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Aorta

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Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach.

European radiology
OBJECTIVES: To develop and validate a deep learning-based algorithm for segmenting and quantifying the physiological and diseased aorta in computed tomography angiographies.

Deep learning method for aortic root detection.

Computers in biology and medicine
BACKGROUND: Computed tomography angiography (CTA) is a preferred imaging technique for a wide range of vascular diseases. However, extensive manual analysis is required to detect and identify several anatomical landmarks for clinical application. Thi...

Potential of high dimensional radiomic features to assess blood components in intraaortic vessels in non-contrast CT scans.

BMC medical imaging
BACKGROUND: To assess the potential of radiomic features to quantify components of blood in intraaortic vessels to non-invasively predict moderate-to-severe anemia in non-contrast enhanced CT scans.

Dissected aorta segmentation using convolutional neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Aortic dissection is a severe cardiovascular pathology in which an injury of the intimal layer of the aorta allows blood flowing into the aortic wall, forcing the wall layers apart. Such situation presents a high mortality r...

A Cascaded Deep Learning Framework for Detecting Aortic Dissection Using Non-contrast Enhanced Computed Tomography.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Aortic dissection (AD) is a rare but potentially fatal disease with high mortality. The aim of this study is to synthesize contrast enhanced computed tomography (CE-CT) images from non-contrast CT (NCE-CT) images for detecting aortic dissection. In t...

Deep Learning-based Automated Aortic Area and Distensibility Assessment: the Multi-Ethnic Study of Atherosclerosis (MESA).

Journal of digital imaging
This study details application of deep learning for automatic segmentation of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging for automatic aortic analysis on the large MESA cohort with assessment on an exter...

Deep Learning Based Centerline-Aggregated Aortic Hemodynamics: An Efficient Alternative to Numerical Modeling of Hemodynamics.

IEEE journal of biomedical and health informatics
Image-based patient-specific modelling of hemodynamics are gaining increased popularity as a diagnosis and outcome prediction solution for a variety of cardiovascular diseases. While their potential to improve diagnostic capabilities and thereby clin...

[Clinical Application of "Three-Low" Technique Combined with Artificial Intelligence Iterative Reconstruction Algorithm in Aortic CT Angiography].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: To explore the application value of the "three-low" technique (low radiation dose, low contrast agent dosage and low contrast agent flow rate) combined with artificial intelligence iterative reconstruction (AIIR) in aortic CT angiography (...

Deep learning-based medical image segmentation of the aorta using XR-MSF-U-Net.

Computer methods and programs in biomedicine
PURPOSE: This paper proposes a CT images and MRI segmentation technology of cardiac aorta based on XR-MSF-U-Net model. The purpose of this method is to better analyze the patient's condition, reduce the misdiagnosis and mortality rate of cardiovascul...

Artificial intelligence for automated evaluation of aortic measurements in 2D echocardiography: Feasibility, accuracy, and reproducibility.

Echocardiography (Mount Kisco, N.Y.)
AIMS: This study sought to examine the feasibility, accuracy and reproducibility of a novel, fully automated 2D transthoracic echocardiography (2D TTE) parasternal long axis (PLAX) view aortic measurements quantification software compared to board-ce...