The international journal of cardiovascular imaging最新文献

Hypertrophic cardiomyopathy (HCM) is a major health concern, with cardiac magnetic resonance (CMR) playing a crucial role in risk assessment. We investigated for the first time the utility of sequential CMR, particularly strain analysis, for tracking HCM progression. We retrospectively evaluated HCM patients undergoing two CMR scans over a 10-year period. We measured changes in left ventricular (LV) strain parameters and examined their yearly changes as predictors of a composite of sudden cardiac death, life-threatening ventricular arrhythmias, stroke, new-onset atrial fibrillation, and heart failure hospitalizations. Patients (n = 114) were predominantly male (73%), with a median age of 51 years (interquartile range 36-60), obstructive HCM in 14%, and a median HCM risk score of 2% (1-3%). Only one patient (0.9%) had LGE ≥ 15% on the first scan, while 8 (7.0%) had LGE ≥ 15% after a median of 5.1 years (3.5-6.5). Absolute changes in LV strain displayed significant relationships with changes in LGE mass (longitudinal strain: beta = 0.227, p = 0.016; circumferential strain: beta = 0.421, p < 0.001; radial long-axis: beta=-0.261, p = 0.006). During a 4.3-year median follow-up after the second CMR scan (interquartile range 2.2-6.9), 40 patients experienced an event; hard arrhythmic events were infrequent. Among patients with LGE < 15% at baseline, yearly absolute changes in radial short-axis strain predicted outcomes beyond baseline HCM score and LGE extent (hazard ratio 1.12, 95% confidence interval 1.03-1.22, p = 0.011). In patients with predominantly early-stage HCM, worsening short-axis radial strain was associated with composite adverse events after the second CMR, independent of baseline LGE and ESC SCD risk.

Different reports have suggested that epicardial adipose tissue (EAT) is related to impaired coronary function, myocardial ischemia and major adverse cardiac events. The aim of this study was to evaluate whether a relationship between EAT volume (EATvol) and thickness (EATthick) with myocardial blood flow (MBF) and myocardial flow reserve (MFR) evaluated with [¹³N]ammonia ([¹³N]NH3) positron emission tomography/computed tomography (PET/CT) is present.MBF and MFR were extracted from stress and rest [¹³N]NH3 PET/CT images. EATvol was extracted from unenhanced CT images with a semiautomatic method; EATthick from the same images. Correlation between these parameters, MBF, MFR and other clinicopathological features were investigated. Independent predictors for MBF and MFR were identified.we retrospectively enrolled 164 patients. MFR was reduced in 30 subjects. EATvol and EATthick were significantly different between subjects with normal and impaired MFR in the total cohort (p 0.001 and 0.005, respectively) and also dividing the patients on the basis of CAD history (p 0.017 and 0.038 respectively for patients without CAD, p 0.010 and 0.007 respectively for subjects with CAD). In the total cohort age, diabetes and EATvol were independent predictors of MFR (p 0.036, 0.029 and 0.008, respectively). Presence of diabetes and EATvol were confirmed as independent predictors also in the group of patients without CAD history (p 0.042 and 0.038, respectively), while in subjects with CAD history no features were confirmed. Similarly, EATvol and EATthick were not independent predictors of stress MBF.a correlation with EATvol and impaired MFR has been reported, confirming therefore the idea that EAT may influence coronary function.

This study evaluated the performance of a deep learning-based artificial intelligence (DLAI) system for detecting fusiform aortic aneurysms and measuring the diameters of the entire aorta on non-contrast CT images.We retrospectively collected 160 non-contrast CT images (130 males, 71.8 ± 9.2 years) to evaluate aortic aneurysms and used them as training data. To evaluate the accuracy of the DLAI system for the automatic detection and measurement of aortic aneurysms, another 190 non-contrast CT images (153 males, 70.7 ± 10.3 years) were collected and used as validation data. First, the accuracy of DLAI results was calculated by referring to the radiology reports. An expert radiologist read the test data again and calculated the final accuracy by referring to the DLAI predictions.Dice scores for aortic segmentation were 0.90, 0.94, 0.93, and 0.84 for the entire aorta, thoracic aorta, abdominal aorta, and iliac artery, respectively. The sensitivity, positive predictive value, and F-measure of the DLAI system for detecting aortic aneurysms were 0.81, 0.83, and 0.82, respectively. Eleven (4.4%) aneurysms were newly identified after the radiologist's second reading based on DLAI predictions. The final accuracy of the DLAI detection increased to 0.83, 0.87, and 0.85 for sensitivity, positive predictive value, and F-measure, respectively. The size error in the measurement of aortic aneurysms between the DLAI system and radiology reports was 0.86 ± 2.72 mm, with a strong correlation (ICCs = 0.97).Our DLAI system was highly accurate in detecting aneurysms and measuring the diameters of the entire aorta on non-contrast CT images.

Right ventricular (RV) to pulmonary arterial (PA) uncoupling measures RV exhaustion. There is limited evidence regarding the association between RV to PA coupling and hypertrophic cardiomyopathy (HCM). Evaluate RV to PA uncoupling in patients with HCM. Prospective cohort study in 62 patients with HCM without obstructive epicardial coronary disease. Echocardiography was used to assess RV to PA coupling as the ratio of tricuspid annular plane systolic excursion (TAPSE) to pulmonary artery systolic pressure (PASP). Coronary flow reserve in the left anterior descending artery (CVFR_LAD) was a surrogate marker for coronary microvascular dysfunction. Cardiopulmonary exercise testing (CPET) and cardiac magnetic resonance (CMR) were also performed. Mean age was 55 (15) years, 65% males. Mean TAPSE/PASP was 0.56 (0.23). The independent predictors of RV to PA coupling were age (β: - 0.184), left atrial volume index (β: - 0.641), CFVR_LAD (β: 0.183) and the extent of LGE in the LV (β: - 0.262). 43.5% showed reduced exercise tolerance. Independent predictors of peak VO2 were male gender (β: 0.349), age (β: - 0.286), global radial strain (β: 0.249) and TAPSE/PASP (β: 0.253). TAPSE/PASP showed a modest predictive accuracy for peak VO2 < 20 ml/Kg/min (AUC 0.671, p = 0.022), with the best cut-off set at 0.60 mm/mmHg (sensitivity 85% and specificity 47%). A TAPSE / PASP ≤ 0.60 mm/mmHg was present in 66% of patients. In patients with HCM, both coronary microvascular dysfunction and fibrosis are associated with RV to PA coupling, which is a determinant of exercise tolerance.

To assess the feasibility of a native 3D T1-weighted Bright-blood and black-blOOd phase SensiTive (BOOST) cardiac MRI sequence for detecting acute atrial lesions immediately after radiofrequency ablation (RFA), without gadolinium contrast administration. Ten atrial fibrillation patients underwent 3T cardiac MRI within one hour following RFA. Whole-heart black-blood T1-weighted BOOST and late gadolinium enhanced (LGE) images were acquired during free breathing. Two experienced readers independently assessed the presence and continuity of acute lesions in six predefined pulmonary vein (PV) antrum quadrants per patient. Findings were correlated with procedural RFA parameters. BOOST and LGE sequences detected high signal intensity lesions in all patients, involving both right and left PV antra. BOOST identified acute lesions in 62 and 63% of right and left PV quadrants, respectively, against 100% of quadrants with LGE. Over 80% of LGE-positive quadrants also showed microvascular obstruction. Quadrants with absent or discontinuous BOOST lesions were associated with a significantly greater inter-lesion distance and lower impedance drop during RFA compared to quadrants with continuous lesions. Whole-heart black-blood T1-weighted BOOST cardiac MRI at 3T enables detection of acute PV antrum lesions immediately after RFA without contrast administration. This contrast-free imaging technique may enable real-time, gadolinium-free lesion assessment during or immediately after RFA.

It is observed that some atrial fibrillation (AF) patients develop right heart enlargement and severe right heart failure, which affect the prognosis. An abnormal right atrioventricular coupling index (RACI) indicates that AF has led to significant right atrial and right ventricular dysfunction. However, right atrial (RA) remodeling in AF patients is rarely assessed and data on the prognosis of RA is limited. This study aims to determine if the RACI can be used to predict the risk of long-term adverse outcomes in a cohort of patients with non-valvular AF after catheter ablation (CA). A total of 123 non-valvular AF patients who underwent CA in our center were enrolled in this retrospective study. Conventional and speckle tracking echocardiography (STE) were performed for AF patients before CA. Patients were followed up with until April 2025. The adverse outcomes were the composite of atrial tachyarrhythmia recurrence (ATa), stroke or transient ischemic attack (TIA), AF-associated hospitalization, pacemaker insertion and cardiovascular death. Logistic regression analysis was used to determine the optimal cutoff value of correlates for predicting adverse events. The median follow-up time was 5.3(1.6-5.5) years. Among the 123 AF patients, 69 experienced adverse outcomes. Multivariable logistic regression analysis showed that RACI and left atrial ejection fraction (LAEF) were the independent predictors of adverse events (95% CI, 0.643-0.806; P < 0.0001 and 95% CI, 0.628-0.795; P < 0.0001; respectively). The optimal cutoff values of RACI and LAEF were 70.97% and 35%, respectively. Spearman's correlation analysis showed that RACI was negatively correlated with RA reservoir strain and RA boost strain (r=-0.583, P < 0.001; r=-0.572, P < 0.001, respectively), and LAEF was positively correlated with LA reservoir strain and LA boost strain (r = 0.859, P < 0.001; r = 0.765, P < 0.001, respectively). Increased RACI and impaired LAEF are strongly associated with long-term outcomes, with RACI showing better independent predictive value.

This study evaluated the predictive value of combining clinical characteristics, serum biomarkers, and radiomic features for poor prognosis in patients with acute pulmonary embolism (APE). Clinical data, serum biomarker data (e.g., inflammatory and coagulation biomarkers), and computed tomography pulmonary angiography (CTPA) image from patients with APE were retrospectively collected from the First Affiliated Hospital of Hebei Northern University, First Hospital of Zhangjiakou, and Second Affiliated Hospital of Hebei Northern University. Patients were divided into good and poor prognosis groups. Data from the first two hospitals were randomly split into a training cohort (166) and internal validation cohort (72) using a 7:3 ratio; data from the third hospital formed the external validation cohort (37). Using 3D Slicer software, thrombus regions were outlined to extract radiomic features were constructed using R software. Model performance was assessed via receiver operating characteristic (ROC) curve, calibration, and decision curves analysis, and the Delong test. Six statistically significant radiomic features and independent clinical risk factors, including white blood cell count, neutrophile percentage, lymphocyte percentage, respiratory rate, pulse, syncope, smoking history, and RV / LV ratio, were identified (P < 0.05). The combined model outperformed individual models in the training (AUC = 0.85) (P < 0.05), internal validation (AUC = 0.86), and external validation cohorts (AUC = 0.88) (P < 0.05), demonstrating high clinical utility. The combined model effectively predicts early poor prognosis in APE, offering a robust tool for clinical evaluation and intervention planning.