AIMC Topic: Radiography, Abdominal

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Contrast-enhanced thin-slice abdominal CT with super-resolution deep learning reconstruction technique: evaluation of image quality and visibility of anatomical structures.

Japanese journal of radiology Nov 14, 2024
PURPOSE: To compare image quality and visibility of anatomical structures on contrast-enhanced thin-slice abdominal CT images reconstructed using super-resolution deep learning reconstruction (SR-DLR), deep learning-based reconstruction (DLR), and hy...
Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Radiographic Image Enhancement Female Radiography, Abdominal Aged Aged, 80 and over Adult Male Middle Aged Deep Learning Contrast Media Humans Retrospective Studies
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PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans.

Nature communications Nov 7, 2024
Delays or misdiagnoses in detecting pneumoperitoneum can significantly increase mortality and morbidity. We developed and validated a deep learning model designed to identify pneumoperitoneum in computed tomography images. The model is trained on abd...
Algorithms Prospective Studies Retrospective Studies Tomography, X-Ray Computed Female Middle Aged Humans Deep Learning Abdomen Radiography, Abdominal Pneumoperitoneum Aged Adult Sensitivity and Specificity Male
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Advances in spatial resolution and radiation dose reduction using super-resolution deep learning-based reconstruction for abdominal computed tomography: A phantom study.

Academic radiology Sep 20, 2024
RATIONALE AND OBJECTIVES: This study evaluated the performance of super-resolution deep learning-based reconstruction (SR-DLR) and compared with it that of hybrid iterative reconstruction (HIR) and normal-resolution DLR (NR-DLR) for enhancing image q...
Radiation Dosage Signal-To-Noise Ratio Phantoms, Imaging Radiography, Abdominal Humans Tomography, X-Ray Computed Deep Learning Radiographic Image Interpretation, Computer-Assisted
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Quality assessment of expedited AI generated reformatted images for ED acquired CT abdomen and pelvis imaging.

Abdominal radiology (New York) Sep 18, 2024
PURPOSE: Retrospectively compare image quality, radiologist diagnostic confidence, and time for images to reach PACS for contrast enhanced abdominopelvic CT examinations created on the scanner console by technologists versus those generated automatic...
Quality Assurance, Health Care Radiology Information Systems Radiography, Abdominal Contrast Media Emergency Service, Hospital Pelvis Radiographic Image Interpretation, Computer-Assisted Humans Artificial Intelligence Retrospective Studies Tomography, X-Ray Computed Female Aged Adult Male Middle Aged
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Artificial intelligence system for identification of overlooked lung metastasis in abdominopelvic computed tomography scans of patients with malignancy.

Diagnostic and interventional radiology (Ankara, Turkey) Sep 9, 2024
PURPOSE: This study aimed to evaluate whether an artificial intelligence (AI) system can identify basal lung metastatic nodules examined using abdominopelvic computed tomography (CT) that were initially overlooked by radiologists.
Humans Artificial Intelligence Aged, 80 and over Adult Sensitivity and Specificity Male Middle Aged Retrospective Studies Tomography, X-Ray Computed Female Aged Pelvis Radiography, Abdominal Lung ROC Curve Lung Neoplasms Abdomen
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Fast prediction of personalized abdominal organ doses from CT examinations by radiomics feature-based machine learning models.

Scientific reports Aug 20, 2024
The X-rays emitted during CT scans can increase solid cancer risks by damaging DNA, with the risk tied to patient-specific organ doses. This study aims to establish a new method to predict patient specific abdominal organ doses from CT examinations u...
Aged Adult Radiomics Male Machine Learning Middle Aged Software Humans Abdomen Precision Medicine Radiation Dosage Tomography, X-Ray Computed Monte Carlo Method Female Radiography, Abdominal
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Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans.

Scientific reports Jul 31, 2024
Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the ...
Adult Male Middle Aged Deep Learning Contrast Media Humans Tomography, X-Ray Computed Abdomen Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Female Feasibility Studies Aged Radiography, Abdominal Aged, 80 and over
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Deep learning-based fully automatic Risser stage assessment model using abdominal radiographs.

Pediatric radiology Jul 24, 2024
BACKGROUND: Artificial intelligence has been increasingly used in medical imaging and has demonstrated expert level performance in image classification tasks.
Adolescent Child Radiographic Image Interpretation, Computer-Assisted Radiography, Abdominal Humans Retrospective Studies Female Male Deep Learning
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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) 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.
Female Aged Aged, 80 and over Adult Male Middle Aged Deep Learning Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted Humans Algorithms Retrospective Studies Signal-To-Noise Ratio Tomography, X-Ray Computed Radiography, Abdominal 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 Jun 26, 2024
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).
Middle Aged Deep Learning Radiographic Image Interpretation, Computer-Assisted Humans Aged Iodine Radiography, Abdominal Prospective Studies Aged, 80 and over Contrast Media Radiography, Dual-Energy Scanned Projection Tomography, X-Ray Computed Adult Female Male
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