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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&#95;L), medium (DLIR&#95;M), and high (DLIR&#95;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., (© 2022. The Author(s).)

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 mm 3 ) were assessed quantitatively. Stenosis severity was visually categorized into no stenosis (0%), stenosis (< 20%, 20-50%, 51-70%, 71-90%, 91-99%), and occlusion (100%). Plaque composition was classified as calcified, non-calcified, or mixed., 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 < 0.001) had no impact on image noise (28 vs. 27 HU, 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 mm 3 (± 2.5 mm 3 ) and limits of agreement between - 5.8 and + 4.1 mm 3 ., 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., (© 2021. The Author(s).)

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 13 N-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 mm 3 /g, n = 24) versus high (i.e., > 20.2 mm 3 /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, p diff < 0.05 and 0.770 versus 0.645, p diff < 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 from 13 N-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.

Objectives: To evaluate the diagnostic performance of a deep learning algorithm for automated detection of small 18 F-FDG-avid pulmonary nodules in PET scans, and to assess whether novel block sequential regularized expectation maximization (BSREM) reconstruction affects detection accuracy as compared to ordered subset expectation maximization (OSEM) reconstruction., Methods: Fifty-seven patients with 92 18 F-FDG-avid pulmonary nodules (all ≤ 2 cm) undergoing PET/CT for oncological (re-)staging were retrospectively included and a total of 8824 PET images of the lungs were extracted using OSEM and BSREM reconstruction. Per-slice and per-nodule sensitivity of a deep learning algorithm was assessed, with an expert readout by a radiologist/nuclear medicine physician serving as standard of reference. Receiver-operator characteristic (ROC) curve of OSEM and BSREM were assessed and the areas under the ROC curve (AUC) were compared. A maximum standardized uptake value (SUV max )-based sensitivity analysis and a size-based sensitivity analysis with subgroups defined by nodule size was performed., Results: The AUC of the deep learning algorithm for nodule detection using OSEM reconstruction was 0.796 (CI 95%; 0.772-0.869), and 0.848 (CI 95%; 0.828-0.869) using BSREM reconstruction. The AUC was significantly higher for BSREM compared to OSEM (p = 0.001). On a per-slice analysis, sensitivity and specificity were 66.7% and 79.0% for OSEM, and 69.2% and 84.5% for BSREM. On a per-nodule analysis, the overall sensitivity of OSEM was 81.5% compared to 87.0% for BSREM., Conclusions: Our results suggest that machine learning algorithms may aid detection of small 18 F-FDG-avid pulmonary nodules in clinical PET/CT. AI performed significantly better on images with BSREM than OSEM., Key Points: • The diagnostic value of deep learning for detecting small lung nodules (≤ 2 cm) in PET images using BSREM and OSEM reconstruction was assessed. • BSREM yields higher SUV max of small pulmonary nodules as compared to OSEM reconstruction. • The use of BSREM translates into a higher detectability of small pulmonary nodules in PET images as assessed with artificial intelligence.

Objectives: To assess long-term prognosis after low-dose 64-slice coronary computed tomography angiography (CCTA) using prospective electrocardiogram-triggering., Methods: We included 434 consecutive patients with suspected or known coronary artery disease referred for low-dose CCTA. Patients were classified as normal, with non-obstructive or obstructive lesions, or previously revascularized. Coronary artery calcium score (CACS) was assessed in 223 patients. Follow-up was obtained regarding major adverse cardiac events (MACE): cardiac death, myocardial infarction and elective revascularization. We performed Kaplan-Meier analysis and Cox regressions., Results: Mean effective radiation dose was 1.7 ± 0.6 mSv. At baseline, 38% of patients had normal arteries, 21% non-obstructive lesions, 32% obstructive stenosis and 8% were revascularized. Twenty-nine patients (7%) were lost to follow-up. After a median follow-up of 6.1 ± 0.6 years, MACE occurred in 0% of patients with normal arteries, 6% with non-obstructive lesions, 30% with obstructive stenosis and 39% of those revascularized. MACE occurrence increased with increasing CACS (P < 0.001), but 4% of patients with CACS = 0 experienced MACE. Multivariate Cox regression identified obstructive stenosis, lesion burden in CCTA and CACS as independent MACE predictors (P ≤ 0.001)., Conclusion: Low-dose CCTA with prospective electrocardiogram-triggering has an excellent long-term prognostic performance with a warranty period >6 years for patients with normal coronary arteries., Key Points: • Coronary CT angiography (CCTA) has an excellent long-term prognostic performance. • CCTA can accurately stratify cardiac risk according to coronary lesion severity. • A normal CCTA predicts freedom from cardiac events for >6 years. • Patients with a coronary calcium score of 0 may experience cardiac events. • CCTA allows for reclassification of cardiac risk compared with ESC SCORE.