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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

Objective: Our study aimed to explore with cardiovascular magnetic resonance (CMR) the impact of left atrial (LA) and left ventricular (LV) myocardial strain in patients with acute pericarditis and to investigate their possible prognostic significance in adverse outcomes. Method: This retrospective study performed CMR scans in 36 consecutive patients with acute pericarditis (24 males, age 52 [23–52]). The primary endpoint was the combination of recurrent pericarditis, constrictive pericarditis, and surgery for pericardial diseases defined as pericardial events. Atrial and ventricular strain function were performed on conventional cine SSFP sequences. Results: After a median follow-up time of 16 months (interquartile range [13–24]), 12 patients with acute pericarditis reached the primary endpoint. In multivariable Cox regression analysis, LA reservoir and LA conduit strain parameters were all independent determinants of adverse pericardial diseases. Conversely, LV myocardial strain parameters did not remain an independent predictor of outcome. With receiving operating characteristics curve analysis, LA conduit and reservoir strain showed excellent predictive performance (area under the curve of 0.914 and 0.895, respectively) for outcome prediction at 12 months. Conclusion: LA reservoir and conduit mechanisms on CMR are independently associated with a higher risk of adverse pericardial events. Including atrial strain parameters in the management of acute pericarditis may improve risk stratification. Clinical relevance statement: Atrial strain could be a suitable non-invasive and non-contrast cardiovascular magnetic resonance parameter for predicting adverse pericardial complications in patients with acute pericarditis. Key Points: • Myocardial strain is a well-validated CMR parameter for risk stratification in cardiovascular diseases. • LA reservoir and conduit functions are significantly associated with adverse pericardial events. • Atrial strain may serve

OBJECTIVES: Aortic valve neocuspidalization aims to replace the 3 aortic cusps with autologous pericardium pre-treated with glutaraldehyde, and it is a surgical alternative to the classical aortic valve replacement (AVR). Image-based patient-specific computational fluid dynamics allows the derivation of shear stress on the aortic wall [wall shear stress (WSS)]. Previous studies support a potential link between increased WSS and histological alterations of the aortic wall. The aim of this study is to compare the WSS of the ascending aorta in patients undergoing aortic valve neocuspidalization versus AVR with biological prostheses. METHODS: This is a prospective nonrandomized clinical trial. Each patient underwent a 4D-flow cardiac magnetic resonance scan after surgery, which informed patient-specific computational fluid dynamics models to evaluate WSS at the ascending aortic wall. The adjusted variables were calculated by summing the residuals obtained from a multivariate linear model (with ejection fraction and left ventricle outflow tract-aorta angle as covariates) to the mean of the variables. RESULTS: Ten patients treated with aortic valve neocuspidalization were enrolled and compared with 10 AVR patients. The aortic valve neocuspidalization group showed a significantly lower WSS in the outer curvature segments of the proximal and distal ascending aorta as compared to AVR patients (P = 0.0179 and 0.0412, respectively). WSS levels remained significantly lower along the outer curvature of the proximal aorta in the aortic valve neocuspidalization population, even after adjusting the WSS for the ejection fraction and the left ventricle outflow tract-aorta angle [2.44 Pa (2.17-3.01) vs 1.94 Pa (1.72-2.01), P = 0.02]. CONCLUSIONS: Aortic valve neocuspidalization hemodynamical features are potentially associated with a lower WSS in the ascending aorta as compared to commercially available bioprosthetic valves.

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.

Background: Most recent cardiac implantable electronic devices (CIEDs) can safely undergo a cardiovascular magnetic resonance (CMR) scan under certain conditions, but metal artifacts may degrade image quality. The aim of this study was to assess the overall diagnostic yield of CMR and the extent of metal artifacts in a multicenter, multivendor study on CIED patients referred for CMR. Methods: We analyzed 309 CMR scans from 292 patients (age 57 ± 16 years, 219 male) with an MR-conditional pacemaker (n = 122), defibrillator (n = 149), or loop recorder (n = 38); CMR scans were performed in 10 centers from 2012 to 2020; MR-unsafe implants were excluded. Clinical and device parameters were recorded before and after the CMR scan. A visual analysis of metal artifacts was performed for each sequence on a segmental basis, based on a 5-point artifact score. Results: The vast majority of CMR scans (n = 255, 83%) were completely performed, while only 32 (10%) were interrupted soon after the first sequences and 22 (7%) were only partly acquired; CMR quality was non-diagnostic in 34 (11%) scans, poor (<1/3 sequences were diagnostic) in 25 (8%), or acceptable (1/3 to 2/3 sequences were diagnostic) in 40 (13%), while most scans (n = 201, 68%) were of overall good quality. No adverse event or device malfunctioning occurred, and only nonsignificant changes in device parameters were recorded. The most affected sequences were SSFP (median score 0.32 [interquartile range 0.07–0.91]), followed by GRE (0.18 [0.02–0.59]) and LGE (0.14 [0.02–0.55]). ICDs induced more artifacts (median score in SSFP images 0.87 [0.50–1.46]) than PMs (0.11 [0.03–0.28]) or ILRs (0.11 [0.00–0.56]). Moreover, most artifacts were located in the anterior, anteroseptal, anterolateral, and apical segments of the LV and in the outflow tract of the RV. Conclusions: CMR is a versatile imaging technique, with a high safety profile and overall good image quality even in patients with MR-conditional CIEDs. Several stra

Coronary artery disease (CAD) is one of the major causes of mortality and morbidity worldwide, with a high socioeconomic impact. Currently, various guidelines and recommendations have been published about chronic coronary syndromes (CCS). According to the recent European Society of Cardiology guidelines on chronic coronary syndrome, a multimodal imaging approach is strongly recommended in the evaluation of patients with suspected CAD. Today, in the current practice, non-invasive imaging methods can assess coronary anatomy through coronary computed tomography angiography (CCTA) and/or inducible myocardial ischemia through functional stress testing (stress echocardiography, cardiac magnetic resonance imaging, single photon emission computed tomography—SPECT, or positron emission tomography—PET). However, recent trials (ISCHEMIA and REVIVED) have cast doubt on the previous conception of the management of patients with CCS, and nowadays it is essential to understand the limitations and strengths of each imaging method and, specifically, when to choose a functional approach focused on the ischemia versus a coronary anatomy-based one. Finally, the concept of a pathophysiology-driven treatment of these patients emerged as an important goal of multimodal imaging, integrating ‘anatomical’ and ‘functional’ information. The present review aims to provide an overview of non-invasive imaging modalities for the comprehensive management of CCS patients.

Purpose: Takotsubo cardiomyopathy (TTC) is a transient but severe acute myocardial dysfunction with a wide range of outcomes from favorable to life-threatening. The current risk stratification scores of TTC patients do not include cardiac magnetic resonance (CMR) parameters. To date, it is still unknown whether and how clinical, trans-thoracic echocardiography (TTE), and CMR data can be integrated to improve risk stratification. Methods: EVOLUTION (Exploring the eVolution in prognOstic capabiLity of mUlti-sequence cardiac magneTIc resOnance in patieNts affected by Takotsubo cardiomyopathy) is a multicenter, international registry of TTC patients who will undergo a clinical, TTE, and CMR evaluation. Clinical data including demographics, risk factors, comorbidities, laboratory values, ECG, and results from TTE and CMR analysis will be collected, and each patient will be followed-up for in-hospital and long-term outcomes. Clinical outcome measures during hospitalization will include cardiovascular death, pulmonary edema, arrhythmias, stroke, or transient ischemic attack. Clinical long-term outcome measures will include cardiovascular death, pulmonary edema, heart failure, arrhythmias, sudden cardiac death, and major adverse cardiac and cerebrovascular events defined as a composite endpoint of death from any cause, myocardial infarction, recurrence of TTC, transient ischemic attack, and stroke. We will develop a comprehensive clinical and imaging score that predicts TTC outcomes and test the value of machine learning models, incorporating clinical and imaging parameters to predict prognosis. Conclusions: The main goal of the study is to develop a comprehensive clinical and imaging score, that includes TTE and CMR data, in a large cohort of TTC patients for risk stratification and outcome prediction as a basis for possible changes in patient management.

Objective: To assess the value of opportunistic biomarkers derived from chest CT performed at hospital admission of COVID-19 patients for the phenotypization of high-risk patients. Methods: In this multicentre retrospective study, 1845 consecutive COVID-19 patients with chest CT performed within 72 h from hospital admission were analysed. Clinical and outcome data were collected by each center 30 and 80 days after hospital admission. Patients with unknown outcomes were excluded. Chest CT was analysed in a single core lab and behind pneumonia CT scores were extracted opportunistic data about atherosclerotic profile (calcium score according to Agatston method), liver steatosis (≤ 40 HU), myosteatosis (paraspinal muscle F < 31.3 HU, M < 37.5 HU), and osteoporosis (D12 bone attenuation < 134 HU). Differences according to treatment and outcome were assessed with ANOVA. Prediction models were obtained using multivariate binary logistic regression and their AUCs were compared with the DeLong test. Results: The final cohort included 1669 patients (age 67.5 [58.5–77.4] yo) mainly men 1105/1669, 66.2%) and with reduced oxygen saturation (92% [88–95%]). Pneumonia severity, high Agatston score, myosteatosis, liver steatosis, and osteoporosis derived from CT were more prevalent in patients with more aggressive treatment, access to ICU, and in-hospital death (always p < 0.05). A multivariable model including clinical and CT variables improved the capability to predict non-critical pneumonia compared to a model including only clinical variables (AUC 0.801 vs 0.789; p = 0.0198) to predict patient death (AUC 0.815 vs 0.800; p = 0.001). Conclusion: Opportunistic biomarkers derived from chest CT can improve the characterization of COVID-19 high-risk patients. Clinical relevance statement: In COVID-19 patients, opportunistic biomarkers of cardiometabolic risk extracted from chest CT improve patient risk stratification. Key Points: • In COVID-19 patients, several i

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.

Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.

Valvular regurgitation represents an important cause of cardiovascular morbidity and mortality. Imaging is pivotal in the evaluation of native valve regurgitation and echocardiography is the primary imaging modality for this purpose. The imaging assessment of valvular regurgitation should integrate quantification of the regurgitation, assessment of the valve anatomy and function, and the consequences of valvular disease on cardiac chambers. In clinical practice, the management of patients with valvular regurgitation largely relies on the results of imaging. It is crucial to provide standards that aim at establishing a baseline list of measurements to be performed when assessing native valve regurgitation. The present document aims to present clinical guidance for the multi-modality imaging assessment of native valvular regurgitation.

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..

Background: The aortic valve neocuspidalization (AVNeo) is an innovative surgical technique aiming at the reconstruction of the aortic valve using autologous pericardium. One of the main criticisms to AVNeo is the longer duration of the aortic clamping time (ACT) as compared to standard aortic valve replacement due to the sizing of the valve neocusps. Methods: We retrospectively enrolled 30 consecutives patients underwent AVNeo. For each patient we developed a 3D aortic root model (ARM) based on CT-scan datasets. We retrospectively compared the leaflets measurements performed during surgery with those obtained on the corresponding ARMs. Results: In 100% of cases no difference between the in vitro and in vivo measurements exceeded the acceptable error limit of 2 ​mm. The correlation of each single in vitro versus in vivo measurements demonstrates a strong coincidence between the two different methods of sizing (r ​> ​0,9, p ​< ​.0001). By analyzing the data considering the annulus perimeter and not the single cusp size, the perfect coincidence was to be found in 89.9% with a slight acceptable discrepancy (2 ​mm on total) in the remaining 10.1%. Conclusions: 3D-ARMs, printed from CT-scan, represent a reproducible process to obtain overlapping cusp sizes compared to those measured in-vivo, possibly reducing the ACT.

SARS-CoV-2 infection, responsible for COVID-19 outbreak, can cause cardiac complications, worsening outcome and prognosis. In particular, it can exacerbate any underlying cardiovascular condition, leading to atherosclerosis and increased plaque vulnerability, which may cause acute coronary syndrome. We review current knowledge on the mechanisms by which SARS-CoV-2 can trigger endothelial/myocardial damage and cause plaque formation, instability and deterioration. The aim of this review is to evaluate current non-invasive diagnostic techniques for coronary arteries evaluation in COVID-19 patients, such as coronary CT angiography and atherosclerotic plaque imaging, and their clinical implications. We also discuss the role of artificial intelligence, deep learning and radiomics in the context of coronary imaging in COVID-19 patients.

Ischemic chronic cardiomyopathy (ICC) is still one of the most common cardiac diseases leading to the development of myocardial ischemia, infarction, or heart failure. The application of several imaging modalities can provide information regarding coronary anatomy, coronary artery disease, myocardial ischemia and tissue characterization. In particular, coronary computed tomography angiography (CCTA) can provide information regarding coronary plaque stenosis, its composition, and the possible evaluation of myocardial ischemia using fractional flow reserve CT or CT perfusion. Cardiac magnetic resonance (CMR) can be used to evaluate cardiac function as well as the presence of ischemia. In addition, CMR can be used to characterize the myocardial tissue of hibernated or infarcted myocardium. Echocardiography is the most widely used technique to achieve information regarding function and myocardial wall motion abnormalities during myocardial ischemia. Nuclear medicine can be used to evaluate perfusion in both qualitative and quantitative assessment. In this review we aim to provide an overview regarding the different noninvasive imaging techniques for the evaluation of ICC, providing information ranging from the anatomical assessment of coronary artery arteries to the assessment of ischemic myocardium and myocardial infarction. In particular this review is going to show the different noninvasive approaches based on the specific clinical history of patients with ICC.

Ischemic cardiomyopathy (ICM) is one of the most common causes of congestive heart failure. In patients with ICM, tissue characterization with cardiac magnetic resonance imaging (CMR) allows for evaluation of myocardial abnormalities in acute and chronic settings. Myocardial edema, microvascular obstruction (MVO), intracardiac thrombus, intramyocardial hemorrhage, and late gadolinium enhancement of the myocardium are easily depicted using standard CMR sequences. In the acute setting, tissue characterization is mainly focused on assessment of ventricular thrombus and MVO, which are associated with poor prognosis. Conversely, in chronic ICM, it is important to depict late gadolinium enhancement and myocardial ischemia using stress perfusion sequences. Overall, with CMR's ability to accurately characterize myocardial tissue in acute and chronic ICM, it represents a valuable diagnostic and prognostic imaging method for treatment planning. In particular, tissue characterization abnormalities in the acute setting can provide information regarding the patients that may develop major adverse cardiac event and show the presence of ventricular thrombus; in the chronic setting, evaluation of viable myocardium can be fundamental for planning myocardial revascularization. In this review, the main findings on tissue characterization are illustrated in acute and chronic settings using qualitative and quantitative tissue characterization.

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

Background and aims: The aim of the present study is to evaluate whether advanced coronary atherosclerosis analysis by CCTA may improve prognostic stratification among diabetic patients at high cardiovascular risk (CV risk). Methods and results: The study population consisted of 265 consecutive diabetic patients at high CV risk who underwent CCTA for suspected CAD between January 2011 and December 2016. For every patients both traditional and advanced, qualitative and quantitative coronary plaque analysis were performed. The occurrence of cardiac death, ACS, and non-urgent revascularization were recorded at follow-up. Among the 265 patients enrolled, 21 were lost to follow-up, whereas 244 (92%) had a complete follow-up (mean 45 ± 22 months) and were classified at high (n = 67) or very high cardiovascular risk (n = 177), according to ESC Guidelines. A total of 63 events were recorded (3 Cardiac Death, 3 NSTEMI, 8 unstable angina, 36 late non-urgent revascularization and 13 non-cardiac death) in 57 different patients. Elevated fibro-fatty plaque volume was the only predictor of events over age, gender and traditional risk factor when ACS and MACE were considered as end-points [HR (95% CI) 6.01 (1.65–21.87), p = 0.006 and 3.46 (2.00–5.97); p < 0.001]. Conclusion: The present study confirms the prognostic role of advance coronary atherosclerosis evaluation beyond risk factors and stenosis severity, even in diabetics. Despite the very high cardiovascular risk of study population, a not negligible portion (23%) of patients exhibited totally normal coronaries.

The diagnosis of acute myocarditis often involves several noninvasive techniques that can provide information regarding volumes, ejection fraction, and tissue characterization. In particular, echocardiography is extremely helpful for the evaluation of biventricular volumes, strain and ejection fraction. Cardiac magnetic resonance, beyond biventricular volumes, strain, and ejection fraction allows to characterize myocardial tissue providing information regarding edema, hyperemia, and fibrosis. Contemporary cardiac computed tomography angiography (CCTA) can not only be extremely important for the assessment of coronary arteries, pulmonary arteries and aorta but also tissue characterization using CCTA can be an additional tool that can explain chest pain with a diagnosis of myocarditis.

Sudden cardiac death (SCD) is a potentially fatal event usually caused by a cardiac arrhythmia, which is often the result of coronary artery disease (CAD). Up to 80% of patients suffering from SCD have concomitant CAD. Arrhythmic complications may occur in patients with acute coronary syndrome (ACS) before admission, during revascularization procedures, and in hospital intensive care monitoring. In addition, about 20% of patients who survive cardiac arrest develop a transmural myocardial infarction (MI). Prevention of ACS can be evaluated in selected patients using cardiac computed tomography angiography (CCTA), while diagnosis can be depicted using electrocardiography (ECG), and complications can be evaluated with cardiac magnetic resonance (CMR) and echocardiography. CCTA can evaluate plaque, burden of disease, stenosis, and adverse plaque characteristics, in patients with chest pain. ECG and echocardiography are the first-line tests for ACS and are affordable and useful for diagnosis. CMR can evaluate function and the presence of complications after ACS, such as development of ventricular thrombus and presence of myocardial tissue characterization abnormalities that can be the substrate of ventricular arrhythmias.

Purpose: To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients’ risk stratification. Material and Methods: In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web–mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020). Results: The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816–0.867) on wave 1 and was used to build a 0–100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402–0.8766). Conclusions: AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.

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.