Artificial CT images can enhance variation of case images in diagnostic radiology skills training
Objectives We sought to investigate if artificial medical images can blend with original ones and whether they adhere to the variable …
Can we increase efficiency of CT lung cancer screening by combining with CVD and COPD screening? Results of an early economic evaluation.
Objectives Estimating the maximum acceptable cost (MAC) per screened individual for low-dose computed tomography (LDCT) lung cancer …
Deep learning for automated exclusion of cardiac CT examinations negative for coronary artery calcium
Coronary artery calcium (CAC) score has shown to be an accurate predictor of future cardiovascular events. Early detection by CAC …
High-pitch dual-source CT for coronary artery calcium scoring: A head-to-head comparison of non-triggered chest versus triggered cardiac acquisition
To determine the effect of low-dose, high-pitch non-electrocardiographic (ECG)-triggered chest CT on coronary artery calcium (CAC) …
Deep learning-based pulmonary nodule detection: Effect of slab thickness in maximum intensity projections at the nodule candidate detection stage
To investigate the effect of the slab thickness in maximum intensity projections (MIPs) on the candidate detection performance of a …
Application of artificial intelligence in cardiac CT: From basics to clinical practice
Research into the possibilities of AI in cardiac CT has been growing rapidly in the last decade. With the rise of publicly available …
Manual Correction of CT-Derived Airway Segmentations for Artificial Intelligence Applications: A Technical Note
Artificial Intelligence (AI) tools provide rapid analysis of complex datasets, at the cost of flexibility in the data that is fed to …
B3CARE XNAT-Based Research Infrastructure for Imaging Biomarker Evaluation
The B3CARE project aims to validate and evaluate imaging biomarkers for the Big-3 diseases (lung cancer, COPD, and cardiovascular …
Automated Pipeline for XNAT Data Bulk Export
Lung cancer, chronic obstructive pulmonary disease, and cardiovascular disease, the so-called Big-3 (B3), are responsible for high …
Efficient convolutional neural networks for multi-planar lung nodule detection: improvement on small nodule identification
In clinical practice, small lung nodules can be easily overlooked by radiologists. The paper aims to provide an efficient and accurate …
Potential for dose reduction in CT emphysema densitometry with post-scan noise reduction: a phantom study
The aim of this phantom study was to investigate the effect of scan parameters and noise suppression techniques on the minimum …
B3Care Background
Lung cancer, chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD), the so-called Big-3 (B3), are expected to …
Early imaging biomarkers of lung cancer, COPD and coronary artery disease in the general population: rationale and design of the ImaLife (Imaging in Lifelines) Study
Lung cancer, chronic obstructive pulmonary disease (COPD), and coronary artery disease (CAD) are expected to cause most deaths by 2050. …
Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection
Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not …
Screening for Early Lung Cancer, Chronic Obstructive Pulmonary Disease, and Cardiovascular Disease (the Big-3) Using Low-dose Chest Computed Tomography: Current Evidence and Technical Considerations
Lung cancer, chronic obstructive pulmonary disease, and cardiovascular disease are highly prevalent in the general population and …