PURPOSE: Our objective was to evaluate whether the normal lung index (NLI) from quantitative computed tomography (QCT) analysis can be used to predict mortality as well as pulmonary function tests (PFTs) in patients with chronic obstructive pulmonary...
OBJECTIVE: This study determined the effect of dose reduction and kernel selection on quantifying emphysema using low-dose computed tomography (LDCT) and evaluated the efficiency of a deep learning-based kernel conversion technique in normalizing ker...
PURPOSE: To develop and evaluate a deep learning (DL) approach to extract rich information from high-resolution computed tomography (HRCT) of patients with chronic obstructive pulmonary disease (COPD).
We propose a deep learning system to automatically detect four explainable emphysema signs on frontal and lateral chest radiographs. Frontal and lateral chest radiographs from 3000 studies were retrospectively collected. Two radiologists annotated th...
OBJECTIVE: Chest CT can display the main pathogenic factors of chronic obstructive pulmonary disease (COPD), emphysema and airway wall remodeling. This study aims to establish deep convolutional neural network (CNN) models using these two imaging mar...
PURPOSE: We aimed to identify clinically relevant deep learning algorithms for emphysema quantification using low-dose chest computed tomography (LDCT) through an invitation-based competition.
Background Visual assessment remains the standard for evaluating emphysema at CT; however, it is time consuming, is subjective, requires training, and is affected by variability that may limit sensitivity to longitudinal change. Purpose To evaluate t...
PURPOSE: Quantitative analysis of emphysema volume is affected by the radiation dose and the CT reconstruction technique. We aim to evaluate the influence of a commercially available deep learning image reconstruction algorithm (DLIR) on the quantifi...
Background and Objectives: Although reducing the radiation dose level is important during diagnostic computed tomography (CT) applications, effective image quality enhancement strategies are crucial to compensate for the degradation that is caused by...