AIMC Topic: Contrast Media
Clear Filters Showing 51 to 60 of 546 articles
Accuracy of machine learning models for pre-diagnosis and diagnosis of pancreatic ductal adenocarcinoma in contrast-CT images: a systematic review and meta-analysis.
Abdominal radiology (New York)
Dec 25, 2024
PURPOSE: To evaluate the diagnostic ability and methodological quality of ML models in detecting Pancreatic Ductal Adenocarcinoma (PDAC) in Contrast CT images.
Performance of recurrent neural networks with Monte Carlo dropout for predicting pharmacokinetic parameters from dynamic contrast-enhanced magnetic resonance imaging data.
Journal of applied clinical medical physics
Dec 23, 2024
PURPOSE: To quantitatively evaluate the performance of two types of recurrent neural networks (RNNs), long short-term memory (LSTM) and gated recurrent units (GRU), using Monte Carlo dropout (MCD) to predict pharmacokinetic (PK) parameters from dynam...
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.
Aged
Male
Middle Aged
Deep Learning
Retrospective Studies
Tomography, X-Ray Computed
Female
Signal-To-Noise Ratio
Computed Tomography Angiography
Myocardial Perfusion Imaging
Radiographic Image Enhancement
Iodine
Humans
Contrast Media
Radiographic Image Interpretation, Computer-Assisted
Radiation Dosage
Combination of deep learning reconstruction and quantification for dynamic contrast-enhanced (DCE) MRI.
Magnetic resonance imaging
Dec 20, 2024
Dynamic contrast-enhanced (DCE) MRI is an important imaging tool for evaluating tumor vascularity that can lead to improved characterization of tumor extent and heterogeneity, and for early assessment of treatment response. However, clinical adoption...
Reliability of post-contrast deep learning-based highly accelerated cardiac cine MRI for the assessment of ventricular function.
Magnetic resonance imaging
Dec 19, 2024
OBJECTIVE: The total examination time can be reduced if high-quality two-dimensional (2D) cine images can be collected post-contrast to minimize non-scanning time prior to late gadolinium-enhanced imaging. This study aimed to assess the equivalency o...
Generative Adversarial Network Based Contrast Enhancement: Synthetic Contrast Brain Magnetic Resonance Imaging.
Academic radiology
Dec 18, 2024
RATIONALE AND OBJECTIVES: Magnetic resonance imaging (MRI) is a vital tool for diagnosing neurological disorders, frequently utilising gadolinium-based contrast agents (GBCAs) to enhance resolution and specificity. However, GBCAs present certain risk...
Detection of late gadolinium enhancement in patients with hypertrophic cardiomyopathy using machine learning.
International journal of cardiology
Dec 18, 2024
BACKGROUND: Late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) in hypertrophic cardiomyopathy (HCM) typically represents myocardial fibrosis and may lead to fatal ventricular arrhythmias. However, CMR is resource-intensive and some...
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...
Accurate prediction of pollution and health risks of iodinated X-ray contrast media in Taihu Lake with machine learning and revealing key environmental factors.
Water research
Dec 17, 2024
Iodinated X-ray contrast media (ICM) are commonly detected at considerable concentrations in aquatic environments. The long-term pollution trends in ICM at the whole lake/river scale have not yet been investigated; therefore, the risks associated wit...
Predicting molecular subtypes of breast cancer based on multi-parametric MRI dataset using deep learning method.
Magnetic resonance imaging
Dec 14, 2024
PURPOSE: To develop a multi-parametric MRI model for the prediction of molecular subtypes of breast cancer using five types of breast cancer preoperative MRI images.
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823