Imaging of the aortic root on high-pitch non-gated and ECG-gated CT: awareness is the key!

AbstractThe aortic pathologies are well recognized on imaging. However, conventionally cardiac and proximal aortic abnormalities were only seen on dedicated cardiac or aortic studies due to need for ECG gating. Advances in CT technology have allowed motionless imaging of the chest and abdomen, leading to an increased visualization of cardiac and aortic root diseases on non-ECG-gated imaging. The advances are mostly driven by high pitch due to faster gantry rotation and table speed. The high-pitch scans are being increasingly used for variety of clinical indications because the images are free of motion artifact (both breathing and pulsation) as well as decreased radiation dose. Recognition of aortic root pathologies may be challenging due to lack of familiarity of radiologists with disease spectrum and their imaging appearance. It is important to recognize some of these conditions as early diagnosis and intervention is key to improving prognosis. We present a comprehensive review of proximal aortic anatomy, pathologies commonly seen at the aortic root, and their imaging appearances to familiarize radiologists with the diseases of this location.</jats:p

Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification

Rationale and Objectives: Cardiac magnetic resonance imaging is crucial for diagnosing cardiovascular diseases, but lengthy postprocessing and manual segmentation can lead to observer bias. Deep learning (DL) has been proposed for automated cardiac segmentation; however, its effectiveness is limited by the slice range selection from base to apex. Materials and Methods: In this study, we integrated an automated slice range classification step to identify basal to apical short-axis slices before DL-based segmentation. We employed publicly available Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI data set with short-axis cine data from 160 training, 40 validation, and 160 testing cases. Three classification and seven segmentation DL models were studied. The top-performing segmentation model was assessed with and without the classification model. Model validation to compare automated and manual segmentation was performed using Dice score and Hausdorff distance and clinical indices (correlation score and Bland-Altman plots). Results: The combined classification (CBAM-integrated 2D-CNN) and segmentation model (2D-UNet with dilated convolution block) demonstrated superior performance, achieving Dice scores of 0.952 for left ventricle (LV), 0.933 for right ventricle (RV), and 0.875 for myocardium, compared to the stand-alone segmentation model (0.949 for LV, 0.925 for RV, and 0.867 for myocardium). Combined classification and segmentation model showed high correlation (0.92-0.99) with manual segmentation for biventricular volumes, ejection fraction, and myocardial mass. The mean absolute difference (2.8-8.3 mL) for clinical parameters between automated and manual segmentation was within the interobserver variability range, indicating comparable performance to manual annotation. Conclusion: Integrating an initial automated slice range classification step into the segmentation process improves the performance of DL-based cardiac chamber segmentation