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Radiography, Dual-Energy Scanned Projection

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Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT.

BMC medical imaging 38926711
BACKGROUND: To assess the improvement of image quality and diagnostic acceptance of thinner slice iodine maps enabled by deep learning image reconstruction (DLIR) in abdominal dual-energy CT (DECT).
Adult Aged Aged, 80 and over Contrast Media Deep Learning Female Humans Iodine Male Middle Aged Prospective Studies Radiographic Image Interpretation, Computer-Assisted Radiography, Abdominal Radiography, Dual-Energy Scanned Projection Tomography, X-Ray Computed
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Low energy virtual monochromatic CT with deep learning image reconstruction to improve delineation of endoleaks.

Clinical radiology 39079807
AIM: This study aimed to investigate the utility of low-energy virtual monochromatic imaging (VMI) combined with deep-learning image reconstruction (DLIR) in improving the delineation of endoleaks (ELs) after endovascular aortic repair (EVAR) in cont...
Aged Aged, 80 and over Aortic Aneurysm, Abdominal Contrast Media Deep Learning Endoleak Endovascular Procedures Female Humans Male Radiographic Image Interpretation, Computer-Assisted Radiography, Dual-Energy Scanned Projection Retrospective Studies Tomography, X-Ray Computed
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Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques.

European radiology 39046499
OBJECTIVES: To perform a multi-reader comparison of multiparametric dual-energy computed tomography (DECT) images reconstructed with deep-learning image reconstruction (DLIR) and standard-of-care adaptive statistical iterative reconstruction-V (ASIR-...
Adult Aged Aged, 80 and over Artifacts Deep Learning Female Humans Image Processing, Computer-Assisted Male Middle Aged Radiographic Image Interpretation, Computer-Assisted Radiography, Dual-Energy Scanned Projection Retrospective Studies Tomography, X-Ray Computed
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Improving diagnostic confidence in low-dose dual-energy CTE with low energy level and deep learning reconstruction.

European journal of radiology 39033690
OBJECTIVE: To demonstrate the value of using 50 keV virtual monochromatic images with deep learning image reconstruction (DLIR) in low-dose dual-energy CT enterography (CTE).
Adult Aged Crohn Disease Deep Learning Female Humans Male Middle Aged Prospective Studies Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Radiography, Dual-Energy Scanned Projection Signal-To-Noise Ratio Tomography, X-Ray Computed
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Deep learning image reconstruction for low-kiloelectron volt virtual monoenergetic images in abdominal dual-energy CT: medium strength provides higher lesion conspicuity.

Acta radiologica (Stockholm, Sweden : 1987) 39033390
BACKGROUND: The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined.
Adult Aged Aged, 80 and over Algorithms Deep Learning Female Humans Image Processing, Computer-Assisted Male Middle Aged Radiographic Image Interpretation, Computer-Assisted Radiography, Abdominal Radiography, Dual-Energy Scanned Projection Retrospective Studies Signal-To-Noise Ratio Tomography, X-Ray Computed
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Image quality of virtual monochromatic and material density iodine images for evaluation of head and neck neoplasms using deep learning-based CT image reconstruction - A retrospective observational study.

European journal of radiology 39500043
PURPOSE: To compare the quality of deep learning image reconstructed (DLIR) virtual monochromatic images (VMI) and material density (MD) iodine images from dual-energy computed tomography (DECT) for the evaluation of head and neck neoplasms with CT s...
Adult Aged Aged, 80 and over Artifacts Contrast Media Deep Learning Female Head and Neck Neoplasms Humans Iodine Male Middle Aged Radiographic Image Interpretation, Computer-Assisted Radiography, Dual-Energy Scanned Projection Retrospective Studies Tomography, X-Ray Computed
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Multi-Energy Evaluation of Image Quality in Spectral CT Pulmonary Angiography Using Different Strength Deep Learning Spectral Reconstructions.

Academic radiology 39732618
RATIONALE AND OBJECTIVES: To evaluate and compare image quality of different energy levels of virtual monochromatic images (VMIs) using standard versus strong deep learning spectral reconstruction (DLSR) on dual-energy CT pulmonary angiogram (DECT-PA...
Adult Aged Aged, 80 and over Computed Tomography Angiography Deep Learning Female Humans Male Middle Aged Pulmonary Artery Pulmonary Embolism Radiographic Image Interpretation, Computer-Assisted Radiography, Dual-Energy Scanned Projection Retrospective Studies Signal-To-Noise Ratio
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Comparing fully automated AI body composition biomarkers at differing virtual monoenergetic levels using dual-energy CT.

Abdominal radiology (New York) 39643734
PURPOSE: To investigate the behavior of artificial intelligence (AI) CT-based body composition biomarkers at different virtual monoenergetic imaging (VMI) levels using dual-energy CT (DECT).
Adult Aged Aged, 80 and over Algorithms Artificial Intelligence Biomarkers Body Composition Contrast Media Female Humans Male Middle Aged Radiographic Image Interpretation, Computer-Assisted Radiography, Dual-Energy Scanned Projection Retrospective Studies Tomography, X-Ray Computed
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Deep-learning reconstruction enhances image quality of Adamkiewicz Artery in low-keV dual-energy CT.

Acta radiologica (Stockholm, Sweden : 1987) 39435504
BACKGROUND: Low-keV virtual monoenergetic images (VMIs) of dual-energy computed tomography (CT) enhances iodine contrast for detecting small arteries like the Adamkiewicz artery (AKA), but image noise can be problematic. Deep-learning image reconstru...
Adult Aged Aged, 80 and over Computed Tomography Angiography Contrast Media Deep Learning Female Humans Male Middle Aged Radiographic Image Interpretation, Computer-Assisted Radiography, Dual-Energy Scanned Projection Retrospective Studies Signal-To-Noise Ratio Spinal Cord
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Image quality improvement in head and neck angiography based on dual-energy CT and deep learning.

BMC medical imaging 40211222
OBJECTIVE: Compare the image quality of image reconstructed using deep learning-based image reconstruction (DLIR) and iterative reconstruction algorithms for head and neck dual-energy CT angiography (DECTA).
Adult Aged Aged, 80 and over Algorithms Artifacts Computed Tomography Angiography Deep Learning Female Head Head and Neck Neoplasms Humans Male Middle Aged Neck Prospective Studies Quality Improvement Radiographic Image Interpretation, Computer-Assisted Radiography, Dual-Energy Scanned Projection Signal-To-Noise Ratio Tomography, X-Ray Computed
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