Search results for "Aortic diameter"
showing 3 items of 3 documents
Risk of aortic dissection in patients with ascending aorta aneurysm: a new biological, morphological, and biomechanical network behind the aortic dia…
2020
Thoracic aortic aneurysm represents a deadly condition, particularly when it evolves into rupture and dissection. Proper surgical timing is the key to positively influencing the survival of patients with this pathology. According to the most recent guidelines, ascending aorta size ≥ 55 mm and a rate of growth ≥ 0.5 cm per year are the most important factors for surgical indication. Nevertheless, a lot of evidence show that aortic ruptures and dissections might occur also in small size ascending aorta. In this review, we sought to analyze a new biological and morphological network behind the aortic diameter that need to be considered in order to identify the portion of patients with thoracic…
Impact of Aortic Diameter Measurements at Three Anatomical Landmarks on Left Ventricular Output Calculation in Neonates
2021
OBJECTIVES To assess reproducibility and accuracy of left ventricular output (LVO) quantifications in neonates, when left ventricular outflow tract diameter (LVOTD) was measured at the hinges of the aortic valve (AV), at the aortic sinus (AS), and at the sinotubular junction (STJ). METHODS This was an observational study. In the first cohort of very preterm neonates, we assessed intraobserver and interobserver repeatability of LVOTD measured at the AV, AS, and STJ and of the corresponding LVO. In the second cohort of older neonates, we compared paired LVO measurements by echo and magnetic resonance imaging (MRI). RESULTS In the first cohort of 48 neonates, mean (standard deviation) weight a…
Segmentation-Free Estimation of Aortic Diameters from MRI Using Deep Learning
2021
Accurate and reproducible measurements of the aortic diameters are crucial for the diagnosis of cardiovascular diseases and for therapeutic decision making. Currently, these measurements are manually performed by healthcare professionals, being time consuming, highly variable, and suffering from lack of reproducibility. In this work we propose a supervised deep-learning method for the direct estimation of aortic diameters. The approach is devised and tested over 100 magnetic resonance angiography scans without contrast agent. All data was expert-annotated at six aortic locations typically used in clinical practice. Our approach makes use of a 3D+2D convolutional neural network (CNN) that ta…