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Objectives: No clear recommendations are endorsed by the different scientific societies on the clinical use of repeat coronary computed tomography angiography (CCTA) in patients with non-obstructive coronary artery disease (CAD). This study aimed to develop and validate a practical CCTA risk score to predict medium-term disease progression in patients at a low-to-intermediate probability of CAD. Methods: Patients were part of the Progression of AtheRosclerotic PlAque Determined by Computed Tomographic Angiography Imaging (PARADIGM) registry. Specifically, 370 (derivation cohort) and 219 (validation cohort) patients with two repeat, clinically indicated CCTA scans, non-obstructive CAD, and absence of high-risk plaque (≥ 2 high-risk features) at baseline CCTA were included. Disease progression was defined as the new occurrence of ≥ 50% stenosis and/or high-risk plaque at follow-up CCTA. Results: In the derivation cohort, 104 (28%) patients experienced disease progression. The median time interval between the two CCTAs was 3.3 years (2.7–4.8). Odds ratios for disease progression derived from multivariable logistic regression were as follows: 4.59 (95% confidence interval: 1.69–12.48) for the number of plaques with spotty calcification, 3.73 (1.46–9.52) for the number of plaques with low attenuation component, 2.71 (1.62–4.50) for 25–49% stenosis severity, 1.47 (1.17–1.84) for the number of bifurcation plaques, and 1.21 (1.02–1.42) for the time between the two CCTAs. The C-statistics of the model were 0.732 (0.676–0.788) and 0.668 (0.583–0.752) in the derivation and validation cohorts, respectively. Conclusions: The new CCTA-based risk score is a simple and practical tool that can predict mid-term CAD progression in patients with known non-obstructive CAD. Clinical relevance statement: The clinical implementation of this new CCTA-based risk score can help promote the management of patients with non-obstructive coronary disease in terms of timing of imaging follow-u

Non-ischemic dilated cardiomyopathy (DCM) is a disease characterized by left ventricular dilation and systolic dysfunction. Patients with DCM are at higher risk for ventricular arrhythmias and sudden cardiac death (SCD). According to current international guidelines, left ventricular ejection fraction (LVEF) ≤ 35% represents the main indication for prophylactic implantable cardioverter defibrillator (ICD) implantation in patients with DCM. However, LVEF lacks sensitivity and specificity as a risk marker for SCD. It has been seen that the majority of patients with DCM do not actually benefit from the ICD implantation and, on the contrary, that many patients at risk of SCD are not identified as they have preserved or mildly depressed LVEF. Therefore, the use of LVEF as unique decision parameter does not maximize the benefit of ICD therapy. Multiple risk factors used in combination could likely predict SCD risk better than any single risk parameter. Several predictors have been proposed including genetic variants, electric indexes, and volumetric parameters of LV. Cardiac magnetic resonance (CMR) can improve risk stratification thanks to tissue characterization sequences such as LGE sequence, parametric mapping, and feature tracking. This review evaluates the role of CMR as a risk stratification tool in DCM patients referred for ICD.

Background: Scar burden with late gadolinium enhancement (LGE) cardiac MRI (CMR) predicts arrhythmic events in patients with postinfarction in single-center studies. However, LGE analysis requires experienced human observers, is time consuming, and introduces variability. Purpose: To test whether postinfarct scar with LGE CMR can be quantified fully automatically by machines and to compare the ability of LGE CMR scar analyzed by humans and machines to predict arrhythmic events. Materials and Methods: This study is a retrospective analysis of the multicenter, multivendor CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy (DERIVATE) registry. Patients with chronic heart failure, echocardiographic left ventricular ejection fraction (LVEF) of less than 50%, and LGE CMR were recruited (from January 2015 through December 2020). In the current study, only patients with ischemic cardiomyopathy were included. Quantification of total, dense, and nondense scars was carried out by two experienced readers or a Ternaus network, trained and tested with LGE images of 515 and 246 patients, respectively. Univariable and multivariable Cox analyses were used to assess patient and cardiac characteristics associated with a major adverse cardiac event (MACE). Area under the receiver operating characteristic curve (AUC) was used to compare model performances. Results: In 761 patients (mean age, 65 years ± 11, 671 men), 83 MACEs occurred. With use of the testing group, univariable Cox-analysis found New York Heart Association class, left ventricle volume and/or function parameters (by echocardiography or CMR), guideline criterion (LVEF of ≤35% and New York Heart Association class II or III), and LGE scar analyzed by humans or the machine-learning algorithm as predictors of MACE. Machine-based dense or total scar conferred incremental value over the guideline criterion for the association with MACE (AUC: 0.68 vs 0.63, P = .02 and AUC: 0.67 vs 0.63

Background: Implantable cardioverter-defibrillator (ICD) therapy is the most effective prophylactic strategy against sudden cardiac death (SCD) in patients with ischemic cardiomyopathy (ICM) and left ventricle ejection fraction (LVEF) ≤35% as detected by transthoracic echocardiograpgy (TTE). This approach has been recently questioned because of the low rate of ICD interventions in patients who received implantation and the not-negligible percentage of patients who experienced SCD despite not fulfilling criteria for implantation. Objectives: The DERIVATE-ICM registry (CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy; NCT03352648) is an international, multicenter, and multivendor study to assess the net reclassification improvement (NRI) for the indication of ICD implantation by the use of cardiac magnetic resonance (CMR) as compared to TTE in patients with ICM. Methods: A total of 861 patients with ICM (mean age 65 ± 11 years, 86% male) with chronic heart failure and TTE-LVEF <50% participated. Major adverse arrhythmic cardiac events (MAACE) were the primary endpoints. Results: During a median follow-up of 1,054 days, MAACE occurred in 88 (10.2%). Left ventricular end-diastolic volume index (HR: 1.007 [95% CI: 1.000-1.011]; P = 0.05), CMR-LVEF (HR: 0.972 [95% CI: 0.945-0.999]; P = 0.045) and late gadolinium enhancement (LGE) mass (HR: 1.010 [95% CI: 1.002-1.018]; P = 0.015) were independent predictors of MAACE. A multiparametric CMR weighted predictive derived score identifies subjects at high risk for MAACE compared with TTE-LVEF cutoff of 35% with a NRI of 31.7% (P = 0.007). Conclusions: The DERIVATE-ICM registry is a large multicenter registry showing the additional value of CMR to stratify the risk for MAACE in a large cohort of patients with ICM compared with standard of care.

Aims To compare the diagnostic accuracy of speckle tracking echocardiography technique using territorial longitudinal strain (TLS) for the detection of culprit vessel vs. vessel-specific wall motion score index (WMSI) in non-ST-segment elevation–acute coronary syndrome (NSTE-ACS) patients scheduled for invasive coronary angiography (ICA). Methods and results One hundred and eighty-three patients (mean age: 66 ± 12 years, male: 71%) diagnosed with NSTE-ACS underwent echocardiography evaluation at hospital admission and ICA within 24 h. Culprit vessels were left anterior descending (LAD), left circumflex (CX), and right coronary arteries (RCAs) in 38.5%, 39.6%, and 21.4%, respectively. An increase of affected vessels [1-, 2-, and 3-vessel coronary artery disease (CAD)] was associated with increased WMSI and TLS values. There was a statistically significant difference of both WMSI-LAD, WMSI-CX, WMSI-RCA and TLS-LAD, TLS-CX, TLS-RCA of myocardial segments with underlying severe CAD compared to no CAD (P = 0.001 and P < 0.001, respectively). Moreover, a significant difference of TLS-LAD, TLS-CX, TLS-RCA, and WMSI-CX of myocardial segments with an underlying culprit vessel compared to non-culprit vessels (P < 0.001, P < 0.001, P = 0.022, and P < 0.001, respectively) was identified. WMSI-LAD and WMSI-RCA did not show statistical significant differences. A regression model revealed that the combination of WMSI þ TLS was more accurate compared to WMSI alone in detecting the culprit vessel (LAD, P = 0.001; CX, P < 0.001; and RCA, P = 0.019). Conclusion Territorial longitudinal strain allows an accurate identification of the culprit vessel in NSTE-ACS patients. In addition to WMSI, TLS may be considered as part of routine echocardiography for better clinical assessment in this subset of patients.

Heart failure with preserved ejection fraction (HFpEF) is a syndrome defined by the presence of heart failure symptoms and increased levels of circulating natriuretic peptide (NP) in patients with preserved left ventricular ejection fraction and various degrees of diastolic dysfunction (DD). HFpEF is a complex condition that encompasses a wide range of different etiologies. Cardiovascular imaging plays a pivotal role in diagnosing HFpEF, in identifying specific underlying etiologies, in prognostic stratification, and in therapeutic individualization. Echocardiography is the first line imaging modality with its wide availability; it has high spatial and temporal resolution and can reliably assess systolic and diastolic function. Cardiovascular magnetic resonance (CMR) is the gold standard for cardiac morphology and function assessment, and has superior contrast resolution to look in depth into tissue changes and help to identify specific HFpEF etiologies. Differently, the most important role of nuclear imaging [i.e., planar scintigraphy and/or single photon emission CT (SPECT)] consists in the screening and diagnosis of cardiac transthyretin amyloidosis (ATTR) in patients with HFpEF. Cardiac CT can accurately evaluate coronary artery disease both from an anatomical and functional point of view, but tissue characterization methods have also been developed. The aim of this review is to critically summarize the current uses and future perspectives of echocardiography, nuclear imaging, CT, and CMR in patients with HFpEF.

Purpose: To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive evaluation. Methods: One hundred and twelve consecutive symptomatic patients scheduled for clinically indicated invasive coronary angiography (ICA) underwent CCTA plus static stress CTP and ICA with invasive fractional flow reserve (FFR) for stenoses ranging between 30 and 80%. Subsequently, a DL algorithm for the prediction of significant CAD by using the rest dataset (CTP-DLrest) and stress dataset (CTP-DLstress) was developed. The diagnostic accuracy for identification of significant CAD using CCTA, CCTA + CTP stress, CCTA + CTP-DLrest, and CCTA + CTP-DLstress was measured and compared. The time of analysis for CTP stress, CTP-DLrest, and CTP-DLStress was recorded. Results: Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and area under the curve (AUC) of CCTA alone and CCTA + CTPStress were 100%, 33%, 100%, 54%, 63%, 67% and 86%, 89%, 89%, 86%, 88%, 87%, respectively. Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and AUC of CCTA + DLrest and CCTA + DLstress were 100%, 72%, 100%, 74%, 84%, 96% and 93%, 83%, 94%, 81%, 88%, 98%, respectively. All CCTA + CTP stress, CCTA + CTP-DLRest, and CCTA + CTP-DLStress significantly improved detection of hemodynamically significant CAD compared to CCTA alone (p < 0.01). Time of CTP-DL was significantly lower as compared to human analysis (39.2 ± 3.2 vs. 379.6 ± 68.0 s, p < 0.001). Conclusion: Evaluation of myocardial ischemia using a DL approach on rest CTP datasets is feasible and accurate. This approach may be a useful gatekeeper prior to CTP stress..

Purpose: Quantification of myocardial blood flow (MBF) and functional assessment of coronary artery disease (CAD) can be achieved through stress myocardial computed tomography perfusion (stress-CTP). This requires an additional scan after the resting coronary computed tomography angiography (cCTA) and administration of an intravenous stressor. This complex protocol has limited reproducibility and non-negligible side effects for the patient. We aim to mitigate these drawbacks by proposing a computational model able to reproduce MBF maps. Methods: A computational perfusion model was used to reproduce MBF maps. The model parameters were estimated by using information from cCTA and MBF measured from stress-CTP (MBFCTP) maps. The relative error between the computational MBF under stress conditions (MBFCOMP) and MBFCTP was evaluated to assess the accuracy of the proposed computational model. Results: Applying our method to 9 patients (4 control subjects without ischemia vs 5 patients with myocardial ischemia), we found an excellent agreement between the values of MBFCOMP and MBFCTP. In all patients, the relative error was below 8% over all the myocardium, with an average-in-space value below 4%. Conclusion: The results of this pilot work demonstrate the accuracy and reliability of the proposed computational model in reproducing MBF under stress conditions. This consistency test is a preliminary step in the framework of a more ambitious project which is currently under investigation, i.e., the construction of a computational tool able to predict MBF avoiding the stress protocol and potential side effects while reducing radiation exposure.

Objectives: Computed tomography (CT) provides excellent anatomy assessment of the aortic annulus (AoA) and is utilized for pre-procedural planning of transcatheter aortic valve implantation (TAVI). We sought to investigate if geometrical characteristics of the AoA determined by CT may represent predictors of structural valve degeneration (SVD) in patients undergoing TAVI with balloon-expandable valves. Methods: This is a retrospective study on 124 consecutive patients (mean age: 79 ± 7 years; female: 61%) undergoing balloon-expandable TAVI prospectively enrolled in a registry. AoA maximum diameter (Dmax), minimum diameter (Dmin), and area were assessed using pre-procedural CT. SVD was identified during follow-up with transthoracic echocardiography documenting structural prosthetic valve abnormalities with or without hemodynamic changes. Results: The mean follow-up was 5.9 ± 1.7 years. SVD was found in 48 out of 124 patients (38%). AoA Dmax, Dmin, and area were significantly smaller in patients with SVD compared to patients without SVD (25.6 ± 2.2 mm vs. 27.1 ± 2.8 mm, p = 0.012; 20.5 ± 2.1 mm vs. 21.8 ± 2.1 mm, p = 0.001 and 419 ± 77 mm2 vs. 467 ± 88 mm2, p = 0.002, respectively). At univariable analysis, female sex, BSA, 23-mm prosthetic valve size, Dmax < 27.1 mm, and a Dmin < 19.9 mm were associated with SVD, whereas at multivariable analysis, only Dmin < 19.9 mm (OR = 2.873, 95% CI: 1.191–6.929, p = 0.019) and female sex (OR = 2.659, 95% CI: 1.095–6.458, p = 0.031) were independent predictors of SVD. Conclusions: Female sex and AoA Dmin < 19.9 mm are associated with SVD in patients undergoing TAVI with balloon-expandable valves. When implanting large prostheses in order to avoid paraprosthetic regurgitation, caution should be observed due to the risk of excessive stretching of the AoA Dmin, which may play a role in SVD. Key Points: • Long-term durability is a concern for transcatheter aortic valve bioprosthesis. • CT provides an excellent assessment

Cardiovascular disease remains an integral field on which new research in both the biomedical and technological fields is based, as it remains the leading cause of mortality and morbidity worldwide. However, despite the progress of cardiac imaging techniques, the heart remains a challenging organ to study. Artificial intelligence (AI) has emerged as one of the major innovations in the field of diagnostic imaging, with a dramatic impact on cardiovascular magnetic resonance imaging (CMR). AI will be increasingly present in the medical world, with strong potential for greater diagnostic efficiency and accuracy. Regarding the use of AI in image acquisition and reconstruction, the main role was to reduce the time of image acquisition and analysis, one of the biggest challenges concerning magnetic resonance; moreover, it has been seen to play a role in the automatic correction of artifacts. The use of these techniques in image segmentation has allowed automatic and accurate quantification of the volumes and masses of the left and right ventricles, with occasional need for manual correction. Furthermore, AI can be a useful tool to directly help the clinician in the diagnosis and derivation of prognostic information of cardiovascular diseases. This review addresses the applications and future prospects of AI in CMR imaging, from image acquisition and reconstruction to image segmentation, tissue characterization, diagnostic evaluation, and prognostication.

Background: We aim to evaluate the value of Cardiac magnetic resonance (CMR) feature tracking (CMR-FT) in addition to Task Force Criteria(TFC) in patients with (arrhythmogenic cardiomyopathy) AC biopsy-proved. Methods: Thirty-five patients with AC histologically proven who performed CMR with late gadolinium enhancement (LGE) acquisition were enrolled. The study population was divided in Group1 (negative CMR TFC and LV ejection fraction≥55%) and Group2 (positive CMR TFC and/or LVEF<55%) and compared to an age and gender-matched control group. CMR datasets of all patients were analyzed to calculate LV indexed end-diastolic (LVEDi) and end-systolic (LVESi) volumes and RV indexed end-diastolic (RVEDi) and end-systolic (RVESi) volumes, both LV ejection fraction (LVEF) and RV ejection fraction (RVEF). Moreover, LV and RV global longitudinal (GLS), circumferential (GCS) and radial (GRS) strain were measured. Results: The AC patients showed both higher LVEDi (p:0.002) and RVEDi (p:0.017) and lower LVEF (p: 0.016) as compared to control patients. Moreover, AC patients showed impaired LV-GLS (p < 0.001), LV-GRS (p < 0.001), LV-GCS (p < 0.001) and RV-GRS (p:0.026) as compared to control subjects. Group1 patients showed a significant reduction of LV-GRS (p < 0.05) and LV-GCS p < 0.01) as compared to control subjects. At univariate analysis LV-GCS was the most discriminatory parameter between Group1 vs heathy subjects with an optimal cut-off of −15.8 (Sensitivity: 74%; Specificity: 10%). Conclusions: In patients with AC biopsy-proven, CMR-FT could improve the diagnostic yield in the subset of patients who results negative for imaging TFC criteria resulting as useful gatekeeper for indication of myocardial biopsy in case of equivocal clinical and imaging presentation.

Cardiac magnetic resonance (CMR) is the best-suited cardiac imaging modality to provide reliable and reproducible information regarding cardiac volumes, function, and tissue characterization. In clinical practice, artificial intelligence (AI) in CMR can be applied to the whole process, ranging from sequence reconstruction, image analysis, and reporting of findings. In sequence reconstruction, the application of AI is mainly focused on the speed of reconstruction, while in image analysis and reporting AI is primarily aimed at automating the process of segmentation of both ventricles as well as fibrotic tissue. Within the realm of tissue characterization, AI can also be helpful in the depiction of tissue abnormalities from noncontrast images. Finally, AI can be useful for prognostic stratification.

Background: Despite the low spatial resolution of 2D-multisegment late gadolinium enhancement (2D-MSLGE) sequences, it may be useful in uncooperative patients instead of standard 2D single segmented inversion recovery gradient echo late gadolinium enhancement sequences (2D-SSLGE). The aim of the study is to assess the feasibility and comparison of 2D-MSLGE reconstructed with artificial intelligence reconstruction deep learning noise reduction (NR) algorithm compared to standard 2D-SSLGE in consecutive patients with ischemic cardiomyopathy (ICM). Methods: Fifty-seven patients with known ICM referred for a clinically indicated CMR were enrolled in this study. 2D-MSLGE were reconstructed using a growing level of NR (0%,25%,50%,75%and 100%). Subjective image quality, signal to noise ratio (SNR) and contrast to noise ratio (CNR) were evaluated in each dataset and compared to standard 2D-SSLGE. Moreover, diagnostic accuracy, LGE mass and scan time were compared between 2D-MSLGE with NR and 2D-SSLGE. Results: The application of NR reconstruction ≥50% to 2D-MSLGE provided better subjective image quality, CNR and SNR compared to 2D-SSLGE (p < 0.01). The best compromise in terms of subjective and objective image quality was observed for values of 2D-MSLGE 75%, while no differences were found in terms of LGE quantification between 2D-MSLGE versus 2D-SSLGE, regardless the NR applied. The sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 2D-MSLGE NR 75% were 87.77%,96.27%,96.13%,88.16% and 94.22%, respectively. Time of acquisition of 2D-MSLGE was significantly shorter compared to 2D-SSLGE (p < 0.01). Conclusion: When compared to standard 2D-SSLGE, the application of NR reconstruction to 2D-MSLGE provides superior image quality with similar diagnostic accuracy.

The recently published 2019 guidelines on chronic coronary syndromes (CCS) focus on the need for noninvasive imaging modalities to accurately establish the diagnosis of coronary artery disease (CAD) and assess the risk of clinical scenario occurrence. Appropriate patient management should rely on controlling symptoms, improving prognosis, and guiding each therapeutic strategy as well as monitoring disease progress. Among the noninvasive imaging modalities, cardiovascular magnetic resonance (CMR) has gained broad acceptance in past years due to its unique features in providing a complete assessment of CAD through data on cardiac anatomy and function and myocardial viability, with high spatial and temporal resolution and without ionizing radiation. In detail, evaluation of the presence and extent of myocardial ischemia through stress CMR (S-CMR) has shown a high rule-in power in detecting functionally significant coronary artery stenosis in patients suspected of CCS. Moreover, S-CMR technique may add significant prognostic value, as demonstrated by different studies which have progressively evidenced the valuable power of this multiparametric imaging modality in predicting adverse cardiac events. The latest scientific progress supports a greater expansion of S-CMR with improvement of quantitative myocardial perfusion analysis, myocardial strain, and native mapping within the same examination. Although further study is warranted, these techniques, which are currently mostly restricted to the research field, are likely to become increasingly prevalent in the clinical setting with the scope of increasing accuracy in the selection of patients to be sent to invasive revascularization. This review investigates the diagnostic and prognostic role of S-CMR in the context of CAD, by analysing a strong, long-standing, scientific evidence together with an appraisal of new advanced techniques which may potentially enrich CAD management in the next future.

Mitral valve prolapse is a common cardiac condition, with an estimated prevalence between 1% and 3%. Most patients have a benign course, but ever since its initial description mitral valve prolapse has been associated to sudden cardiac death. Although the causal relationship between mitral valve prolapse and sudden cardiac death has never been clearly demonstrated, different factors have been implicated in arrhythmogenesis in patients with mitral valve prolapse. In this work, we offer a comprehensive overview of the etiology and the genetic background, epidemiology, pathophysiology, and we focus on the state-of-the-art imaging-based diagnosis of mitral valve prolapse. Going beyond the classical, well-described clinical factors, such as young age, female gender and auscultatory findings, we investigate multimodality imaging features, such as alterations of anatomy and function of the mitral valve and its leaflets, the structural and contractile anomalies of the myocardium, all of which have been associated to sudden cardiac death.

Epicardial adipose tissue (EAT) represents the fat depot located between the myocardium and the visceral pericardial layer. Far from being an inert tissue, EAT has been recognized as secreting a large amount of bioactive molecules called adipokines, which have numerous exocrine and paracrine effects. Recent evidence demonstrates that pericoronary adipose tissue (PCAT) – the EAT directly surrounding the coronary arteries – has a complex bidirectional interaction with the underlying vascular wall. While in normal conditions this mutual cross-talk helps maintain the homeostasis of the vascular wall, dysfunctional PCAT produces deleterious pro-inflammatory adipokines involved in atherogenesis. Importantly, PCAT inflammation has been associated with coronary artery disease (CAD) and major cardiovascular events. This review aims to provide an overview of the imaging techniques used to assess EAT, with a specific focus on cardiac computed tomography (CCT), which has become the key modality in this field. In contrast to echocardiography and cardiac magnetic resonance (CMR), CCT is not only able to visualize and precisely quantify EAT, but also to assess the coronary arteries and the PCAT simultaneously. In recent years, several papers have shown the utility of using CCT-derived PCAT attenuation as a surrogate measure of coronary inflammation. This noninvasive imaging biomarker may potentially be used to monitor patient responses to new antinflammatory drugs for the treatment of CAD.

Aims: The aim of this registry was to evaluate the additional prognostic value of a composite cardiac magnetic resonance (CMR)-based risk score over standard-of-care (SOC) evaluation in a large cohort of consecutive unselected non-ischaemic cardiomyopathy (NICM) patients. Methods and results: In the DERIVATE registry (www.clinicaltrials.gov/registration: RCT#NCT03352648), 1000 (derivation cohort) and 508 (validation cohort) NICM patients with chronic heart failure (HF) and left ventricular ejection fraction [removed]3 segments with midwall fibrosis on late gadolinium enhancement (LGE) were the only independent predictors of all-cause mortality (HR: 1.036, 95% CI: 1.0117-1.056, P < 0.001 and HR: 2.077, 95% CI: 1.211-3.562, P = 0.008, respectively). For MAACE, the independent predictors were male gender, left ventricular end-diastolic volume index by CMR (CMR-LVEDVi), and >3 segments with midwall fibrosis on LGE (HR: 2.131, 95% CI: 1.231-3.690, P = 0.007; HR: 3.161, 95% CI: 1.750-5.709, P < 0.001; and HR: 1.693, 95% CI: 1.084-2.644, P = 0.021, respectively). A composite clinical and CMR-based risk score provided a net reclassification improvement of 63.7% (P < 0.001) for MAACE occurrence when added to the model based on SOC evaluation. These findings were confirmed in the validation cohort. Conclusion: In a large multicentre, multivendor cohort registry reflecting daily clinical practice in NICM work-up, a composite clinical and CMR-based risk score provides incremental prognostic value beyond SOC evaluation, which may have impact on the indication of implantable cardioverter-defibrillator implantation.

Coronavirus disease 2019 (COVID-19) has become a rapid worldwide pandemic. While COVID-19 primarily manifests as an interstitial pneumonia and severe acute respiratory distress syndrome, severe involvement of other organs has been documented. In this article, we will review the role of non-contrast chest computed tomography in the diagnosis, follow-up and prognosis of patients affected by COVID-19 pneumonia with a detailed description of the imaging findings that may be encountered. Given that patients with COVID-19 may also suffer from coagulopathy, we will discuss the role of CT pulmonary angiography in the detection of acute pulmonary embolism. Finally, we will describe more advanced applications of CT in the differential diagnosis of myocardial injury with an emphasis on ruling out acute coronary syndrome and myocarditis.

Objectives: T1 mapping (T1-map) and cardiac magnetic resonance feature tracking (CMR-FT) techniques have been introduced for the early detection of interstitial myocardial fibrosis and deformation abnormalities. We sought to demonstrate that T1-map and CMR-FT may identify the presence of subclinical myocardial structural changes in patients with mitral valve prolapse (MVP). Methods: Consecutive MVP patients with moderate-to-severe mitral regurgitation and comparative matched healthy subjects were prospectively enrolled and underwent CMR-FT analysis to calculate 2D global and segmental circumferential (CS) and radial strain (RS) and T1-map to determine global and segmental native T1 (nT1) values. Results: Seventy-three MVP patients (mean age, 57 ± 13 years old; male, 76%; regurgitant volume, 57 ± 21 mL) and 42 matched control subjects (mean age, 56 ± 18 years; male, 74%) were included. MVP patients showed a lower global CS (− 16.3 ± 3.4% vs. − 17.8 ± 1.9%, p = 0.020) and longer global nT1 (1124.9 ± 97.7 ms vs. 1007.4 ± 26.1 ms, p < 0.001) as compared to controls. Moreover, MVP patients showed lower RS and CS in basal (21.6 ± 12.3% vs. 27.6 ± 8.9%, p = 0.008, and − 13.0 ± 6.7% vs. − 14.9 ± 4.1%, p = 0.013) and mid-inferolateral (20.6 ± 10.7% vs. 28.4 ± 8.7%, p < 0.001, and − 12.8 ± 6.3% vs. − 16.5 ± 4.0%, p < 0.001) walls as compared to other myocardial segments. Similarly, MVP patients showed longer nT1 values in basal (1080 ± 68 ms vs. 1043 ± 43 ms, p < 0.001) and mid-inferolateral (1080 ± 77 ms vs. 1034 ± 37 ms, p < 0.001) walls as compared to other myocardial segments. Of note, nT1 values were significantly correlated with CS (r, 0.36; p < 0.001) and RS (r, 0.37; p < 0.001) but not with regurgitant volume. Conclusions: T1-map and CMR-FT identify subclinical left ventricle tissue changes in patients with MVP. Further studies are required to correlate these subclinical tissue changes with the outcome. Key Points: • T1 mapping (T1-map) and cardia

Cardiac computed tomography angiography (CCTA) is widely used as a diagnostic tool for evaluation of coronary artery disease (CAD). Despite the excellent capability to rule-out CAD, CCTA may overestimate the degree of stenosis; furthermore, CCTA analysis can be time consuming, often requiring advanced postprocessing techniques. In consideration of the most recent ESC guidelines on CAD management, which will likely increase CCTA volume over the next years, new tools are necessary to shorten reporting time and improve the accuracy for the detection of ischemia-inducing coronary lesions. The application of artificial intelligence (AI) may provide a helpful tool in CCTA, improving the evaluation and quantification of coronary stenosis, plaque characterization, and assessment of myocardial ischemia. Furthermore, in comparison with existing risk scores, machine-learning algorithms can better predict the outcome utilizing both imaging findings and clinical parameters. Medical AI is moving from the research field to daily clinical practice, and with the increasing number of CCTA examinations, AI will be extensively utilized in cardiac imaging. This review is aimed at illustrating the state of the art in AI-based CCTA applications and future clinical scenarios.

Stress computed tomography perfusion (Stress-CTP) and computed tomography-derived fractional flow reserve (FFRCT) are functional techniques that can be added to coronary computed tomography angiography (cCTA) to improve the management of patients with suspected coronary artery disease (CAD). This retrospective analysis from the PERFECTION study aims to assess the impact of their availability on the management of patients with suspected CAD scheduled for invasive coronary angiography (ICA) and invasive FFR. The management plan was defined as optimal medical therapy (OMT) or revascularization and was recorded for the following strategies: cCTA alone, cCTA+FFRCT, cCTA+Stress-CTP and cCTA+FFRCT+Stress-CTP. In 291 prospectively enrolled patients, cCTA+FFRCT, cCTA+Stress-CTP and cCTA+FFRCT+Stress-CTP showed a similar rate of reclassification of cCTA findings when FFRCT and Stress-CTP were added to cCTA. cCTA, cCTA+FFRCT, cCTA+Stress-CTP and cCTA+FFRCT+Stress-CTP showed a rate of agreement versus the final therapeutic decision of 63%, 71%, 89%, 84% (cCTA+Stress-CTP and cCTA+FFRCT+Stress-CTP vs cCTA and cCTA+FFRCT: p < 0.01), respectively, and a rate of agreement in terms of the vessels to be revascularized of 57%, 64%, 74%, 71% (cCTA+Stress-CTP and cCTA+FFRCT+Stress-CTP vs cCTA and cCTA+FFRCT: p < 0.01), respectively, with an effective radiation dose (ED) of 2.9 ± 1.3 mSv, 2.9 ± 1.3 mSv, 5.9 ± 2.7 mSv, and 3.1 ± 2.1 mSv. The addition of FFRCT and Stress-CTP improved therapeutic decision-making compared to cCTA alone, and a sequential strategy with cCTA+FFRCT+Stress-CTP represents the best compromise in terms of clinical impact and radiation exposure.

Non-ischemic cardiomyopathies represent a heterogeneous group of myocardial diseases potentially leading to heart failure, life-threatening arrhythmias, and eventually death. Myocardial dysfunction is associated with different underlying pathological processes, ultimately inducing changes in morphological appearance. Thus, classification based on presenting morphological phenotypes has been proposed, i.e., dilated, hypertrophic, restrictive, and right ventricular cardiomyopathies. In light of the key diagnostic and prognostic role of morphological and functional features, cardiovascular imaging has emerged as key element in the clinical workflow of suspected cardiomyopathies, and above all, cardiovascular magnetic resonance (CMR) represents the ideal technique to be used: thanks to its physical principles, besides optimal spatial and temporal resolutions, incomparable contrast resolution allows to assess myocardial tissue abnormalities in detail. Traditionally, weighted images and late enhancement images after gadolinium-based contrast agent administration have been used to perform tissue characterization, but in the last decade quantitative assessment of pre-contrast longitudinal relaxation time (native T1), post-contrast longitudinal relaxation time (post-contrast T1) and transversal relaxation time (T2), all displayed with dedicated pixel-wise color-coded maps (mapping), has contributed to give precious knowledge insight, with positive influence of diagnostic accuracy and prognosis assessment, mostly in the setting of the hypertrophic phenotype. This review aims to describe the available evidence of the role of mapping techniques in the assessment of hypertrophic phenotype, and to suggest their integration in the routine CMR evaluation of newly diagnosed cardiomyopathies with increased wall thickness.

In the context of chronic coronary syndromes (CCS), coronary computed tomography angiography (CCTA) has gained broad acceptance as a noninvasive anatomical imaging tool with ability of excluding coronary stenosis with strong negative predictive value. Atherosclerotic plaque lesions are independent predictors of cardiovascular outcomes in high risk patients with known coronary artery disease (CAD). Calcium detection is commonly expressed through the coronary artery calcium score (CACS), but further research is warranted to confirm the powerness of a CACS-only strategy in both diagnosis and prognosis assessment. Recent studies evidence how defined plaque composition characteristics effectively relate to the risk of plaque instabilization and the overall ischemic burden. Fractional flow reserve from CCTA (FFR-CT) has been demonstrated as a reliable method for noninvasive functional evaluation of coronary lesions severity, while the assessment of perfusion imaging under stress conditions is growing as a useful tool for assessment of myocardial ischemia. Moreover, specific applications in procedural planning of transcatheter valve substitution and follow-up of heart transplantation have gained recent importance. This review illustrates the incremental role of CCTA, which can potentially revolutionize the diagnosis and management pathway within the wide clinical spectrum of CCS.

Background and aims: Artificial intelligence (AI) is increasing its role in diagnosis of patients with suspicious coronary artery disease. The aim of this manuscript is to develop a deep convolutional neural network (CNN) to classify coronary computed tomography angiography (CCTA) in the correct Coronary Artery Disease Reporting and Data System (CAD-RADS) category. Methods: Two hundred eighty eight patients who underwent clinically indicated CCTA were included in this single-center retrospective study. The CCTAs were stratified by CAD-RADS scores by expert readers and considered as reference standard. A deep CNN was designed and tested on the CCTA dataset and compared to on-site reading. The deep CNN analyzed the diagnostic accuracy of the following three Models based on CAD-RADS classification: Model A (CAD-RADS 0 vs CAD-RADS 1–2 vs CAD-RADS 3,4,5), Model 1 (CAD-RADS 0 vs CAD-RADS>0), Model 2 (CAD-RADS 0–2 vs CAD-RADS 3–5). Time of analysis for both physicians and CNN were recorded. Results: Model A showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 47%, 74%, 77%, 46% and 60%, respectively. Model 1 showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 66%, 91%, 92%, 63%, 86%, respectively. Conversely, Model 2 demonstrated the following sensitivity, specificity, negative predictive value, positive predictive value and accuracy: 82%, 58%, 74%, 69%, 71%, respectively. Time of analysis was significantly lower using CNN as compared to on-site reading (530.5 ± 179.1 vs 104.3 ± 1.4 sec, p=0.01) Conclusions: Deep CNN yielded accurate automated classification of patients with CAD-RADS.

Objectives: This study sought to compare the diagnostic accuracy of coronary computed tomography angiography (cCTA) with that of cCTA+fractional flow reserve derived from cCTA datasets (FFRCT) and that of cCTA+static stress-computed tomography perfusion (stress-CTP) in detecting functionally significant coronary artery lesions using invasive coronary angiography (ICA) plus invasive FFR as the reference standard. Background: FFRCT and static stress-CTP are new techniques that combine anatomy and functional evaluation to improve assessment of coronary artery disease (CAD) using cCTA. Methods: A total of 147 consecutive symptomatic patients scheduled for clinically indicated ICA+invasive FFR were evaluated with cCTA, FFRCT, and stress-CTP. Results: Vessel-based and patient-based sensitivity, specificity, and negative predictive values, and positive predictive values, and accuracy rates of cCTA were 99%, 76%, 100%, 61%, 82%, and 95%, 54%, 94%, 63%, 73%, respectively. cCTA+FFRCT showed vessel-based and patient-based sensitivity, specificity, and negative predictive values, and positive predictive values and accuracy rates of 88%, 94%, 95%, 84%, 92%, and 90%, 85%, 92%, 83%, 87%, respectively. Finally, cCTA+stress-CTP showed vessel-based and patient-based sensitivity, specificity, and negative predictive values, and positive predictive values and accuracy rates of 92%, 95%, 97%, 87%, 94% and 98%, 87%, 99%, 86%, 92%, respectively. Both FFRCT and stress-CTP significantly improved specificity and positive predictive values compared to those of cCTA alone. The area under the curve to detect flow-limiting stenoses of cCTA, cCTA+FFRCT, and cCTA+CTP were 0.89, 0.93, 0.92, and 0.90, 0.94, and 0.93 in a vessel-based and patient-based model, respectively, with significant additional values for both cCTA+FFRCT and cCTA+CTP versus cCTA alone (p < 0.001) but no differences between cCTA+FFRCT versus cCTA+CTP. Conclusions: FFRCT and stress-CTP in addition to cCTA are valid and compara

Objectives: This study aimed to evaluate the correlation between different haptoglobin (Hp)phenotypes and myocardial infarction characteristics as detected by cardiac magnetic resonance (CMR)in consecutive patients after ST-segment elevation myocardial infarction (STEMI). Background: Hp is a plasma protein that prevents iron-mediated oxidative tissue damage. CMR has emerged as the gold standard technique to detect left ventricular ejection fraction (LVEF), extent of scar with late gadolinium enhancement (LGE)technique, microvascular obstruction (MVO), and myocardial hemorrhage (MH)in patients with STEMI treated by primary percutaneous coronary intervention (pPCI). Methods: A total of 145 consecutive STEMI patients (mean age 62.2 ± 10.3 years; 78% men)were prospectively enrolled and underwent Hp phenotyping and CMR assessment within 1 week after STEMI. Results: CMR showed an area at risk (AAR)involving 26.6 ± 19.1% of left ventricular (LV)mass with a late LGE extent of 15.2 ± 13.1% of LV mass. MVO and MH occurred in 38 (26%)and 12 (8%)patients, respectively. Hp phenotypes 1-1, 2-1, 2-2 were observed in 15 (10%), 62 (43%), and 68 (47%), respectively. Multivariable analysis demonstrated that body mass index, Hp2-2, diabetes, and peak troponin I were independent predictors of MVO with Hp2-2 associated with the highest odds ratio (OR)(OR: 5.5 [95% confidence interval [CI]: 2.1 to 14.3; p < 0.001]). Hp2-2 significantly predicted both the presence (area under the curve [AUC]: 0.63 [95% CI: 0.53 to 0.72; p = 0.008])and extent of MVO (AUC: 0.63 [95% CI: 0.54 to 0.72; p = 0.007]). Conclusions: Hp phenotype is an independent predictor of MVO. Therefore, Hp phenotyping could be used for risk stratification and may be useful in assessing new therapies to reduce myocardial reperfusion injury in patients with STEMI.

Background: Coronary artery fractional flow reserve (FFR) derived from CT angiography (FFTCT) enables functional assessment of coronary stenosis. Prior clinical trials showed 13%-33% of coronary CT angiography studies had insufficient quality for quantitative analysis with FFRCT. Purpose: To determine the rejection rate of FFRCT analysis and to determine factors associated with technically unsuccessful calculation of FFRCT. Materials and Methods: Prospectively acquired coronary CT angiography scans submitted as part of the Assessing Diagnostic Value of Noninvasive FFRCT in Coronary Care (ADVANCE) registry (https://ClinicalTrials.gov: NCT02499679) and coronary CT angiography series submitted for clinical analysis were included. The primary outcome was the FFRCT rejection rate (defined as an inability to perform quantitative analysis with FFRCT). Factors that were associated with FFRCT rejection rate were assessed with multiple linear regression. Results: In the ADVANCE registry, FFRCT rejection rate due to inadequate image quality was 2.9% (80 of 2778 patients; 95% confidence interval [CI]: 2.1%, 3.2%). In the 10 621 consecutive patients who underwent clinical analysis, the FFRCT rejection rate was 8.4% (n = 892; 95% CI: 6.2%, 7.2%; P , .001 vs the ADVANCE cohort). The main reason for the inability to perform FFRCT analysis was the presence of motion artifacts (63 of 80 [78%] and 729 of 892 [64%] in the ADVANCE and clinical cohorts, respectively). At multivariable analysis, section thickness in the ADVANCE (odds ratio [OR], 1.04; 95% CI: 1.001, 1.09; P = .045) and clinical (OR, 1.03; 95% CI: 1.02, 1.04; P , .001) cohorts and heart rate in the ADVANCE (OR, 1.05; 95% CI: 1.02, 1.08; P , .001) and clinical (OR, 1.06; 95% CI: 1.05, 1.07; P , .001) cohorts were independent predictors of rejection. Conclusion: The rates for technically unsuccessful CT-derived fractional flow reserve in the ADVANCE registry and in a large clinical cohort were 2.9% and 8.4%, respectively. Th

Objectives: The aims of the study were to test the diagnostic accuracy of integrated evaluation of dynamic myocardial computed tomography perfusion (CTP) on top of coronary computed tomography angiography (cCTA) plus fractional flow reserve computed tomography derived (FFRCT) by using a whole-heart coverage computed tomography (CT) scanner as compared with clinically indicated invasive coronary angiography (ICA) and invasive fractional flow reserve (FFR). Background: Recently, new techniques such as dynamic stress computed tomography perfusion (stress-CTP) emerged as potential strategies to combine anatomical and functional evaluation in a one-shot scan. However, previous experiences with this technique were associated with high radiation exposure. Methods: Eighty-five consecutive symptomatic patients scheduled for ICA were prospectively enrolled. All patients underwent rest cCTA followed by stress dynamic CTP with a whole-heart coverage CT scanner (Revolution CT, GE Healthcare, Milwaukee, Wisconsin). FFRCT was also measured by using the rest cCTA dataset. The diagnostic accuracy to detect functionally significant coronary artery disease (CAD) in a vessel-based model of cCTA alone, cCTA+FFRCT, cCTA+CTP, or cCTA+FFRCT+CTP were assessed and compared by using ICA and invasive FFR as reference. The overall effective dose of dynamic CTP was also measured. Results: The prevalence of obstructive CAD and functionally significant CAD was 77% and 57%, respectively. The sensitivity and specificity of cCTA alone, cCTA+FFRCT, and cCTA+CTP were 83% and 66%, 86% and 75%, and 73% and 86%, respectively. Both the addition of FFRCT and CTP improves the area under the curve (AUC: 0.876 and 0.878, respectively) as compared with cCTA alone (0.826; p < 0.05). The sequential strategy of cCTA+FFRCT+CTP showed the highest AUC (0.919; p < 0.05) as compared with all other strategies. The mean effective radiation dose (ED) for cCTA and stress CTP was 2.8 ± 1.2 mSv and 5.3 ± 0.7 mSv, respe