BACKGROUND: Necrotizing enterocolitis (NEC) is an inflammatory intestinal disease that primarily affects premature infants and is a major cause of death in the neonatal period. Approximately half of the affected infants require surgical intervention,...
To assess the image quality and radiation dose between reduced-dose CT with deep learning reconstruction (DLR) using SilverBeam filter and standard dose with iterative reconstruction (IR) in abdominopelvic CT. In total, 182 patients (mean age ± stand...
OBJECTIVE: To evaluate the efficacy of the "double-low" scanning protocol combined with the artificial intelligence iterative reconstruction (AIIR) algorithm for abdominal computed tomography (CT) enhancement in obese patients and to identify the opt...
OBJECTIVE: This study aimed to develop and validate a predictive model to detect osteoporosis using radiomic features and machine learning (ML) approaches from lumbar spine computed tomography (CT) images during an abdominal CT examination.
Accurate, reproducible body composition analysis from abdominal computed tomography (CT) images is critical for both clinical research and patient care. We present a fully automated, artificial intelligence (AI)-based pipeline that streamlines the en...
Over the past decade, radiomics has seen exponential growth, with over ten thousand publications in PubMed and a steady increase in related studies in journals like Abdominal Radiology. Despite the potential of radiomics, a major challenge lies in va...
PURPOSE: To evaluate the impact of an AI-based, noise reduction technique for compensation of image degradation on pediatric and neonatal chest and abdomen radiography using a visual grading analysis.
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...
Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Mar 15, 2025
PURPOSE: This study aims to investigate estimation of patient-specific organ doses from CT scans via radiomics feature-based SVR models with training parameter optimization, and maximize SVR models' predictive accuracy and robustness via fine-tuning ...
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