Journal of the American Heart Association
Feb 22, 2020
Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed to...
OBJECTIVES: To take advantage of the deep learning algorithms to detect and calculate clot burden of acute pulmonary embolism (APE) on computed tomographic pulmonary angiography (CTPA).
In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac imaging, starting with radiomics, basic algorithms of deep learning and application tasks of algorithms, until recently the availability of the public databa...
OBJECTIVE: To investigate the effect of image quality of coronary CT angiography (CCTA) on the diagnostic performance of a machine learning-based CT-derived fractional flow reserve (FFR).
Intracranial blood vessel segmentation plays an essential role in the diagnosis and surgical planning of cerebrovascular diseases. Recently, deep convolutional neural networks have shown increasingly outstanding performance in image classification an...
The objective of this work was to perform image-based classification of abdominal aortic aneurysms (AAA) based on their demographic, geometric, and biomechanical attributes. We retrospectively reviewed existing demographics and abdominal computed tom...
Journal of cardiovascular computed tomography
Jan 13, 2020
BACKGROUND: Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high levels. Deep-learning image reconstruc...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Dec 28, 2019
Extensive research has been devoted to the segmentation of the coronary artery. However, owing to its complex anatomical structure, it is extremely challenging to automatically segment the coronary artery from 3D coronary computed tomography angiogra...
BACKGROUND AND AIMS: Artificial intelligence (AI) is increasing its role in diagnosis of patients with suspicious coronary artery disease. The aim of this manuscript is to develop a deep convolutional neural network (CNN) to classify coronary compute...
RATIONALE AND OBJECTIVES: To evaluate deep learning (DL)-based optimization algorithm for low-dose coronary CT angiography (CCTA) image noise reduction and image quality (IQ) improvement.