OBJECTIVES: Noise, commonly encountered on computed tomography (CT) images, can impact diagnostic accuracy. To reduce the image noise, we developed a deep-learning reconstruction (DLR) method that integrates deep convolutional neural networks into im...
PURPOSE: To describe a large, publicly available dataset comprising computed tomography (CT) projection data from patient exams, both at routine clinical doses and simulated lower doses.
Radiation dose in computed tomography (CT) has become a hot topic due to an upward trend in the number of CT procedures worldwide and the relatively high doses associated with these procedures. The main aim of this review article is to provide an ove...
OBJECTIVES: This study aimed to evaluate the usefulness of deep learning-based image conversion to improve the reproducibility of computed tomography (CT) radiomics features.
BACKGROUND: Deep artificial neural networks such as convolutional neural networks (CNNs) have been shown to be effective models for reducing noise in CT images while preserving anatomic details. A practical bottleneck for developing CNN-based denoisi...
Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial...
In this study, we propose a method for obtaining a new index to evaluate the resolution properties of computed tomography (CT) images in a task-based manner. This method applies a deep convolutional neural network (DCNN) machine learning system train...
BACKGROUND: Multislice spiral computed tomography (MSCT) requires an interpolation between adjacent detector rows during backprojection. Not satisfying the Nyquist sampling condition along the z-axis results in aliasing effects, also known as windmil...
BACKGROUND: This study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning...