Objective: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those of standard-dose CT (SDCT) images. Materials and methods: This retrospective study included 123 patients (mean age ± standard deviation, 63 ± 11 years; male:female, 70:53) who underwent contrast-enhanced abdominopelvic LDCT between May and August 2020 and had prior SDCT obtained using the same CT scanner within a year. LDCT images were reconstructed with hybrid iterative reconstruction (h-IR) and DLIR at medium and high strengths (DLIR-M and DLIR-H), while SDCT images were reconstructed with h-IR. For quantitative image quality analysis, ...
Abstract Deep learning-based CT image reconstruction (DLR) is a state-of-the-art method for obtainin...
Abstract Objective Few studies have explored the clinical feasibility of using deep-learning reconst...
The widespread use of computed tomography (CT) has increased the medical radiation exposure and canc...
Purpose: To evaluate image quality, image noise and potential dose reduction of low-dose CT scans of...
Objective To determine the difference in CT values and image quality of abdominal CT images reconstr...
Objective To evaluate the effect of a commercial deep learning algorithm on the image quality of che...
Purpose or Learning Objective To perform a comprehensive interindividual objective and subjective i...
We aimed to thoroughly characterize image quality of a novel deep learning image reconstruction (DLI...
Introduction: Cadaveric studies provide a means of safely assessing new technologies and optimizing ...
Background: Efforts to reduce the radiation dose have continued steadily, with new reconstruction te...
BACKGROUND. Because thick-section images (typically 3–5 mm) have low image noise, radiologists typic...
To compare deep learning (True Fidelity, TF) and partial model based Iterative Reconstruction (ASiR-...
International audiencePurpose: To compare the impact on CT image quality and dose reduction of two v...
OBJECTIVES: The objective of this study was to compare image quality (objective and subjective param...
Abstract Background To assess the impact of the new version of a deep learning (DL) spectral reconst...
Abstract Deep learning-based CT image reconstruction (DLR) is a state-of-the-art method for obtainin...
Abstract Objective Few studies have explored the clinical feasibility of using deep-learning reconst...
The widespread use of computed tomography (CT) has increased the medical radiation exposure and canc...
Purpose: To evaluate image quality, image noise and potential dose reduction of low-dose CT scans of...
Objective To determine the difference in CT values and image quality of abdominal CT images reconstr...
Objective To evaluate the effect of a commercial deep learning algorithm on the image quality of che...
Purpose or Learning Objective To perform a comprehensive interindividual objective and subjective i...
We aimed to thoroughly characterize image quality of a novel deep learning image reconstruction (DLI...
Introduction: Cadaveric studies provide a means of safely assessing new technologies and optimizing ...
Background: Efforts to reduce the radiation dose have continued steadily, with new reconstruction te...
BACKGROUND. Because thick-section images (typically 3–5 mm) have low image noise, radiologists typic...
To compare deep learning (True Fidelity, TF) and partial model based Iterative Reconstruction (ASiR-...
International audiencePurpose: To compare the impact on CT image quality and dose reduction of two v...
OBJECTIVES: The objective of this study was to compare image quality (objective and subjective param...
Abstract Background To assess the impact of the new version of a deep learning (DL) spectral reconst...
Abstract Deep learning-based CT image reconstruction (DLR) is a state-of-the-art method for obtainin...
Abstract Objective Few studies have explored the clinical feasibility of using deep-learning reconst...
The widespread use of computed tomography (CT) has increased the medical radiation exposure and canc...