AIMC Topic: Contrast Media

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Superior objective and subjective image quality of deep learning reconstruction for low-dose abdominal CT imaging in comparison with model-based iterative reconstruction and filtered back projection.

The British journal of radiology Jul 1, 2021
OBJECTIVE: This study aimed to conduct objective and subjective comparisons of image quality among abdominal computed tomography (CT) reconstructions with deep learning reconstruction (DLR) algorithms, model-based iterative reconstruction (MBIR), and...
Tomography, X-Ray Computed Female Aged Aged, 80 and over Humans Retrospective Studies Contrast Media Liver Radiographic Image Interpretation, Computer-Assisted Artifacts Radiography, Abdominal Adult Male Middle Aged Deep Learning
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Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer Tomography.

Technology in cancer research & treatment Jan 1, 2021
This study aimed to explore the ability of texture parameters combining with machine learning methods in distinguishing intrahepatic cholangiocarcinoma (ICCA) and hepatic lymphoma (HL). A total of 28 patients with HL and 101 patients with ICCA were...
Humans Cholangiocarcinoma Radiographic Image Enhancement ROC Curve Reproducibility of Results Diagnosis, Differential Image Processing, Computer-Assisted Machine Learning Aged Neoplasm Grading Lymphoma Algorithms Male Tumor Burden Contrast Media Tomography, X-Ray Computed Bile Duct Neoplasms Female Middle Aged
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[Application of artificial intelligence combined with multi-parametric MRI in the early diagnosis of prostate cancer].

Zhonghua nan ke xue = National journal of andrology Sep 1, 2020
OBJECTIVE: To explore the value of artificial intelligence combined with multi-parametric MRI (AI-mpMRI) in the early diagnosis of prostate cancer.
Multiparametric Magnetic Resonance Imaging Humans Artificial Intelligence Retrospective Studies Sensitivity and Specificity Male Contrast Media Early Detection of Cancer Prostatic Neoplasms
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Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: choice of four-phase and three-phase CT imaging protocol.

Abdominal radiology (New York) Sep 1, 2020
PURPOSE: To evaluate whether a three-phase dynamic contrast-enhanced CT protocol, when combined with a deep learning model, has similar accuracy in differentiating hepatocellular carcinoma (HCC) from other focal liver lesions (FLLs) compared with a f...
Aged Aged, 80 and over Young Adult Adult Humans Retrospective Studies Female Carcinoma, Hepatocellular Contrast Media Tomography, Spiral Computed Male Middle Aged Deep Learning Liver Neoplasms
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Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation.

The Lancet. Digital health Jun 1, 2020
BACKGROUND: The diagnostic performance of CT for pancreatic cancer is interpreter-dependent, and approximately 40% of tumours smaller than 2 cm evade detection. Convolutional neural networks (CNNs) have shown promise in image analysis, but the networ...
Radiographic Image Interpretation, Computer-Assisted Pancreas Racial Groups Contrast Media Retrospective Studies Radiographic Image Enhancement Humans Taiwan Male Aged Tomography, X-Ray Computed Diagnosis, Differential Female Sensitivity and Specificity Middle Aged Reproducibility of Results Pancreatic Neoplasms Deep Learning
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Machine Learning and Deep Neural Networks: Applications in Patient and Scan Preparation, Contrast Medium, and Radiation Dose Optimization.

Journal of thoracic imaging May 1, 2020
Artificial intelligence (AI) algorithms are dependent on a high amount of robust data and the application of appropriate computational power and software. AI offers the potential for major changes in cardiothoracic imaging. Beyond image processing, m...
Humans Neural Networks, Computer Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Radiographic Image Enhancement Machine Learning Tomography, X-Ray Computed Imaging, Three-Dimensional Contrast Media
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[Late gadolinium enhancement and T1 mapping for the diagnosis of cardiac amyloidosis].

Zhonghua wei zhong bing ji jiu yi xue Dec 1, 2019
OBJECTIVE: To explore the role of late gadolinium enhancement (LGE) and T1 mapping for detection of cardiac amyloidosis.
Heart Humans Amyloidosis Contrast Media Gadolinium Predictive Value of Tests Myocardium
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Classification of Aortic Dissection and Rupture on Post-contrast CT Images Using a Convolutional Neural Network.

Journal of digital imaging Dec 1, 2019
Aortic dissections and ruptures are life-threatening injuries that must be immediately treated. Our national radiology practice receives dozens of these cases each month, but no automated process is currently available to check for critical pathologi...
Contrast Media Radiographic Image Interpretation, Computer-Assisted Aortic Dissection Aorta Radiographic Image Enhancement Humans Aortic Rupture Neural Networks, Computer Prospective Studies Reproducibility of Results Tomography, X-Ray Computed Sensitivity and Specificity Female Male Middle Aged
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Application of Artificial Intelligence-based Image Optimization for Computed Tomography Angiography of the Aorta With Low Tube Voltage and Reduced Contrast Medium Volume.

Journal of thoracic imaging Nov 1, 2019
PURPOSE: The purpose of this study was to evaluate the impact of artificial intelligence (AI)-based noise reduction algorithm on aorta computed tomography angiography (CTA) image quality (IQ) at 80 kVp tube voltage and 40 mL contrast medium (CM).
Male Middle Aged Adolescent Contrast Media Female Aged Aged, 80 and over Adult Triiodobenzoic Acids Humans Computed Tomography Angiography Artificial Intelligence Radiographic Image Interpretation, Computer-Assisted Aortic Diseases Prospective Studies Aorta, Thoracic Image Enhancement Signal-To-Noise Ratio
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Deep convolutional neural network for reduction of contrast-enhanced region on CT images.

Journal of radiation research Oct 23, 2019
This study aims to produce non-contrast computed tomography (CT) images using a deep convolutional neural network (CNN) for imaging. Twenty-nine patients were selected. CT images were acquired without and with a contrast enhancement medium. The trans...
Neural Networks, Computer Tomography, X-Ray Computed Humans Time Factors Dose-Response Relationship, Radiation Contrast Media
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