PURPOSE: To evaluate the value of deep learning image reconstruction (DLIR) in improving image quality of virtual non-hydroxyapatite (VNHAP) and virtual monoenergetic images (VMIs), and radiologists' performance in detecting acute vertebral compressi...
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).
PURPOSE: Deep Learning Spectral Reconstruction (DLSR) potentially improves dual-energy CT (DECT) image quality, but there is a paucity of research involving human abdominal DECT scans. The purpose of this study was to comprehensively evaluate image q...
Dual-energy CT (DECT), also known as spectral CT, has advanced diagnostic capabilities in head and neck pathologies beyond those of conventional single-energy CT (SECT). By having images at two distinct energy levels, DECT generates virtual monoenerg...
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...
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).
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...
Acta radiologica (Stockholm, Sweden : 1987)
Oct 22, 2024
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...
Acta radiologica (Stockholm, Sweden : 1987)
Jul 21, 2024
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.
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