AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Improving functional correlation of quantification of interstitial lung disease by reducing the vendor difference of CT using generative adversarial network (GAN) style conversion.

European journal of radiology Dec 22, 2024
OBJECTIVE: To assess whether CT style conversion between different CT vendors using a routable generative adversarial network (RouteGAN) could minimize variation in ILD quantification, resulting in improved functional correlation of quantitative CT (...
Respiratory Function Tests Generative Adversarial Networks Deep Learning Retrospective Studies Idiopathic Pulmonary Fibrosis Humans Aged Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Female Male Lung Diseases, Interstitial Reproducibility of Results Middle Aged Lung
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Image quality assessment of artificial intelligence iterative reconstruction for low dose unenhanced abdomen: comparison with hybrid iterative reconstruction.

Abdominal radiology (New York) Dec 21, 2024
OBJECTIVES: To assess the impact of artificial intelligence iterative reconstruction algorithms (AIIR) on image quality with phantom and clinical studies.
Radiography, Abdominal Aged, 80 and over Adult Male Middle Aged Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Signal-To-Noise Ratio Phantoms, Imaging Algorithms Tomography, X-Ray Computed Female Aged Humans Artificial Intelligence
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Deep learning model for low-dose CT late iodine enhancement imaging and extracellular volume quantification.

European radiology Dec 20, 2024
OBJECTIVES: To develop and validate deep learning (DL)-models that denoise late iodine enhancement (LIE) images and enable accurate extracellular volume (ECV) quantification.
Female Aged Male Middle Aged Humans Retrospective Studies Tomography, X-Ray Computed Radiation Dosage Signal-To-Noise Ratio Computed Tomography Angiography Myocardial Perfusion Imaging Deep Learning Iodine Contrast Media Radiographic Image Interpretation, Computer-Assisted Radiographic Image Enhancement
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AI-assisted detection for chest X-rays (AID-CXR): a multi-reader multi-case study protocol.

BMJ open Dec 20, 2024
INTRODUCTION: A chest X-ray (CXR) is the most common imaging investigation performed worldwide. Advances in machine learning and computer vision technologies have led to the development of several artificial intelligence (AI) tools to detect abnormal...
Radiographic Image Interpretation, Computer-Assisted Humans Artificial Intelligence Lung Diseases Algorithms Radiography, Thoracic Research Design Retrospective Studies ROC Curve
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A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer.

Abdominal radiology (New York) Dec 18, 2024
OBJECTIVE: To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images.
Male Middle Aged Deep Learning Female Neoplasm Grading Radiographic Image Interpretation, Computer-Assisted Urinary Bladder Aged Aged, 80 and over Radiomics Humans Retrospective Studies Tomography, X-Ray Computed Urinary Bladder Neoplasms
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Quantitative analysis of deep learning reconstruction in CT angiography: Enhancing CNR and reducing dose.

Journal of X-ray science and technology Dec 18, 2024
BACKGROUND: Computed tomography angiography (CTA) provides significant information on image quality in vascular imaging, thus offering high-resolution images despite having the disadvantages of increased radiation doses and contrast agent-related sid...
Phantoms, Imaging Contrast Media Humans Computed Tomography Angiography Radiographic Image Interpretation, Computer-Assisted Deep Learning Radiation Dosage Image Processing, Computer-Assisted Signal-To-Noise Ratio
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Noise Reduction in Brain CT: A Comparative Study of Deep Learning and Hybrid Iterative Reconstruction Using Multiple Parameters.

Tomography (Ann Arbor, Mich.) Dec 18, 2024
OBJECTIVES: We evaluated the noise reduction effects of deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) in brain computed tomography (CT).
Male Middle Aged Deep Learning Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted Signal-To-Noise Ratio Phantoms, Imaging Tomography, X-Ray Computed Female Aged Adult Humans Brain
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Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current.

BMC medical informatics and decision making Dec 18, 2024
BACKGROUND: The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this s...
Humans Radiation Dosage Radiography, Abdominal Male Middle Aged Deep Learning Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Female Aged Adult
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Segmentation for mammography classification utilizing deep convolutional neural network.

BMC medical imaging Dec 18, 2024
BACKGROUND: Mammography for the diagnosis of early breast cancer (BC) relies heavily on the identification of breast masses. However, in the early stages, it might be challenging to ascertain whether a breast mass is benign or malignant. Consequently...
Radiographic Image Interpretation, Computer-Assisted Breast Humans Neural Networks, Computer Female Breast Neoplasms Deep Learning Mammography
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Deep learning algorithm enables automated Cobb angle measurements with high accuracy.

Skeletal radiology Dec 17, 2024
OBJECTIVE: To determine the accuracy of automatic Cobb angle measurements by deep learning (DL) on full spine radiographs.
Aged Aged, 80 and over Humans Young Adult Child, Preschool Adult Male Algorithms Child Middle Aged Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Deep Learning Scoliosis Female Adolescent Reproducibility of Results
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