 873) Hazard Ratio (95% CI)* P-value* Primary endpoint 127 (9) 85 (14) 42 (5) 3.25 (2.25 - 4.70) 8 group as compared with the summed stress score (SSS)≤8 group, and P-values were calculated by the log-rank test and are unadjusted for multiple variables. Abstract Figure

Background Quantitative flow ratio (QFR) is a novel, adenosine-free method for functional lesion interrogation based on 3-dimensional quantitative coronary angiography and computational algorithms. We sought to investigate the diagnostic performance of QFR versus myocardial perfusion imaging positron emission tomography (MPI-PET), which yields the highest accuracy for detection of myocardial ischemia. Methods Diagnostic performance of QFR versus MPI-PET was assessed in consecutive patients undergoing both clinically indicated coronary angiography and 13N-ammonia MPI-PET within a six-month period. Results Out of 176 patients (439 coronary arteries), 19.3% were women. Percent area stenosis was 45 [32–58] %. Myocardial ischemia on 13N-ammonia MPI-PET was detected in 106 (24.1%) vessel territories and hemodynamic significance defined as contrast-flow vessel QFR ≤ 0.80 was observed in 83 (18.9%) vessels. Diagnostic accuracy, sensitivity, and specificity of contrast-flow vessel QFR for the prediction of myocardial ischemia on 13N-ammonia MPI-PET were 92.5 (95% CI 89.6–94.7) %, 73.6 (95% CI 64.1–81.7) %, and 98.5 (95% CI 96.5–99.5) %, respectively. The AUCs for contrast-flow vessel QFR, percent diameter stenosis, and percent area stenosis were 0.85 (95% CI 0.81–0.88, p Conclusions QFR, a novel diagnostic tool for functional coronary lesion assessment, provides good diagnostic agreement with MPI-PET and superior diagnostic accuracy for the detection of myocardial ischemia as compared to anatomic indices. Future studies will have to determine the non-inferiority of QFR to fractional flow reserve with respect to clinical outcomes.

Pathologic hypertrophy of the cardiac muscle is a commonly encountered phenotype in clinical practice, associated with a variety of structural and non-structural diseases. Coronary microvascular disease is considered to play an important role in the natural history of this pathological phenotype. Non-invasive imaging modalities, most prominently positron emission tomography and cardiac magnetic resonance, have provided insights into the pathophysiological mechanisms of the interplay between hypertrophy and the coronary microvasculature. This article summarizes the current knowledge on coronary microvascular dysfunction in the most frequently encountered forms of pathologic hypertrophy.

PURPOSE: In this cohort study, 5-year data from the Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter Registry (ie, CONFIRM) were examined to identify associations of baseline aspirin and statin use with mortality, major adverse cardiovascular events (MACE), and myocardial infarction (MI) in individuals without substantial (≥50%) stenosis. MATERIALS AND METHODS: In this prospective cohort study, all participants in the registry underwent coronary CT angiography and were classified as having no detectable coronary plaque or having nonobstructive coronary artery disease (CAD) (1%–49% stenosis). Participants with obstructive (≥50%) stenosis were excluded from analysis. The study commenced in June 2003 and was completed in March 2016. All unadjusted and risk-adjusted analyses utilized the Cox proportional hazard model with hospital sites modeled using shared frailty. RESULTS: A total of 6386 participants with no detectable plaque or with nonobstructive CAD were included (mean age, 56.0 years ± 13.3 [SD], 52% men). The mean follow-up period was 5.66 years ± 1.10. Nonobstructive CAD (n = 2815, 44% of all participants included in the study) was associated with a greater risk of all-cause mortality (10.6% [298 of 2815] vs 4.8% [170 of 3571], P < .001) compared to those without CAD (n = 3571, 56%). Baseline aspirin and statin use was documented for 1415 and 1429 participants, respectively, with nonobstructive CAD, and for 1560 and 1565 participants without detectable plaque, respectively. In individuals with nonobstructive CAD, baseline aspirin use was not associated with a reduction in MACE (10.9% [102 of 936] vs 14.7% [52 of 355], P = .06), all-cause mortality (9.6% [95 of 991] vs 10.9% [46 of 424], P = .468), or MI (4.4% [41 of 936] vs 6.2% [22 of 355], P = .18). On multivariate risk-adjusted analysis, baseline statin use was associated with a lower rate of MACE (hazard ratio, 0.59; 95% CI: 0.40, 0.87; P = .007). Neither therapy improved clinical outcomes for participants with no detectable plaque. CONCLUSION: In participants with nonobstructive CAD, baseline use of statins, but not of aspirin, was associated with improved clinical outcomes. Neither therapy was associated with benefit in participants without plaque. Keywords: Aspirin, Statin, Coronary Artery Disease, CT Angiography, Nonobstructive Coronary Artery Disease Clinical trial registration no. NCT01443637 Supplemental material is available for this article. © RSNA, 2022 See also the commentary by Canan and Navar in this issue.

Objectives Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition analysis, and plaque volume quantification. Methods This prospective study includes 50 patients who underwent two sequential CCTA scans at normal-dose (ND) and lower-dose (LD). ND scans were reconstructed with Adaptive Statistical Iterative Reconstruction-Veo (ASiR-V) 100%, and LD scans with DLIR. Image noise (in Hounsfield units, HU) and quantitative plaque volumes (in mm3) were assessed quantitatively. Stenosis severity was visually categorized into no stenosis (0%), stenosis ( Results Reduction of radiation dose from ND scans with ASiR-V 100% to LD scans with DLIR at the highest level (DLIR-H; 1.4 mSv vs. 0.8 mSv, p p = 0.598). Reliability of stenosis severity and plaque composition was excellent between ND scans with ASiR-V 100% and LD scans with DLIR-H (intraclass correlation coefficients of 0.995 and 0.974, respectively). Comparison of plaque volumes using Bland–Altman analysis revealed a mean difference of − 0.8 mm3 (± 2.5 mm3) and limits of agreement between − 5.8 and + 4.1 mm3. Conclusion DLIR enables a reduction in radiation dose from CCTA by 43% without significant impact on image noise, stenosis severity, plaque composition, and quantitative plaque volume. Key Points •Deep-learning image reconstruction (DLIR) enables radiation dose reduction by over 40% for coronary computed tomography angiography (CCTA). •Image noise remains unchanged between a normal-dose CCTA reconstructed by ASiR-V and a lower-dose CCTA reconstructed by DLIR. •There is no impact on the assessment of stenosis severity, plaque composition, and quantitative plaque volume between the two scans.

Aims The relationship between dyspnoea, coronary artery disease (CAD), and major cardiovascular events (MACE) is poorly understood. This study evaluated (i) the association of dyspnoea with the severity of anatomical CAD by coronary computed tomography angiography (CCTA) and (ii) to which extent CAD explains MACE in patients with dyspnoea. Methods and results From the international COronary CT Angiography EvaluatioN for Clinical Outcomes: An InteRnational Multicenter (CONFIRM) registry, 4425 patients (750 with dyspnoea) with suspected but without known CAD were included and prospectively followed for ≥5 years. First, the association of dyspnoea with CAD severity was assessed using logistic regression analysis. Second, the prognostic value of dyspnoea for MACE (myocardial infarction and death), and specifically, the interaction between dyspnoea and CAD severity was investigated using Cox proportional-hazard analysis. Mean patient age was 60.3 ± 11.9 years, 63% of patients were male and 592 MACE events occurred during a median follow-up duration of 5.4 (IQR 5.1–6.0) years. On uni- and multivariable analysis (adjusting for age, sex, body mass index, chest pain typicality, and risk factors), dyspnoea was associated with two- and three-vessel/left main (LM) obstructive CAD. The presence of dyspnoea increased the risk for MACE [hazard ratio (HR) 1.57, 95% confidence interval (CI): 1.29–1.90], which was modified after adjusting for clinical predictors and CAD severity (HR 1.26, 95% CI: 1.02–1.55). Conversely, when stratified by CAD severity, dyspnoea did not provide incremental prognostic value in one-, two-, or three-vessel/LM obstructive CAD, but dyspnoea did provide incremental prognostic value in non-obstructive CAD. Conclusion In patients with suspected CAD, dyspnoea was independently associated with severe obstructive CAD on CCTA. The severity of obstructive CAD explained the elevated MACE rates in patients presenting with dyspnoea, but in patients with non-obstructive CAD, dyspnoea portended additional risk.

OBJECTIVE To evaluate the impact of adaptive statistical iterative reconstruction-V (ASIR-V) on the accuracy of ultra-low-dose coronary artery calcium (CAC) scoring. MATERIALS AND METHOD One-hundred-and-three patients who underwent computed tomography (CT) for CAC scoring were prospectively included. All underwent standard scanning with 120-kilovolt-peak (kVp) and with 80- and 70-kVp tube voltage. ASiR-V was applied to the 80- and 70-kVp scans at different levels. The 120-kVp scans reconstructed with filtered back projection served as the standard of reference. Recently published novel kVp-adapted thresholds were used for calculation of CAC scores from 80- and 70-kVp scans and the resulting CAC scores were compared against the standard of reference. Patients were stratified into six CAC score risk categories: 0, 1-10, 11-100, 101-400, 401-1000, and >1000. RESULTS Increasing levels of ASIR-V led to an increasing underestimation of CAC scores with bias ranging from -128 to -118 and from -205 to -198 for the 80- and 70-kVp scans, respectively, when compared with the standard of reference. Reconstruction with 20% and 40% ASIR-V for the 80- and 70-kVp scans, respectively, yielded noise levels comparable to the standard of reference. Nevertheless, a change in risk-class was observed in 29 (28.6%) and 46 (44.7%) patients, exclusively to a lower risk-class, when CAC scores were derived from these reconstructions. CONCLUSION ASIR-V leads to noise reduction in CT scans acquired with low tube-voltages. However, ASIR-V introduces substantial inaccuracies and marked underestimation of ultra-low-dose CAC scoring as compared with standard-dose CAC scoring despite normalization of noise.