Low-dose computed tomography

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 scoring might reduce the number of deaths by cardiovascular disease (CVD). Automatically excluding scans which test …

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) detection, quantification and risk stratification, compared to ECG-triggered cardiac CT. We selected 1,000 participants …

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 deep learning-based computer-aided detection (DL-CAD) system for pulmonary nodule detection in CT scans. The public …

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 them. To have the best performance, AI tools require training on data similar to the data that will be encountered …

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 rates of morbidity and mortality. B3CARE is a research collaboration project, with a final ambition to establish an …

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 disease). To this end a large-scale, high- quality imaging data biobank is established containing data from different …

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 detection system for small lung nodules while keeping good performance for large nodules. We propose a multi-planar …

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 radiation dose for acceptable image quality for CT emphysema densitometry. The COPDGene phantom was scanned on a third …

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 routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. …

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. State-of-the-art computed tomography (CT) allows early detection of lung cancer and simultaneous evaluation of …