Accuracy of Non-Electrocardiographically Gated Thoracic CT Angiography for Right Atrial and Right Ventricular Enlargement

Noncontrast Myocardial Characterization in Acute Myocardial Infarction Using Electron Density Imaging

PURPOSE

Spectral computed tomography (CT) enables improved tissue characterization, although virtually all research has focused on contrast-enhanced examinations. We hypothesized that changes in myocardial tissue related to acute myocardial infarction (AMI) might potentially be identified without the need for contrast administration using electron density (ED) imaging.

PATIENTS AND METHODS

This retrospective observational study involved a small series (n = 15) of patients admitted to our institution with a first AMI without signs of hemodynamic instability and identification of a culprit vessel with invasive coronary angiography during the same admission, who also underwent a noncontrast, low-dose chest CT using a dual-layer spectral CT scanner. Images were assessed in search of dark areas with low density on ED imaging, and the mean percentage ED relative to water (%EDW) was calculated.

RESULTS

Using a qualitative approach, ED assessment enabled the identification of 11/15 (73%) affected coronary territories, with a sensitivity of 73% (95% CI: 45; 92%) and a specificity of 87% (95% CI: 69; 96%). AMI segments showed significantly lower ED values than the remote myocardium (103.8 ± 0.8 vs 104.3 ± 0.6 %EDW, P < 0.0001), and a threshold below 103.9 %EDW had a sensitivity of 66% and specificity of 79% for the identification of AMI. In a control group of patients without a history of cardiovascular disease, none had areas with focal reduction of ED following the shape of the myocardial wall.

CONCLUSIONS

In our preliminary series, ED imaging showed the potential to enable the identification of myocardial tissue changes related to AMI without iodinated contrast requirement.

Machine learning assisted feature identification and prediction of hemodynamic endpoints using computed tomography in patients with CTEPH

Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare but potentially curable cause of pulmonary hypertension (PH). Currently PH is diagnosed by right heart catheterisation. Computed tomography (CT) is used for ruling out other causes and operative planning. This study aims to evaluate importance of different quantitative/qualitative imaging features and develop a supervised machine learning (ML) model to predict hemodynamic risk groups. 127 Patients with diagnosed CTEPH who received preoperative right heart catheterization and thoracic CTA examinations (39 ECG-gated; 88 non-ECG gated) were included. 19 qualitative/quantitative imaging features and 3 hemodynamic parameters [mean pulmonary artery pressure, right atrial pressure (RAP), pulmonary artery oxygen saturation (PA SaO2)] were gathered. Diameter-based CT features were measured in axial and adjusted multiplane reconstructions (MPR). Univariate analysis was performed for qualitative and quantitative features. A random forest algorithm was trained on imaging features to predict hemodynamic risk groups. Feature importance was calculated for all models. Qualitative and quantitative parameters showed no significant differences between ECG and non-ECG gated CTs. Depending on reconstruction plane, five quantitative features were significantly different, but mean absolute difference between parameters (MPR vs. axial) was 0.3 mm with no difference in correlation with hemodynamic parameters. Univariate analysis showed moderate to strong correlation for multiple imaging features with hemodynamic parameters. The model achieved an AUC score of 0.82 for the mPAP based risk stratification and 0.74 for the PA SaO2 risk stratification. Contrast agent retention in hepatic vein, mosaic attenuation pattern and the ratio right atrium/left ventricle were the most important features among other parameters. Quantitative and qualitative imaging features of reconstructions correlate with hemodynamic parameters in preoperative CTEPH patients-regardless of MPR adaption. Machine learning based analysis of preoperative imaging features can be used for non-invasive risk stratification. Qualitative features seem to be more important than previously anticipated.

Utility of Automated Cardiac Chamber Volumetry by Non-Gated CT Pulmonary Angiography for Detection of Pulmonary Hypertension Using the 2018 Updated Hemodynamic Definition

BACKGROUND: Noninvasive tests for pulmonary hypertension (PH) are needed to help select patients for diagnostic right heart catheterization (RHC). CT pulmonary angiography (CTPA) is commonly performed for suspected PH. OBJECTIVE: To assess the utility of CTPA-based cardiac chamber volumetric measurements for diagnosis of PH in comparison with echocardiographic and conventional CTPA parameters, using as reference the 2018 updated hemodynamic definition. METHODS: This retrospective study included 109 patients (median age, 68 years; 72 women, 37 men) who underwent non-gated CTPA, echocardiography, and RHC for workup of suspected PH between August 2013 and February 2016. Two radiologists independently used automated 3D segmentation software to determine volumes of the right ventricle (RV), right atrium (RA), left ventricle (LV), and left atrium (LA), and measured axial diameters of cardiac chambers, main pulmonary artery, and ascending aorta. Interobserver agreement was assessed, and mean values were obtained; one observer repeated volumetric measurements to assess intraobserver agreement. ROC analysis was used to assess diagnostic performance for detection of PH. A multivariable binary logistic regression model was established. RESULTS: A total of 60/109 patients had PH. Intra- and interobserver agreement were excellent for all volume measurements (intraclass correlation coefficients, 0.935-0.999). In patients with, versus without, PH, RV volume was 172.6 versus 118.1 ml, and RA volume was 130.2 versus 77.0 ml (both p<.05). Cardiac chamber measurements with highest AUC for PH were RV/LV volume ratio and RA volume (both 0.791). Significant predictors of PH after adjustment for age, sex, and body surface area included RV volume per 10 ml [odds ratio (OR)=1.21], RA volume per 10 ml (OR=1.27), RV/LV volume ratio (OR=2.91), and RA/LA volume ratio (OR=11.22). Regression analysis yielded a predictive model for PH containing two independent predictors, echocardiographic pulmonary arterial systolic pressure and CTPA-based RA volume; the model had AUC 0.898, sensitivity 83.3%, and specificity 85.7%. CONCLUSION: Automated cardiac chamber volumetry using non-gated CTPA, particularly of the RA, provides incremental utility relative to echocardiographic and conventional CTPA parameters for diagnosis of PH. CLINICAL IMPACT: Automated cardiac chamber volumetry on CTPA may facilitate early nonvinvasive detection of PH, identifying patients warranting further evaluation by RHC.