AIMC Topic: Contrast Media

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A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI.

Journal of magnetic resonance imaging : JMRI Jul 5, 2023
BACKGROUND: Dynamic contrast-enhanced (DCE) MRI commonly outperforms diffusion-weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE-MRI, particularly in patients with chronic kidney dise...
Breast Neoplasms Deep Learning Contrast Media Retrospective Studies Female Magnetic Resonance Imaging Diffusion Magnetic Resonance Imaging Humans Prospective Studies
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Deep learning-assisted LI-RADS grading and distinguishing hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT: a two-center study.

European radiology Jul 1, 2023
OBJECTIVES: To develop a deep learning (DL) method that can determine the Liver Imaging Reporting and Data System (LI-RADS) grading of high-risk liver lesions and distinguish hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT.
Retrospective Studies Tomography, X-Ray Computed Sensitivity and Specificity Humans Carcinoma, Hepatocellular Contrast Media Magnetic Resonance Imaging Deep Learning Liver Neoplasms
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A unified hybrid transformer for joint MRI sequences super-resolution and missing data imputation.

Physics in medicine and biology Jun 23, 2023
High-resolution multi-modal magnetic resonance imaging (MRI) is crucial in clinical practice for accurate diagnosis and treatment. However, challenges such as budget constraints, potential contrast agent deposition, and image corruption often limit t...
Magnetic Resonance Imaging Humans Neural Networks, Computer Image Processing, Computer-Assisted Contrast Media
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Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study.

European radiology experimental Jun 7, 2023
BACKGROUND: International societies have issued guidelines for high-risk breast cancer (BC) screening, recommending contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast as a supplemental diagnostic tool. In our study, we tested the app...
Deep Learning Contrast Media Magnetic Resonance Imaging Feasibility Studies Prospective Studies Female Breast Neoplasms Humans
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Development of a deep-learning model for classification of LI-RADS major features by using subtraction images of MRI: a preliminary study.

Abdominal radiology (New York) May 24, 2023
PURPOSE: Liver Imaging Reporting and Data System (LI-RADS) is limited by interreader variability. Thus, our study aimed to develop a deep-learning model for classifying LI-RADS major features using subtraction images using magnetic resonance imaging ...
Humans Contrast Media Magnetic Resonance Imaging Deep Learning Retrospective Studies Liver Neoplasms Carcinoma, Hepatocellular Sensitivity and Specificity
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Deep Learning of Time-Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma.

AJNR. American journal of neuroradiology Apr 27, 2023
BACKGROUND AND PURPOSE: An autoencoder can learn representative time-signal intensity patterns to provide tissue heterogeneity measures using dynamic susceptibility contrast MR imaging. The aim of this study was to investigate whether such an autoenc...
Brain Neoplasms Deep Learning Humans Magnetic Resonance Imaging Contrast Media Prognosis Retrospective Studies Glioblastoma
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LAVA HyperSense and deep-learning reconstruction for near-isotropic (3D) enhanced magnetic resonance enterography in patients with Crohn's disease: utility in noise reduction and image quality improvement.

Diagnostic and interventional radiology (Ankara, Turkey) Apr 25, 2023
PURPOSE: This study aimed to compare near-isotropic contrast-enhanced T1-weighted (CE-T1W) magnetic resonance enterography (MRE) images reconstructed with vendor-supplied deep-learning reconstruction (DLR) with those reconstructed conventionally in t...
Contrast Media Radiographic Image Interpretation, Computer-Assisted Crohn Disease Quality Improvement Magnetic Resonance Spectroscopy Humans Retrospective Studies Deep Learning
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A deep learning model based on contrast-enhanced computed tomography for differential diagnosis of gallbladder carcinoma.

Hepatobiliary & pancreatic diseases international : HBPD INT Apr 11, 2023
BACKGROUND: Gallbladder carcinoma (GBC) is highly malignant, and its early diagnosis remains difficult. This study aimed to develop a deep learning model based on contrast-enhanced computed tomography (CT) images to assist radiologists in identifying...
Middle Aged Deep Learning Support Vector Machine Adult Male Radiographic Image Interpretation, Computer-Assisted Gallbladder Neoplasms Cholecystectomy Contrast Media Diagnosis, Differential Predictive Value of Tests Humans Retrospective Studies Female Tomography, X-Ray Computed Reproducibility of Results Aged
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Applications of deep learning to reduce the need for iodinated contrast media for CT imaging: a systematic review.

International journal of computer assisted radiology and surgery Mar 22, 2023
PURPOSE: The usage of iodinated contrast media (ICM) can improve the sensitivity and specificity of computed tomography (CT) for many clinical indications. However, the adverse effects of ICM administration can include renal injury, life-threatening ...
Deep Learning Humans Tomography, X-Ray Computed Contrast Media
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Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study.

European radiology Mar 18, 2023
OBJECTIVE: To investigate the image quality and lesion conspicuity of a deep-learning-based contrast-boosting (DL-CB) algorithm on double-low-dose (DLD) CT of simultaneous reduction of radiation and contrast doses in participants at high-risk for hep...
Double-Blind Method Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Carcinoma, Hepatocellular Contrast Media Algorithms Tomography, X-Ray Computed Prospective Studies Humans Deep Learning Liver Neoplasms
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