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.
Since 2002, transcatheter aortic valve implantation (TAVI) has revolutionized the treatment and prognosis of patients with aortic stenosis. A preprocedural assessment of the patient is vital for achieving optimal outcomes from the procedure. Retrospective ECG-gated cardiac computed tomography (CT) today it is the gold-standard imaging technique that provides three-dimensional images of the heart, thus allowing a rapid and complete evaluation of the morphology of the valve, ascending aorta, coronary arteries, peripheral access vessels, and prognostic factors, and also provides preprocedural coplanar fluoroscopic angle prediction to obtain complete assessment of the patient. The most relevant dimension in preprocedural planning of TAVI is the aortic annulus, which can determine the choice of prosthesis size. CT is also essential to identify patients with increased anatomical risk for coronary artery occlusion in Valve in Valve (ViV) procedures. Moreover, CT is very useful in the evaluation of late complications, such as leakage, thrombosis and displacements. At present, CT is the cornerstone imaging modality for the extensive and thorough work-up required for planning and performing each TAVI procedure, to achieve optimal outcomes. Both the CT procedure and analysis should be performed by trained and experienced personnel, with a radiological background and a deep understanding of the TAVI procedure, in close collaboration with the implantation team. An accurate pre-TAVI CT and post-processing for the evaluation of all the points recommended in this review allow a complete planning for the choice of the valve dimensions and type (balloon or self-expandable) and of the best percutaneous access.
Purpose: Arti cial intelligence could play a key role in cardiac imaging analysis. To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically signi cant 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 ow reserve (FFR) for stenoses ranging between 30% and 80%. Subsequently, a DL algorithm for the prediction of signi cant CAD by using the rest dataset (CTP-DL rest ) and stress dataset (CTP-DL stress ) was developed. The diagnostic accuracy for identi cation of signi cant CAD using CCTA, CCTA+CTP Stress , CCTA+CTP-DL rest , and CCTA+CTP-DL stress were measured and compared. The time of analysis for CTP Stress , CTP-DL rest and CTP-DL Stress were recorded.Results: Patient-speci c sensitivity, speci city, NPV, PPV, accuracy and area under the curve (AUC) of CCTA alone and CCTA+CTP Stress were 100%,
Aim Pre-transcatheter aortic valve implantation (TAVI) computed tomography (CT) has proven to be crucial in identifying pre- and post-procedural predicting factors predisposing the onset of major arrhythmias that require permanent pacemaker (PPM) implantation caused by the compressive effects of the prostheses on the conduction system at the membranous septum (MS) and the muscular crest of the interventricular septum. Our analysis aims to verify if the pre-TAVI assessment of the angle between the MS and the aortic annulus (SVA) might be a predictive factor for the onset of arrhythmias that requires PPM. Methods Two cardiovascular specialist radiologists retrospectively and double-blind evaluated a randomized list of preprocedural CT of 57 patients who underwent TAVI with a self-expandable valve from April 2019 to February 2020. Two anatomical features were measured by readers: width of the SVA and MS length (MSL). Results A PPM was implanted in 18 patients (31%) after the procedure. There was no significant difference in the anatomical measurements performed between the two observers, regarding both anatomical measurements (intraclass correlation coefficient was 0.944 for the SVA and 0.774 for the MSL]. Receiver-operating characteristic curves (ROC) performed for both measurements have documented: for the SVA sensitivity 94% and Negative predictive value (NPV) 96% (area under the curve: 0.77; 95% confidence interval 0.66–0.90). The MSL ROC was not significant. The mean SVA value stratified for patients who did not undergo PPM implantation and patients who did resulted as significant (P < 0.005). Conclusion Measurement of the SVA performed in preprocedural CT scans has proven to be related to the onset of major arrhythmias after TAVI requiring permanent pacemaker implantation with high sensitivity (94%) and NPV (96%).
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