Cardiac computed tomography: from anatomy to function.

Ischaemic heart disease (IHD) is one of the world's leading causes of morbidity and mortality. Likewise, the diagnosis and risk stratification of patients with coronary artery disease (CAD) have always been based on the detection of the presence and extent of ischaemia by physical or pharmacological stress tests with or without the aid of imaging methods (e.g. exercise stress, test, stress echocardiography, single-photon emission computed tomography, or stress cardiac magnetic resonance). These methods show high performance to assess obstructive CAD, whilst they do not show accurate power to detect non-obstructive CAD. The introduction into clinical practice of coronary computed tomography angiography, the only non-invasive method capable of analyzing the coronary anatomy, allowed to add a crucial piece in the puzzle of the assessment of patients with suspected or chronic IHD. The current review evaluates the technical aspects and clinical experience of coronary computed tomography in the evaluation of atherosclerotic burden with a special focus about the new emerging application such as functional relevance of CAD with fractional flow reserve computed tomography (CT)-derived (FFRct), stress CT perfusion, and imaging inflammatory makers discussing the strength and weakness of each approach.

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.

Non-transmural myocardial infarction associated with regional contractile function is an independent predictor of positive outcome: an integrated approach to myocardial viability.

BACKGROUND: Cardiovascular magnetic resonance permits assessment of irreversible myocardial fibrosis and contractile function in patients with previous myocardial infarction. We aimed to assess the prognostic value of myocardial fibrotic tissue with preserved/restored contractile activity. METHODS: In 730 consecutive myocardial infarction patients (64 ± 11 years), we quantified left ventricular (LV) end-diastolic (EDV) and end-systolic (ESV) volumes, ejection fraction (EF), regional wall motion (WM) (1 normal, 2 hypokinetic, 3 akinetic, 4 dyskinetic), and WM score index (WMSI), and measured the transmural (1-50 and 51-100) and global extent of the infarct scar by late gadolinium enhancement (LGE). Contractile fibrotic (CT-F) segments were identified as those showing WM-1 and WM-2 with LGE ≤ or ≥ 50%. RESULTS: During follow-up (median 2.5, range 1-4.7 years), cardiac events (cardiac death or appropriate implantable defibrillator shocks) occurred in 123 patients (17%). At univariate analysis, age, LVEDV, LVESV, LVEF, WMSI, extent of LGE, segments with transmural extent > 50%, and CT-F segments were associated with cardiac events. At multivariate analysis, age > 65 years, LVEF < 30%, WMSI > 1.7, and dilated LVEDV independently predicted cardiac events, while CT-F tissue was the only independent predictor of better outcome. After adjustment for LVEF < 30% and LVEDV dilatation, the presence of CT-F tissue was associated with good prognosis. CONCLUSIONS: In addition to CMR imaging parameters associated with adverse outcome (severe LV dysfunction, poor WM, and dilated EDV), the presence of fibrotic myocardium showing contractile activity in patients with previous myocardial infarction yields a beneficial effect on patient survival.

Multimodality imaging in chronic heart failure.

The prevalence of heart failure (HF) is approximately 1-2% of the adult population in developed countries, rising to ≥ 10% among people over 70. The common symptoms of HF include shortness of breath, ankle swelling and fatigue, determined by a reduced cardiac output. Multimodality imaging is crucial to define HF etiology, determine prognosis and guiding tailored treatments. Echocardiography is the most widely used imaging modality and maintains a pivotal role in the initial diagnostic work-up and in the follow-up of HF patients. Cardiac magnetic resonance (CMR) may support the morpho-functional assessment provided by echocardiography when the acoustic window is limited or a gold standard evaluation is required. Furthermore, CMR is frequently used due to the unmatched capability to characterize myocardial structure. Coronary computed tomography angiography has become the non-invasive imaging of choice to diagnose or rule-out coronary artery disease, acquiring remarkable importance in the management of HF patients. Moreover, emerging capabilities of CT-based tissue characterization may be useful, especially when CMR is contraindicated. Finally, chest CT may contribute to precisely define the framework of HF patients, revealing new insight about cardiopulmonary pathophysiological interactions with potential high prognostic value.

Asymptomatic Left Ventricular Dysfunction: Is There a Role for Screening in General Population?

Stage A and B heart failure (HF) include asymptomatic patients without and with structural cardiac disorder, respectively. Asymptomatic left ventricular (LV) dysfunction represents an early stage of HF that should be recognized to prevent overt HF development. Echocardiography plays a pivotal role in assessment of cardiac structure and function and represents the ideal imaging technique for screening in the general population, thanks to its availability, feasibility, and low cost. Traditional echocardiography, with LV systolic and diastolic function and cardiac remodeling assessment, is usually performed. Development of new technologies may offer additional information and insights in detection of early LV dysfunction.

Extra-abdominal dual-energy CT applications: a comprehensive overview.

Unlike conventional computed tomography, dual-energy computed tomography is a relatively novel technique that exploits ionizing radiations at different energy levels. The separate radiation sets can be achieved through different technologies, such as dual source, dual layers or rapid switching voltage. Body tissue molecules vary for their specific atomic numbers and electron density, and the interaction with different sets of radiations results in different attenuations, allowing to their final distinction. In particular, iodine recognition and quantification have led to important information about intravenous contrast medium delivery within the body. Over the years, useful post-processing algorithms have also been validated for improving tissue characterization. For instance, contrast resolution improvement and metal artifact reduction can be obtained through virtual monoenergetic images, dose reduction by virtual non-contrast reconstructions and iodine distribution highlighting through iodine overlay maps. Beyond the evaluation of the abdominal organs, dual-energy computed tomography has also been successfully employed in other anatomical districts. Although lung perfusion is one of the most investigated, this evaluation has been extended to narrowly fields of application, such as musculoskeletal, head and neck, vascular and cardiac. The potential pool of information provided by dual-energy technology is already wide and not completely explored, yet. Therefore, its performance continues to raise increasing interest from both radiologists and clinicians.

Dual-source dual-energy CT in the evaluation of hepatic fractional extracellular space in cirrhosis.

BACKGROUND: One of the main features of liver fibrosis is the expansion of the interstitial space. All water-soluble CT contrast agents remain confined in the vascular and interstitial space constituting the fractional extracellular space (fECS). Indirect measure of its expansion can be quantified during equilibrium phase with CT. The goal of this prospective study was to assess the feasibility of dual-energy CT (DECT) with iodine quantification at equilibrium phase in the evaluation of significant fibrosis or cirrhosis. METHODS: Thirty-eight cirrhotic patients (according to Child-Pugh and MELD scores), scheduled for liver CT, were enrolled in the study group. Twenty-four patients undergoing CT urography with a 10-min excretory phase were included in the control group. fECS was calculated as the ratio of the iodine concentration of liver parenchyma to that of the aorta, multiplied by 1 minus hematocrit. RESULTS: Final study and control group were, respectively, composed of 22 and 20 patients. Mean hepatic fECS value was statistically greater in study group (P < 0.05). Positive correlation was observed between hepatic fECS value and MELD score (r = 0.64, P < 0.05). Analysis of variance showed statistical differences between control group and the Child-Pugh grades and between Child-Pugh A and B patients and Child-Pugh C patients (P < 0.05). ROC curves analysis yielded an optimum fECS cutoff value of 26.3% for differentiation of control group and cirrhotic patients (AUC 0.88; 86% sensitivity, 85% specificity). CONCLUSIONS: Dual-source DECT is a feasible, noninvasive method for the assessment of significant liver fibrosis or cirrhosis.

PROGnostic RolE of strain measurements in stress cardiac MRI in predicting major adverse cardiac events.

OBJECTIVES: We aimed to investigate the role of feature-tracking (FT) strain in long-term risk stratification of patients with known or suspected coronary artery disease (CAD) who underwent stress cardiac MRI with dipyridamole; to determine if contrast-free stress cardiac MRI with strain measurements could provide comparable prognostic value to myocardial perfusion. MATERIALS AND METHODS: This retrospective study included consecutive patients with stable symptoms suggesting possible cardiac ischemia who underwent stress cardiac MRI with dipyridamole. The mean follow-up period was 5.8 years ±1.2 [SD]. FT cardiac MRI analysis was performed for each patient to obtain 2D global peak circumferential strain (GCS). The primary outcome measure was major adverse cardiac events (MACE), defined as nonfatal myocardial infarction and cardiac death. RESULTS: A total of 729 patients (mean age, 63 years ±10 [SD]; 616 males) were included. MACE occurred in 70 (9.6%) patients. The presence of late gadolinium enhancement (LGE) ([HR] 2.74, [95% CI: 1.53, 4.88]; P < .001) and stress GCS (HR, 1.06 [95% CI: 1.01, 1.12]; P = .016) were independently associated with MACE. A model based on contrast-free assessment of LVEF and stress GCS showed similar performance for predicting MACE than LVEF and perfusion (P = .056). CONCLUSIONS: In patients with known or suspected CAD undergoing stress cardiac MRI with dipyridamole, GCS and LGE presence were independent predictors of MACE. Contrast-free stress cardiac MRI with stress GCS measurement offered prognostic value akin to myocardial perfusion assessment. CLINICAL RELEVANCE STATEMENT: Stress global circumferential strain represented an additional method to predict major adverse cardiac events in patients undergoing stress cardiac MRI, even without the use of contrast agents. This would be of particular significance in patients with severe renal impairment.

STress computed tomogRaphy perfusion and stress cArdiac magnetic resonance for ThE manaGement of suspected or known coronarY artery disease: resources and outcomes impact.

BACKGROUND: The aim of this study is to describe resources and outcomes of coronary computed tomography angiography plus Stress CT perfusion (CCTA â€‹+ â€‹Stress-CTP) and stress cardiovascular magnetic resonance (Stress-CMR) in symptomatic patients with suspected or known CAD. METHODS: Six hundred and twenty-four consecutive symptomatic patients with intermediate to high-risk pretest likelihood for CAD or previous history of revascularization referred to our hospital for clinically indicated CCTA â€‹+ â€‹Stress-CTP or Stress-CMR were enrolled. Stress-CTP scans were performed in 223 patients while 401 patients performed Stress-CMR. Patient follow-up was performed at 1 year after index test performance. Endpoints were all cardiac events, as a combined endpoint of revascularization, non-fatal MI and death, and hard cardiac events, as combined endpoint of non-fatal MI and death. RESULTS: Twenty-nine percent of patients who underwent CCTA â€‹+ â€‹Stress-CTP received revascularization, 7% of subjects assessed with Stress-CMR were treated invasively, and a low number of non-fatal MI and death was observed with both strategies (hard events in 0.4% of patients that had CCTA â€‹+ â€‹Stress-CTP as index test, and in 3% of patients evaluated with Stress-CMR). According to the predefined endpoints, CCTA â€‹+ â€‹Stress-CTP group showed high rate of all cardiac events and low rate of hard cardiac events, respectively. The cumulative costs were 1970 â€‹± â€‹2506 Euro and 733 â€‹± â€‹1418 Euro for the CCTA â€‹+ â€‹Stress-CTP group and Stress-CMR group, respectively. CONCLUSIONS: The use of CCTA â€‹+ â€‹Stress-CTP strategy was associated with high referral to revascularization but with a favourable trend in terms of hard cardiac events and diagnostic yield in identifying individuals at lower risk of adverse events despite the presence of CAD.

DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR.

BACKGROUND AND AIMS: This study investigated the additional prognostic value of epicardial adipose tissue (EAT) volume for major adverse cardiovascular events (MACE) in patients undergoing stress cardiac magnetic resonance (CMR) imaging. METHODS: 730 consecutive patients [mean age: 63 ± 10 years; 616 men] who underwent stress CMR for known or suspected coronary artery disease were randomly divided into derivation (n = 365) and validation (n = 365) cohorts. MACE was defined as non-fatal myocardial infarction and cardiac deaths. A deep learning algorithm was developed and trained to quantify EAT volume from CMR. EAT volume was adjusted for height (EAT volume index). A composite CMR-based risk score by Cox analysis of the risk of MACE was created. RESULTS: In the derivation cohort, 32 patients (8.7 %) developed MACE during a follow-up of 2103 days. Left ventricular ejection fraction (LVEF) < 35 % (HR 4.407 [95 % CI 1.903-10.202]; p<0.001), stress perfusion defect (HR 3.550 [95 % CI 1.765-7.138]; p<0.001), late gadolinium enhancement (LGE) (HR 4.428 [95%CI 1.822-10.759]; p = 0.001) and EAT volume index (HR 1.082 [95 % CI 1.045-1.120]; p<0.001) were independent predictors of MACE. In a multivariate Cox regression analysis, adding EAT volume index to a composite risk score including LVEF, stress perfusion defect and LGE provided additional value in MACE prediction, with a net reclassification improvement of 0.683 (95%CI, 0.336-1.03; p<0.001). The combined evaluation of risk score and EAT volume index showed a higher Harrel C statistic as compared to risk score (0.85 vs. 0.76; p<0.001) and EAT volume index alone (0.85 vs.0.74; p<0.001). These findings were confirmed in the validation cohort. CONCLUSIONS: In patients with clinically indicated stress CMR, fully automated EAT volume measured by deep learning can provide additional prognostic information on top of standard clinical and imaging parameters.

A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images.

BACKGROUND AND OBJECTIVE: In patients with suspected Coronary Artery Disease (CAD), the severity of stenosis needs to be assessed for precise clinical management. An automatic deep learning-based algorithm to classify coronary stenosis lesions according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) in multiplanar reconstruction images acquired with Coronary Computed Tomography Angiography (CCTA) is proposed. METHODS: In this retrospective study, 288 patients with suspected CAD who underwent CCTA scans were included. To model long-range semantic information, which is needed to identify and classify stenosis with challenging appearance, we adopted a token-mixer architecture (ConvMixer), which can learn structural relationship over the whole coronary artery. ConvMixer consists of a patch embedding layer followed by repeated convolutional blocks to enable the algorithm to learn long-range dependences between pixels. To visually assess ConvMixer performance, Gradient-Weighted Class Activation Mapping (Grad-CAM) analysis was used. RESULTS: Experimental results using 5-fold cross-validation showed that our ConvMixer can classify significant coronary artery stenosis (i.e., stenosis with luminal narrowing ≥50%) with accuracy and sensitivity of 87% and 90%, respectively. For CAD-RADS 0 vs. 1-2 vs. 3-4 vs. 5 classification, ConvMixer achieved accuracy and sensitivity of 72% and 75%, respectively. Additional experiments showed that ConvMixer achieved a better trade-off between performance and complexity compared to pyramid-shaped convolutional neural networks. CONCLUSIONS: Our algorithm might provide clinicians with decision support, potentially reducing the interobserver variability for coronary artery stenosis evaluation.

Spontaneous transverse colon volvulus in a patient with Duchenne muscular dystrophy: An unreported complication.

A 22-year-old male patient with Duchenne muscular dystrophy (DMD) and chronic constipation presents to the emergency room with severe abdominal pain and hive closed to feces and gas. Contrast-enhanced computed tomography of the abdomen demonstrates mechanical ileus due to volvulus of the transverse colon: torsion of the transverse mesocolon is confirmed and subtotaly colectomy is performed, revealing multiple ischemic areas with focal perforations. DMD is frequently associated with gastrointestinal motility disorders, including chronic constipation and life-threatening conditions like intestinal pseudo-obstruction and sigmoid volvulus. To date, transverse colic localization of volvolus represents an unreported condition among patients with DMD.

A deep-learning approach for myocardial fibrosis detection in early contrast-enhanced cardiac CT images.

Aims: Diagnosis of myocardial fibrosis is commonly performed with late gadolinium contrast-enhanced (CE) cardiac magnetic resonance (CMR), which might be contraindicated or unavailable. Coronary computed tomography (CCT) is emerging as an alternative to CMR. We sought to evaluate whether a deep learning (DL) model could allow identification of myocardial fibrosis from routine early CE-CCT images. Methods and results: Fifty consecutive patients with known left ventricular (LV) dysfunction (LVD) underwent both CE-CMR and (early and late) CE-CCT. According to the CE-CMR patterns, patients were classified as ischemic (n = 15, 30%) or non-ischemic (n = 35, 70%) LVD. Delayed enhancement regions were manually traced on late CE-CCT using CE-CMR as reference. On early CE-CCT images, the myocardial sectors were extracted according to AHA 16-segment model and labeled as with scar or not, based on the late CE-CCT manual tracing. A DL model was developed to classify each segment. A total of 44,187 LV segments were analyzed, resulting in accuracy of 71% and area under the ROC curve of 76% (95% CI: 72%-81%), while, with the bull's eye segmental comparison of CE-CMR and respective early CE-CCT findings, an 89% agreement was achieved. Conclusions: DL on early CE-CCT acquisition may allow detection of LV sectors affected with myocardial fibrosis, thus without additional contrast-agent administration or radiational dose. Such tool might reduce the user interaction and visual inspection with benefit in both efforts and time.

Erratum: Spontaneous transverse colon volvulus in a patient with Duchenne muscular dystrophy: An unreported complication.

[This corrects the article DOI: 10.1016/j.radcr.2022.12.062.][This corrects the article DOI: 10.1016/j.radcr.2023.03.026.].

Constrictive Pericarditis: An Update on Noninvasive Multimodal Diagnosis.

Constrictive pericarditis (CP) is a rare condition that can affect the pericardium after every pericardial disease process and has been described even after SARS-CoV-2 infection or vaccine. In CP, the affected pericardium, usually the inner layer, is noncompliant, constraining the heart to a fixed maximum volume and impairing the diastolic function. This leads to several clinical features, that, however, can be pleomorphic. In its difficult diagnostic workup, noninvasive multimodal imaging plays a central role, providing important morphological and functional data, like the enhanced ventricular interdependence and the dissociation between intrathoracic and intracardiac pressures. An early and proper diagnosis is crucial to set an appropriate therapy, changing the prognosis of patients affected by CP. In this review, we cover in detail the main elements of each imaging technique, after a reminder of pathophysiology useful for understanding the diagnostic findings.

Preoperative angiography-derived fractional flow reserve may predict coronary artery bypass grafting occlusion and disease progression.

BACKGROUND: Graft occlusion after coronary artery bypass grafting (CABG) has been associated with competitive flow of native coronary arteries. OBJECTIVES: To assess with coronary computed tomography angiography (CCTA) graft occlusion and coronary artery disease (CAD) progression of native vessels after CABG and their relationship with angiography-derived vessel fractional flow reserve (vFFR) performed before surgery. METHODS: Between 2006 and 2018, serial vFFR analyses were obtained before CABG in each major native coronary vessel from two institutions. All patients underwent follow-up CCTA. RESULTS: In 171 consecutive patients, serial preoperative angiograms were suitable for vFFR analysis of 298 grafted and 59 nongrafted vessels. Median time between CABG and CCTA was 2.1 years. Preoperative vFFR was assessed in 131 left anterior descending artery (LAD), 132 left circumflex artery (LCX) and 94 right coronary aretry (RCA) and was less than 0.80 in 255 of 298 bypassed vessels. Graft occlusion was observed at CCTA in 28 of 298 grafts. The median preoperative vFFR value of native coronaries was higher in occluded compared with patent grafts (0.75 vs. 0.60, P < 0.001) and was associated with graft. The best vFFR cut-off to predict graft occlusion was 0.67. Progression of CAD was higher in grafted than in nongrafted vessels (89.6 vs. 47.5%, P < 0.001). Pre-CABG vFFR predicted disease progression of grafted native vessels (AUC = 0.83). CONCLUSION: Preoperative vFFR derived from invasive coronary angiography was able to predict graft occlusion and CAD progression of grafted coronary arteries.

Circulating Small Extracellular Vesicles Reflect the Severity of Myocardial Damage in STEMI Patients.

Circulating small extracellular vesicles (sEVs) contribute to inflammation, coagulation and vascular injury, and have great potential as diagnostic markers of disease. The ability of sEVs to reflect myocardial damage assessed by Cardiac Magnetic Resonance (CMR) in ST-segment elevation myocardial infarction (STEMI) is unknown. To fill this gap, plasma sEVs were isolated from 42 STEMI patients treated by primary percutaneous coronary intervention (pPCI) and evaluated by CMR between days 3 and 6. Nanoparticle tracking analysis showed that sEVs were greater in patients with anterior STEMI (p = 0.0001), with the culprit lesion located in LAD (p = 0.045), and in those who underwent late revascularization (p = 0.038). A smaller sEV size was observed in patients with a low myocardial salvage index (MSI, p = 0.014). Patients with microvascular obstruction (MVO) had smaller sEVs (p < 0.002) and lower expression of the platelet marker CD41-CD61 (p = 0.039). sEV size and CD41-CD61 expression were independent predictors of MVO/MSI (OR [95% CI]: 0.93 [0.87-0.98] and 0.04 [0-0.61], respectively). In conclusion, we provide evidence that the CD41-CD61 expression in sEVs reflects the CMR-assessed ischemic damage after STEMI. This finding paves the way for the development of a new strategy for the timely identification of high-risk patients and their treatment optimization.

Quantification of extracellular volume with cardiac computed tomography in patients with dilated cardiomyopathy.

BACKGROUND: Cardiac computed tomography (CCT) was recently validated to measure extracellular volume (ECV) in the setting of cardiac amyloidosis, showing good agreement with cardiovascular magnetic resonance (CMR). However, no evidence is available with a whole-heart single source, single energy CT scanner in the clinical context of newly diagnosed left ventricular dysfunction. Therefore, the aim of this study was to test the diagnostic accuracy of ECVCCT in patients with a recent diagnosis of dilated cardiomyopathy, having ECVCMR as the reference technique. METHODS: 39 consecutive patients with newly diagnosed dilated cardiomyopathy (LVEF <50%) scheduled for clinically indicated CMR were prospectively enrolled. Myocardial segment evaluability assessment with each technique, agreement between ECVCMR and ECVCCT, regression analysis, Bland-Altman analysis and interclass correlation coefficient (ICC) were performed. RESULTS: Mean age of enrolled patients was 62 â€‹± â€‹11 years, and mean LVEF at CMR was 35.4 â€‹± â€‹10.7%. Overall radiation exposure for ECV estimation was 2.1 â€‹± â€‹1.1 â€‹mSv. Out of 624 myocardial segments available for analysis, 624 (100%) segments were assessable by CCT while 608 (97.4%) were evaluable at CMR. ECVCCT demonstrated slightly lower values compared to ECVCMR (all segments, 31.8 â€‹± â€‹6.5% vs 33.9 â€‹± â€‹8.0%, p â€‹< â€‹0.001). At regression analysis, strong correlations were described (all segments, r â€‹= â€‹0.819, 95% CI: 0.791 to 0.844). On Bland-Altman analysis, bias between ECVCMR and ECVCCT for global analysis was 2.1 (95% CI: -6.8 to 11.1). ICC analysis showed both high intra-observer and inter-observer agreement for ECVCCT calculation (0.986, 95%CI: 0.983 to 0.988 and 0.966, 95%CI: 0.960 to 0.971, respectively). CONCLUSIONS: ECV estimation with a whole-heart single source, single energy CT scanner is feasible and accurate. Integration of ECV measurement in a comprehensive CCT evaluation of patients with newly diagnosed dilated cardiomyopathy can be performed with a small increase in overall radiation exposure.

Pulmonary barotrauma in patient suffering from COVID-19.

Pneumothorax and pneumomediastinum are life-threatening conditions especially in critically ill patients. One of the most common situations in which they occur is prolonged invasive and non-invasive mechanical ventilation with high end-expiratory pressure. Probably due to the high number of patients with SARS-CoV-2 respiratory infection being treated with mechanical ventilation, increasing number of pulmonary barotrauma cases have been reported.