AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Accuracy of two deep learning-based reconstruction methods compared with an adaptive statistical iterative reconstruction method for solid and ground-glass nodule volumetry on low-dose and ultra-low-dose chest computed tomography: A phantom study.

PloS one Jun 23, 2022
No published studies have evaluated the accuracy of volumetric measurement of solid nodules and ground-glass nodules on low-dose or ultra-low-dose chest computed tomography, reconstructed using deep learning-based algorithms. This is an important iss...
Early Detection of Cancer Tomography, X-Ray Computed Humans Radiographic Image Interpretation, Computer-Assisted Deep Learning Algorithms Lung Neoplasms Radiation Dosage Phantoms, Imaging
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Lung-Optimized Deep-Learning-Based Reconstruction for Ultralow-Dose CT.

Academic radiology Jun 20, 2022
RATIONALE AND OBJECTIVES: To evaluate the image properties of lung-specialized deep-learning-based reconstruction (DLR) and its applicability in ultralow-dose CT (ULDCT) relative to hybrid- (HIR) and model-based iterative-reconstructions (MBIR).
Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Humans Algorithms Tomography, X-Ray Computed Lung Deep Learning
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Deep learning-based reconstruction of virtual monoenergetic images of kVp-switching dual energy CT for evaluation of hypervascular liver lesions: Comparison with standard reconstruction technique.

European journal of radiology Jun 15, 2022
OBJECTIVE: To investigate clinical applicability of deep learning(DL)-based reconstruction of virtual monoenergetic images(VMIs) of arterial phase liver CT obtained by rapid kVp-switching dual-energy CT for evaluation of hypervascular liver lesions.
Humans Retrospective Studies Tomography, X-Ray Computed Signal-To-Noise Ratio Deep Learning Liver Neoplasms Radiographic Image Interpretation, Computer-Assisted
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Clinical validation of an AI-based motion correction reconstruction algorithm in cerebral CT.

European radiology Jun 9, 2022
OBJECTIVES: To evaluate the clinical performance of an artificial intelligence (AI)-based motion correction (MC) reconstruction algorithm for cerebral CT.
Algorithms Humans Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Artificial Intelligence Radiation Dosage Tomography, X-Ray Computed Signal-To-Noise Ratio
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Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice: A Methodologic Framework for Clinical Testing of Artificial Intelligence Tools.

Journal of the American College of Radiology : JACR Jun 8, 2022
Artificial Intelligence Deep Learning Breast Density Mammography Radiographic Image Interpretation, Computer-Assisted
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Assessment of low-dose paranasal sinus CT imaging using a new deep learning image reconstruction technique in children compared to adaptive statistical iterative reconstruction V (ASiR-V).

BMC medical imaging Jun 3, 2022
PURPOSE: To compare the effects of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction V (ASiR-V) on image quality in low-dose computed tomography (CT) of paranasal sinuses in children.
Retrospective Studies Tomography, X-Ray Computed Deep Learning Humans Algorithms Radiographic Image Interpretation, Computer-Assisted Paranasal Sinuses Radiation Dosage Child Image Processing, Computer-Assisted Double-Blind Method
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Comparison of a Deep Learning-Based Reconstruction Algorithm with Filtered Back Projection and Iterative Reconstruction Algorithms for Pediatric Abdominopelvic CT.

Korean journal of radiology May 27, 2022
OBJECTIVE: To compare a deep learning-based reconstruction (DLR) algorithm for pediatric abdominopelvic computed tomography (CT) with filtered back projection (FBP) and iterative reconstruction (IR) algorithms.
Tomography, X-Ray Computed Male Deep Learning Humans Algorithms Image Processing, Computer-Assisted Adolescent Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Child, Preschool Radiation Dosage Child
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The impact of deep learning reconstruction on image quality and coronary CT angiography-derived fractional flow reserve values.

European radiology May 21, 2022
OBJECTIVES: To explore the impact of deep learning reconstruction (DLR) on image quality and machine learning-based coronary CT angiography (CTA)-derived fractional flow reserve (CT-FFR) values.
Humans Retrospective Studies Fractional Flow Reserve, Myocardial Coronary Angiography Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Computed Tomography Angiography Deep Learning
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Feasibility of Deep Learning-Based Noise and Artifact Reduction in Coronal Reformation of Contrast-Enhanced Chest Computed Tomography.

Journal of computer assisted tomography May 20, 2022
PURPOSE: This study aimed to evaluate the feasibility of a deep learning method for imaging artifact and noise reduction in coronal reformation of contrast-enhanced chest computed tomography (CT).
Radiographic Image Interpretation, Computer-Assisted Feasibility Studies Tomography, X-Ray Computed Artifacts Signal-To-Noise Ratio Deep Learning Image Processing, Computer-Assisted Humans Algorithms
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Pulmonary emphysema quantification at low dose chest CT using Deep Learning image reconstruction.

European journal of radiology May 5, 2022
PURPOSE: Quantitative analysis of emphysema volume is affected by the radiation dose and the CT reconstruction technique. We aim to evaluate the influence of a commercially available deep learning image reconstruction algorithm (DLIR) on the quantifi...
Pulmonary Emphysema Radiation Dosage Humans Emphysema Algorithms Image Processing, Computer-Assisted Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Deep Learning
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