Challenges in cardiology: diagnosis of native and prosthetic valve endocarditis.

New cardiovascular imaging technologies have strongly influenced the diagnosis of bacterial endocarditis [infective endocarditis (IE)]. The clinical presentation of IE is polymorphic, which explains the difficulty of diagnosis and the delay in treatment. Symptoms such as fever, chills, loss of appetite and weight, and embolic phenomena strongly support the diagnosis of endocarditis, but are not always present, particularly in elderly or immuno-compromised patients. Moreover, subtle symptoms in patients at high risk for the development of IE, such as those with prosthetic valves or intracardiac devices, patients with congenital heart disease, and drug addicts should lead to the suspicion that a diagnosis of IE is highly probable. In this review, we will focus on the diagnosis of complex IE in native valves and prosthetic valves.

Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion.

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..

Computed tomography predictors of structural valve degeneration in patients undergoing transcatheter aortic valve implantation with balloon-expandable prostheses.

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 of the aortic annulus's geometrical characteristics for prosthesis sizing before transcatheter aortic valve implantation (TAVI). • Female sex and a small minimum aortic annulus diameter measured with CT are independent predictors of structural valve degeneration in patients undergoing TAVI with balloon-expandable valves.

Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment.

BACKGROUND: Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED). METHODS: In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi42(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability. RESULTS: In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF - 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s). CONCLUSIONS: Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.

Plaque assessment by coronary CT angiography may predict cardiac events in high risk and very high risk diabetic patients: A long-term follow-up study.

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 prognostic value of left ventricular dimensions at the time of transcatheter aortic valve replacement: A propensity-matched analysis.

AIMS: In the clinical practice a noteworthy proportion of severe symptomatic aortic stenosis patients presents with low-flow low-gradients features, these having reported a less favorable prognosis even when surgically or transcatheter treated. METHODS AND RESULTS: We retrospectively analyzed the prospectively collected data on 1051 consecutive patients undergone balloon-expandable transcatheter aortic valve replacement at our Institution from January 2008 to January 2020. We divided the population according with a mean aortic gradient (MAG) < or ≥40 mmHg and we performed a propensity-matched analysis based on the Society of Thoracic Surgery Score and age, obtaining two homogeneous groups of 314 patients each (Groups A and B, respectively). We then analyzed the outcomes of the two groups by implementing adjusted Cox models adjusted for significant clinical differences between the two groups, such as sex, ejection fraction, comorbidities and other variables not included in the propensity-matched analysis. The only variable associated with both cardiovascular and all-cause events was an ejection fraction ≤35%. Finally, a sensitivity analysis found that an ejection fraction ≤35% was associated with an increase cardiovascular and all-cause mortality only in patients with an indexed end-diastolic volume >97 ml/m2 (p = .0438 and .3363, respectively). CONCLUSIONS: In our series a MAG <40 mmHg was not found to be per se an independent risk factor for cardiac and all-cause mortality after transcatheter aortic valve replacement. The ejection fraction was found to be an independent risk factor only in the context of enlarged left ventricular dimensions.

Prognostic value of stress echocardiography assessed by the ABCDE protocol.

AIM: The aim of this study was to assess the prognostic value of ABCDE-SE in a prospective, large scale, multicentre, international, effectiveness study. Stress echocardiography (SE) was recently upgraded to the ABCDE protocol: step A, regional wall motion abnormalities; step B, B lines; step C, left ventricular contractile reserve; step D, Doppler-based coronary flow velocity reserve in left anterior descending coronary artery; and step E, electrocardiogram-based heart rate reserve. METHODS AND RESULTS: From July 2016 to November 2020, we enrolled 3574 all-comers (age 65 ± 11 years, 2070 males, 58%; ejection fraction 60 ± 10%) with known or suspected chronic coronary syndromes referred from 13 certified laboratories. All patients underwent clinically indicated ABCDE-SE. The employed stress modality was exercise (n = 952, with semi-supine bike, n = 887, or treadmill, n = 65 with adenosine for step D) or pharmacological stress (n = 2622, with vasodilator, n = 2151; or dobutamine, n = 471). SE response ranged from score 0 (all steps normal) to score 5 (all steps abnormal). All-cause death was the only endpoint. Rate of abnormal results was 16% for A, 30% for B, 36% for C, 28% for D, and 37% for E steps. During a median follow-up of 21 months (interquartile range: 13-36), 73 deaths occurred. Global X2 was 49.5 considering clinical variables, 50.7 after step A only (P = NS (not significant)) and 80.6 after B-E steps (P < 0.001 vs. step A). Annual mortality rate ranged from 0.4% person-year for score 0 up to 2.7% person-year for score 5. CONCLUSION: ABCDE-SE allows an effective prediction of survival in patients with chronic coronary syndromes.

T1 mapping and cardiac magnetic resonance feature tracking in mitral valve prolapse.

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 cardiac magnetic resonance feature tracking (CMR-FT) techniques have been introduced for the early detection of interstitial myocardial fibrosis and deformation abnormalities. • In MVP patients, we demonstrated a longer global nT1 with associated reduced global circumferential (CS) and radial strain (RS) as compared to control subjects. • Among MVP patients, the mid-basal left ventricle inferolateral wall showed longer nT1 with reduced CS and RS as compared to other myocardial segments. Further studies are required to correlate these subclinical tissue changes with the outcome.

Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images.

OBJECTIVE: The aim of this paper is to investigate the use of fully convolutional neural networks (FCNNs) to segment scar tissue in the left ventricle from cardiac magnetic resonance with late gadolinium enhancement (CMR-LGE) images. METHODS: A successful FCNN in the literature (the ENet) was modified and trained to provide scar-tissue segmentation. Two segmentation protocols (Protocol 1 and Protocol 2) were investigated, the latter limiting the scar-segmentation search area to the left ventricular myocardial tissue region. CMR-LGE from 30 patients with ischemic-heart disease were retrospectively analyzed, for a total of 250 images, presenting high variability in terms of scar dimension and location. Segmentation results were assessed against manual scar-tissue tracing using one-patient-out cross validation. RESULTS: Protocol 2 outperformed Protocol 1 significantly (p value < 0.05), with median sensitivity and Dice similarity coefficient equal to 88.07% [inter-quartile range (IQR) 18.84%] and 71.25% (IQR 31.82%), respectively. DISCUSSION: Both segmentation protocols were able to detect scar tissues in the CMR-LGE images but higher performance was achieved when limiting the search area to the myocardial region. The findings of this paper represent an encouraging starting point for the use of FCNNs for the segmentation of nonviable scar tissue from CMR-LGE images.

Automated, machine learning-based, 3D echocardiographic quantification of left ventricular mass.

BACKGROUND: Although 3D echocardiography (3DE) circumvents many limitations of 2D echocardiography by allowing direct measurements of left ventricular (LV) mass, it is seldom used in clinical practice due to time-consuming analysis. A recently developed 3DE machine learning (ML) approach allows automated determination of LV mass. We aimed to evaluate the accuracy of this new approach by comparing it to cardiac magnetic resonance (CMR) reference and to conventional 3DE volumetric analysis. METHODS: We prospectively studied 23 patients who underwent 3DE (Philips EPIQ) and CMR imaging on the same day. Single-beat wide-angle 3D datasets of the left ventricle were acquired. LV mass was quantified using the new automated software (Philips HeartModel) with manual corrections when necessary and using conventional volumetric analysis (TomTec). CMR analysis was performed by manual slice-by-slice tracing of LV endo- and epicardial boundaries. Reproducibility of the ML approach was assessed using repeated measurements and quantified by intra-class correlation (ICC) and coefficients of variation (CoV). RESULTS: Automated LV mass measurements were feasible in 20 patients (87%). The results were similar to CMR-derived values (Bland-Altman bias 5 g, limits of agreement ±37 g) and also to the conventional 3DE analysis (bias 7 g, ±27 g). Processing time was considerably shorter: 1.02 ± 0.24 minutes (CMR: 2.20 ± 0.13 minutes; TomTec: 2.36 ± 0.09 minutes), although manual corrections were performed in most patients. Repeated measurements showed high reproducibility: ICC = 0.99; CoV = 4 ± 5%. CONCLUSIONS: 3D Echocardiography analysis of LV mass using novel ML-based algorithm is feasible, fast, and accurate and may thus facilitate the incorporation of 3DE measurements of LV mass into clinical practice.

Fluid-Structure Interaction and In Vitro Analysis of a Real Bileaflet Mitral Prosthetic Valve to Gain Insight Into Doppler-Silent Thrombosis.

Prosthetic valve thrombosis (PVT) is a serious complication affecting prosthetic heart valves. The transvalvular mean pressure gradient (MPG) derived by Doppler echocardiography is a crucial index to diagnose PVT but may result in false negatives mainly in case of bileaflet mechanical valves (BMVs) in mitral position. This may happen because MPG estimation relies on simplifying assumptions on the transvalvular fluid dynamics or because Doppler examination is manual and operator dependent. A deeper understanding of these issues may allow for improving PVT diagnosis and management. To this aim, we used in vitro and fluid-structure interaction (FSI) modeling to simulate the function of a real mitral BMV in different configurations: normally functioning and stenotic with symmetric and completely asymmetric leaflet opening, respectively. In each condition, the MPG was measured in vitro, computed directly from FSI simulations and derived from the corresponding velocity field through a Doppler-like postprocessing approach. Following verification versus in vitro data, MPG computational data were analyzed to test their dependency on the severity of fluid-dynamic derangements and on the measurement site. Computed MPG clearly discriminated between normally functioning and stenotic configurations. They did not depend markedly on the site of measurement, yet differences below 3 mmHg were found between MPG values at the central and lateral orifices of the BMV. This evidence suggests a mild uncertainty of the Doppler-based evaluation of the MPG due to probe positioning, which yet may lead to false negatives when analyzing subjects with almost normal MPG.

Image quality and radiation dose of coronary CT angiography performed with whole-heart coverage CT scanner with intra-cycle motion correction algorithm in patients with atrial fibrillation.

OBJECTIVES: To evaluate image quality, coronary evaluability and radiation exposure of coronary CT angiography (CCTA) performed with whole-heart coverage cardiac-CT in patients with atrial fibrillation (AF). MATERIALS AND METHODS: We prospectively enrolled 164 patients with AF who underwent a clinically indicated CCTA with a 16-cm z-axis coverage scanner. In all patients CCTA was performed using prospective ECG-triggering with targeted RR interval. We evaluated image quality, coronary evaluability and effective dose (ED). Patients were divided in two subgroups based on heart rate (HR) during imaging. Group 1: 64 patients with low HR (<75 bpm), group 2: 100 patients with high HR (≥75 bpm). Written informed consent was obtained from all patients and the institutional ethics committee approved the study protocol. RESULTS: In a segment-based analysis, coronary evaluability was 98.4 % (2,577/2,620 segments) in the whole population, without significant differences between groups (1,013/1,024 (98.9 %) and 1,565/1,596 (98.1 %), for groups 1 and 2, respectively, p=0.15). Mean ED was similar in both groups (3.8±1.9 mSv and 3.9±2.1 mSv in groups 1 and 2, respectively, p=0.75) CONCLUSIONS: The whole-heart-coverage scanner could evaluate coronary arteries with high image quality and without increase in radiation exposure in AF patients, even in the high HR group. KEY POINTS: • Last-generation CT scanner improves coronary artery assessment in AF patients. • The new CT scanner enables low radiation exposure in AF patients. • Diagnostic ICA maybe avoided in AF patients with suspected CAD. • Whole-heart coverage CT scanner enables low radiation exposure in AF patients.

Atrial Fibrillation: Diagnostic Accuracy of Coronary CT Angiography Performed with a Whole-Heart 230-µm Spatial Resolution CT Scanner.

Purpose To assess image quality, interpretability, diagnostic accuracy, and radiation exposure of a computed tomography (CT) scanner with 16-cm coverage and 230-µm spatial resolution at coronary artery evaluation in patients with atrial fibrillation (AF) by using invasive coronary angiography (ICA) as the reference method and to compare the results with those obtained in patients with sinus rhythm (SR). Materials and Methods Written informed consent and institutional ethics committee approval were obtained. Between March 2015 and February 2016, 166 consecutive patients were prospectively enrolled (83 with AF, 83 with SR). They underwent ICA and coronary CT angiography performed with a whole-heart CT scanner. Image quality, coronary segment interpretability, effective dose (ED), and diagnostic accuracy were assessed at CT angiography and were compared with those attained with ICA. Diagnostic performance of the groups was compared with the pairwise McNemar test. Results Mean heart rate during scanning was 83 beats per minute ± 21 (standard deviation) in the AF group and 63 beats per minute ± 14 in the SR group (P < .01). Coronary interpretability was 98.5% in the AF group and 98.4% in the SR group (P = .96). In a segment-based analysis, sensitivity and specificity in the detection of coronary stenosis of more than 50% compared with detection of ICA were 96.4% and 98.7%, respectively, in the chronic AF group (P = .98) and 95.6% and 98.1%, respectively, in the SR group (P = .32). In a patient-based analysis, sensitivity and specificity were 95.2% and 97.6%, respectively, in the chronic AF group (P = .95) and 97.8% and 94.7%, respectively, in the SR group (P = .93). Conclusion Whole-heart CT enables evaluation of coronary arteries with high image quality, low radiation exposure, and high diagnostic accuracy in patients with chronic AF, with a diagnostic performance similar to that in patients with SR. © RSNA, 2017 Online supplemental material is available for this article.

Impact of an intra-cycle motion correction algorithm on overall evaluability and diagnostic accuracy of computed tomography coronary angiography.

OBJECTIVES: The aim of this study was to evaluate the impact of a novel intra-cycle motion correction algorithm (MCA) on overall evaluability and diagnostic accuracy of cardiac computed tomography coronary angiography (CCT). METHODS: From a cohort of 900 consecutive patients referred for CCT for suspected coronary artery disease (CAD), we enrolled 160 (18 %) patients (mean age 65.3 ± 11.7 years, 101 male) with at least one coronary segment classified as non-evaluable for motion artefacts. The CCT data sets were evaluated using a standard reconstruction algorithm (SRA) and MCA and compared in terms of subjective image quality, evaluability and diagnostic accuracy. RESULTS: The mean heart rate during the examination was 68.3 ± 9.4 bpm. The MCA showed a higher Likert score (3.1 ± 0.9 vs. 2.5 ± 1.1, p < 0.001) and evaluability (94%vs.79 %, p < 0.001) than the SRA. In a 45-patient subgroup studied by clinically indicated invasive coronary angiography, specificity, positive predictive value and accuracy were higher in MCA vs. SRA in segment-based and vessel-based models, respectively (87%vs.73 %, 50%vs.34 %, 85%vs.73 %, p < 0.001 and 62%vs.28 %, 66%vs.51 % and 75%vs.57 %, p < 0.001). In a patient-based model, MCA showed higher accuracy vs. SCA (93%vs.76 %, p < 0.05). CONCLUSIONS: MCA can significantly improve subjective image quality, overall evaluability and diagnostic accuracy of CCT. KEY POINTS: Cardiac computed tomographic coronary angiography (CCT) allows non-invasive evaluation of coronary arteries. Intra-cycle motion correction algorithm (MCA) allows for compensation of coronary motion. An MCA improves image quality, CCT evaluability and diagnostic accuracy.

Prognostic value of dipyridamole stress cardiac magnetic resonance in patients with known or suspected coronary artery disease: a mid-term follow-up study.

OBJECTIVES: Dipyridamole stress cardiac magnetic resonance (CMR) evaluates the key phases (perfusion and wall motion) of the ischemic cascade. We sought to determine the prognostic value of dipyridamole stress-CMR in consecutive patients symptomatic for chest pain. METHODS: Seven hundred and ninety-three consecutive patients symptomatic for chest pain underwent dipyridamole stress-CMR and were followed up for 810 ± 665 days. Patients were classified in group 1 (no- reversible ischemia), group 2 (stress perfusion defect alone), and group 3 [stress perfusion defect plus abnormal wall motion (AWM)]. End points were "all cardiac events" (myocardial infarction, cardiac death and revascularization) and "hard cardiac events" (all cardiac events excluding revascularization). RESULTS: One hundred and ninety-five (24 %) all cardiac events and 53 (7 %) hard cardiac events were observed. All and hard cardiac event rates in groups 1, 2, and 3 were 11 %, 49 %, 69 % and 4 %, 8 %, 21 %, respectively, with a higher rate in group 2 vs. group 1 (p<0.01) and group 3 vs. groups 1 and 2 (p<0.01). Multivariate analysis showed the presence of late gadolinium enhancement and stress perfusion defect plus AWM as independent predictors of all and hard cardiac events. CONCLUSIONS: Dipyridamole stress-CMR improves prognostic stratification of patients through differentiation between the different components of the ischemic cascade. KEY POINTS: • Dipyridamole stress cardiac magnetic resonance helps to assess coronary artery disease. • Novel technique to study the key phases of myocardial ischemia. • Combined assessment of perfusion and motion defects. • Dipyridamole stress imaging has additional value for predicting cardiac events.

Concomitant cryoballoon ablation and percutaneous closure of left atrial appendage in patients with atrial fibrillation.

AIMS: Pulmonary veins (PVs) isolation is the cornerstone of atrial fibrillation (AF) ablation and can be achieved either by conventional radiofrequency ablation or by cryoenergy. Left atrial appendage (LAA) closure has been proposed as alternative treatment to vitamin K antagonists (VKA). We aimed to evaluate the feasibility of combining cryoballoon (CB) ablation and LAA occlusion in patients with AF and a high thromboembolic risk or contraindication to antithrombotic therapy. METHODS AND RESULTS: Thirty-five patients (28 males, 74 ± 2 years) underwent CB ablation. Left atrial appendage occlusion was carried out by using two occluder devices (Amplatz Cardiac Plug, ACP, St. Jude Medical, MN, USA, in 25 patients; Watchman, Boston Scientific, MA, USA, in 10 patients). Thirty patients (86%) had previous stroke/TIA episodes, 6 patients (17%) had major bleeding while on VKA therapy, and 7 patients (20%) had inherited bleeding disorders. Over the follow-up (24 ± 12 months), atrial arrhythmias recurred in 10 (28%) patients. Thirty patients (86%) had complete sealing; 5 patients (14%) showed a residual flow (<5 mm) at first transoesophageal echocardiography (TEE) check, while at 1-year TEE residual flow was detected in 3 patients. In 13 patients (37%), VKA therapy was immediately discontinued. Six patients (17%) received novel oral anticoagulants treatment and then discontinued 3 months thereafter. No device-related complications or clinical thromboembolic events occurred. CONCLUSION: Combined CB ablation and LAA closure using different devices appears to be feasible in patients with non-valvular AF associated with high risk of stroke or contraindication to antithrombotic treatment.

Segmentation of the left ventricular endocardium from magnetic resonance images by using different statistical shape models.

We evaluate in this paper different strategies for the construction of a statistical shape model (SSM) of the left ventricle (LV) to be used for segmentation in cardiac magnetic resonance (CMR) images. From a large database of LV surfaces obtained throughout the cardiac cycle from 3D echocardiographic (3DE) LV images, different LV shape models were built by varying the considered phase in the cardiac cycle and the registration procedure employed for surface alignment. Principal component analysis was computed to describe the statistical variability of the SSMs, which were then deformed by applying an active shape model (ASM) approach to segment the LV endocardium in CMR images of 45 patients. Segmentation performance was evaluated by comparing LV volumes derived by ASM segmentation with different SSMs and those obtained by manual tracing, considered as a reference. A high correlation (r(2)>0.92) was found in all cases, with better results when using the SSM models comprising more than one frame of the cardiac cycle.

Diagnostic accuracy of multidetector computed tomography coronary angiography in 325 consecutive patients referred for transcatheter aortic valve replacement.

BACKGROUND: Multidetector computed tomography (MDCT) provides detailed assessment of valve annulus and iliofemoral vessels in transcatheter aortic valve replacement (TAVR) patients. However, data on diagnostic performance of MDCT coronary angiography (MDCT-CA) are scarce. The aim of the study is to assess diagnostic performance of MDCT for coronary artery evaluation before TAVR. METHODS: A total of 325 consecutive patients (234 without previous myocardial revascularization, 49 with previous coronary stenting, and 42 with previous coronary artery bypass graft [CABG]) underwent invasive coronary angiography and MDCT before TAVR. MDCT-CA was performed using the same data set dedicated to standard MDCT aortic annulus evaluation. Multidetector computed tomography-CA evaluability and diagnostic accuracy in comparison with invasive coronary angiography as criterion standard were assessed. RESULTS: The MDCT-CA evaluability of native coronaries was 95.6%. The leading cause of unevaluability was beam-hardening artifact due to coronary calcifications. In a segment-based analysis, MDCT-CA showed sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for detecting ≥50% stenosis of 91%, 99.2%, 83.4%, 99.6% and 98.8%, respectively. The MDCT-CA evaluability of coronary stents was 82.1%. In a segment-based analysis, MDCT-CA showed sensitivity, specificity, PPV, NPV, and accuracy for detecting ≥50% in-stent restenosis of 94.1%, 86.7%, 66.7%, 98.1%, and 88.3%, respectively. All CABGs were correctly assessed by MDCT-CA. In a patient-based analysis, MDCT-CA showed sensitivity, specificity, PPV, NPV, and accuracy of 89.7%, 90.8%, 80.6%, 95.4%, and 90.5%, respectively. CONCLUSIONS: Multidetector computed tomography-CA allows to correctly rule out the presence of significant native coronary artery stenosis, significant in-stent restenosis, and CABG disease in patients referred for TAVR.

Three-dimensional left ventricular segmentation from magnetic resonance imaging for patient-specific modelling purposes.

AIMS: To propose a nearly automated left ventricular (LV) three-dimensional (3D) surface segmentation procedure, based on active shape modelling (ASM) and built on a database of 3D echocardiographic (3DE) LV surfaces, for cardiac magnetic resonance (CMR) images, and to test its accuracy for LV volumes computation compared with 'gold standard' manual tracings and discs-summation method. METHODS AND RESULTS: The ASM was created based on segmented LV surfaces (4D LV analysis, Tomtec) from 3DE datasets of 205 patients. Then, it was applied to the cardiac magnetic resonance imaging short-axis (SAX) images stack of 12 consecutive patients. After proper realignment using two- and four-chambers CMR long-axis views both as reference and for initializing LV apex and base (six points in total), the ASM was iteratively and automatically updated to match the information of all the SAX planes contemporaneously, resulting in an endocardial LV 3D mesh from which volume was directly derived. The same CMR images were analysed by an experienced cardiologist to derive end-diastolic and end-systolic volumes. Linear correlation and Bland-Altman analyses were applied vs. the manual 'gold standard'. Active shape modelling results showed high correlations with manual values both for LV volumes (r(2) > 0.98) and ejection fraction (EF) (r(2) > 0.90), non-significant biases and narrow limits of agreement. CONCLUSION: The proposed method resulted in accurate detection of 3D LV endocardial surfaces, which lead to fast and reliable measurements of LV volumes and EF when compared with manual tracing of CMR SAX images. The segmented 3D mesh, including a realistic LV apex and base, could constitute a novel starting point for more realistic patient-specific finite element modelling.

Deep learning approaches for the detection of scar presence from cine cardiac magnetic resonance adding derived parametric images.

This work proposes a convolutional neural network (CNN) that utilizes different combinations of parametric images computed from cine cardiac magnetic resonance (CMR) images, to classify each slice for possible myocardial scar tissue presence. The CNN performance comparison in respect to expert interpretation of CMR with late gadolinium enhancement (LGE) images, used as ground truth (GT), was conducted on 206 patients (158 scar, 48 control) from Centro Cardiologico Monzino (Milan, Italy) at both slice- and patient-levels. Left ventricle dynamic features were extracted in non-enhanced cine images using parametric images based on both Fourier and monogenic signal analyses. The CNN, fed with cine images and Fourier-based parametric images, achieved an area under the ROC curve of 0.86 (accuracy 0.79, F1 0.81, sensitivity 0.9, specificity 0.65, and negative (NPV) and positive (PPV) predictive values 0.83 and 0.77, respectively), for individual slice classification. Remarkably, it exhibited 1.0 prediction accuracy (F1 0.98, sensitivity 1.0, specificity 0.9, NPV 1.0, and PPV 0.97) in patient classification as a control or pathologic. The proposed approach represents a first step towards scar detection in contrast-free CMR images. Patient-level results suggest its preliminary potential as a screening tool to guide decisions regarding LGE-CMR prescription, particularly in cases where indication is uncertain.