Radiographic Image Interpretation, Computer-Assisted

Impact of deep Learning-enhanced contrast on diagnostic accuracy in stroke CT angiography.

European journal of radiology Oct 28, 2024

PURPOSE: To examine the impact of deep learning-augmented contrast enhancement on image quality and diagnostic accuracy of poorly contrasted CT angiography in patients with suspected stroke.

Deep Learning Contrast Media Radiographic Image Interpretation, Computer-Assisted Signal-To-Noise Ratio Stroke Computed Tomography Angiography Cerebral Angiography Radiographic Image Enhancement Aged Humans Sensitivity and Specificity Male Middle Aged Retrospective Studies Female Reproducibility of Results

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Evaluation of a deep learning-based software to automatically detect and quantify breast arterial calcifications on digital mammogram.

Diagnostic and interventional imaging Oct 26, 2024

PURPOSE: The purpose of this study was to evaluate an artificial intelligence (AI) software that automatically detects and quantifies breast arterial calcifications (BAC).

Radiographic Image Interpretation, Computer-Assisted Breast Vascular Calcification Breast Diseases Adult Humans Sensitivity and Specificity Software Middle Aged Retrospective Studies Deep Learning Mammography Tomography, X-Ray Computed Female Aged Aged, 80 and over

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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 Oct 25, 2024

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...

Humans Iodine Head and Neck Neoplasms Contrast Media Radiographic Image Interpretation, Computer-Assisted Aged, 80 and over Retrospective Studies Adult Tomography, X-Ray Computed Male Artifacts Female Middle Aged Radiography, Dual-Energy Scanned Projection Aged Deep Learning

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Significance of Image Reconstruction Parameters for Future Lung Cancer Risk Prediction Using Low-Dose Chest Computed Tomography and the Open-Access Sybil Algorithm.

Investigative radiology Oct 23, 2024

PURPOSE: Sybil is a validated publicly available deep learning-based algorithm that can accurately predict lung cancer risk from a single low-dose computed tomography (LDCT) scan. We aimed to study the effect of image reconstruction parameters and CT...

Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Humans Middle Aged Risk Assessment Algorithms Deep Learning Tomography, X-Ray Computed Lung Neoplasms Female Aged Male

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Lung nodule classification using radiomics model trained on degraded SDCT images.

Computer methods and programs in biomedicine Oct 23, 2024

BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) screening has shown promise in reducing lung cancer mortality; however, it suffers from high false positive rates and a scarcity of available annotated datasets. To overcome these challeng...

Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Radiomics Image Processing, Computer-Assisted Lung Neoplasms Logistic Models Humans Machine Learning Algorithms Tomography, X-Ray Computed Reproducibility of Results

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Deep-learning reconstruction enhances image quality of Adamkiewicz Artery in low-keV dual-energy CT.

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...

Radiography, Dual-Energy Scanned Projection Radiographic Image Interpretation, Computer-Assisted Spinal Cord Signal-To-Noise Ratio Computed Tomography Angiography Retrospective Studies Humans Female Adult Aged Male Aged, 80 and over Middle Aged Deep Learning Contrast Media

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Automated AI-based coronary calcium scoring using retrospective CT data from SCAPIS is accurate and correlates with expert scoring.

European radiology Oct 18, 2024

OBJECTIVES: Evaluation of the correlation and agreement between AI and semi-automatic evaluations of calcium scoring CT (CSCT) examinations using extensive data from the Swedish CardioPulmonary bio-Image study (SCAPIS).

Humans Artificial Intelligence Retrospective Studies Male Middle Aged Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Vascular Calcification Tomography, X-Ray Computed Female Reproducibility of Results Sweden

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Application of a Deep Learning-Based Contrast-Boosting Algorithm to Low-Dose Computed Tomography Pulmonary Angiography With Reduced Iodine Load.

Journal of computer assisted tomography Oct 10, 2024

OBJECTIVE: The aim of this study was to assess the effectiveness of a deep learning-based image contrast-boosting algorithm by enhancing the image quality of low-dose computed tomography pulmonary angiography at reduced iodine load.

Algorithms Retrospective Studies Female Aged Aged, 80 and over Humans Computed Tomography Angiography Radiographic Image Enhancement Pulmonary Artery Iodine Contrast Media Radiographic Image Interpretation, Computer-Assisted Adult Pulmonary Embolism Male Radiation Dosage Middle Aged Deep Learning

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An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Oct 10, 2024

The accurate categorization of lung nodules in CT scans is an essential aspect in the prompt detection and diagnosis of lung cancer. The categorization of grade and texture for nodules is particularly significant since it can aid radiologists and cli...

Humans Tomography, X-Ray Computed Deep Learning Lung Neoplasms Solitary Pulmonary Nodule Diagnosis, Differential Radiographic Image Interpretation, Computer-Assisted

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Energy-integrating detector based ultra-high-resolution CT with deep learning reconstruction for the assessment of calcified lesions in coronary artery disease.

Journal of cardiovascular computed tomography Oct 8, 2024

BACKGROUND: The aim of this study to compare of the image quality of calcified lesions in coronary artery disease between deep learning reconstruction (DLR) and model-based iterative reconstruction (MBIR) on energy-integrating detector (EID) based ul...

Humans Retrospective Studies Female Reproducibility of Results Aged Multidetector Computed Tomography Predictive Value of Tests Male Vascular Calcification Coronary Angiography Middle Aged Phantoms, Imaging Radiographic Image Interpretation, Computer-Assisted Severity of Illness Index Computed Tomography Angiography Coronary Artery Disease Deep Learning Coronary Vessels

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Radiographic Image Interpretation, Computer-Assisted

Impact of deep Learning-enhanced contrast on diagnostic accuracy in stroke CT angiography.

Evaluation of a deep learning-based software to automatically detect and quantify breast arterial calcifications on digital mammogram.

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.

Significance of Image Reconstruction Parameters for Future Lung Cancer Risk Prediction Using Low-Dose Chest Computed Tomography and the Open-Access Sybil Algorithm.

Lung nodule classification using radiomics model trained on degraded SDCT images.

Deep-learning reconstruction enhances image quality of Adamkiewicz Artery in low-keV dual-energy CT.

Automated AI-based coronary calcium scoring using retrospective CT data from SCAPIS is accurate and correlates with expert scoring.

Application of a Deep Learning-Based Contrast-Boosting Algorithm to Low-Dose Computed Tomography Pulmonary Angiography With Reduced Iodine Load.

An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning.

Energy-integrating detector based ultra-high-resolution CT with deep learning reconstruction for the assessment of calcified lesions in coronary artery disease.

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