BACKGROUND. Because thick-section images (typically 3–5 mm) have low image noise, radiologists typically use them to perform clinical interpretation, although they may additionally refer to thin-section images (typically 0.5–0.625 mm) for problem solving. Deep learning reconstruction (DLR) can yield thin-section images with low noise. OBJECTIVE. The purpose of this study is to compare abdominopelvic CT image quality between thin-section DLR images and thin- and thick-section hybrid iterative reconstruction (HIR) images. METHODS. This retrospective study included 50 patients (31 men and 19 women; median age, 64 years) who underwent abdominopelvic CT between June 15, 2020, and July 29, 2020. Images were reconstructed at 0.5-mm section using D...
Purpose or Learning Objective To compare T2 and diffusion-weighted images (DWI) in upper abdomen ma...
Abstract Deep learning-based CT image reconstruction (DLR) is a state-of-the-art method for obtainin...
Objective To compare the image quality between the vendor-agnostic and vendor-specific algorithms on...
Objective: To determine the difference in CT values and image quality of abdominal CT images reconst...
Objective: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abd...
Purpose or Learning Objective To perform a comprehensive interindividual objective and subjective i...
Purpose: To evaluate image quality, image noise and potential dose reduction of low-dose CT scans of...
Objective To evaluate the effect of a commercial deep learning algorithm on the image quality of che...
We aimed to thoroughly characterize image quality of a novel deep learning image reconstruction (DLI...
The purpose of this study was to investigate whether the novel image-based noise reduction software ...
International audiencePurpose: To compare the impact on CT image quality and dose reduction of two v...
Abstract Background Efforts to reduce the radiation dose have continued steadily, with new reconstru...
Abstract Background To assess the impact of the new version of a deep learning (DL) spectral reconst...
Introduction: Cadaveric studies provide a means of safely assessing new technologies and optimizing ...
Purpose: To determine the effect of tube load, model-based iterative reconstruction (MBIR) strength ...
Purpose or Learning Objective To compare T2 and diffusion-weighted images (DWI) in upper abdomen ma...
Abstract Deep learning-based CT image reconstruction (DLR) is a state-of-the-art method for obtainin...
Objective To compare the image quality between the vendor-agnostic and vendor-specific algorithms on...
Objective: To determine the difference in CT values and image quality of abdominal CT images reconst...
Objective: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abd...
Purpose or Learning Objective To perform a comprehensive interindividual objective and subjective i...
Purpose: To evaluate image quality, image noise and potential dose reduction of low-dose CT scans of...
Objective To evaluate the effect of a commercial deep learning algorithm on the image quality of che...
We aimed to thoroughly characterize image quality of a novel deep learning image reconstruction (DLI...
The purpose of this study was to investigate whether the novel image-based noise reduction software ...
International audiencePurpose: To compare the impact on CT image quality and dose reduction of two v...
Abstract Background Efforts to reduce the radiation dose have continued steadily, with new reconstru...
Abstract Background To assess the impact of the new version of a deep learning (DL) spectral reconst...
Introduction: Cadaveric studies provide a means of safely assessing new technologies and optimizing ...
Purpose: To determine the effect of tube load, model-based iterative reconstruction (MBIR) strength ...
Purpose or Learning Objective To compare T2 and diffusion-weighted images (DWI) in upper abdomen ma...
Abstract Deep learning-based CT image reconstruction (DLR) is a state-of-the-art method for obtainin...
Objective To compare the image quality between the vendor-agnostic and vendor-specific algorithms on...