AIMC Topic: Contrast Media
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Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Sep 1, 2023
PURPOSE: Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has been proposed as useful for improving temporal resolution and image quality on MR sequences in differ...
Lung
Deep Learning
Imaging, Three-Dimensional
Image Processing, Computer-Assisted
Adult
Male
Middle Aged
Humans
Magnetic Resonance Imaging
Image Interpretation, Computer-Assisted
Aged
Signal-To-Noise Ratio
Aged, 80 and over
Multidetector Computed Tomography
Lung Neoplasms
Carcinoma, Non-Small-Cell Lung
Contrast Media
Retrospective Studies
Female
Deep-learning prostate cancer detection and segmentation on biparametric versus multiparametric magnetic resonance imaging: Added value of dynamic contrast-enhanced imaging.
International journal of urology : official journal of the Japanese Urological Association
Aug 22, 2023
OBJECTIVES: To develop diagnostic algorithms of multisequence prostate magnetic resonance imaging for cancer detection and segmentation using deep learning and explore values of dynamic contrast-enhanced imaging in multiparametric imaging, compared w...
3D Breast Cancer Segmentation in DCE-MRI Using Deep Learning With Weak Annotation.
Journal of magnetic resonance imaging : JMRI
Aug 19, 2023
BACKGROUND: Deep learning models require large-scale training to perform confidently, but obtaining annotated datasets in medical imaging is challenging. Weak annotation has emerged as a way to save time and effort.
Editorial for "3D Breast Cancer Segmentation in DCE-MRI Using Deep Learning With Weak Annotation".
Journal of magnetic resonance imaging : JMRI
Aug 14, 2023
Deep learning-assisted identification and quantification of aneurysmal subarachnoid hemorrhage in non-contrast CT scans: Development and external validation of Hybrid 2D/3D UNet.
NeuroImage
Aug 11, 2023
Accurate stroke assessment and consequent favorable clinical outcomes rely on the early identification and quantification of aneurysmal subarachnoid hemorrhage (aSAH) in non-contrast computed tomography (NCCT) images. However, hemorrhagic lesions can...
The Feasibility of Using a Deep Learning-Based Model to Determine Cardiac Computed Tomographic Contrast Dose.
Journal of computer assisted tomography
Aug 3, 2023
PURPOSE: This study aimed to predict contrast effects in cardiac computed tomography (CT) from CT localizer radiographs using a deep learning (DL) model and to compare the prediction performance of the DL model with that of conventional models based ...
Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data.
Investigative radiology
Jul 28, 2023
OBJECTIVES: Artificial intelligence (AI) methods can be applied to enhance contrast in diagnostic images beyond that attainable with the standard doses of contrast agents (CAs) normally used in the clinic, thus potentially increasing diagnostic power...
Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function.
NeuroImage
Jul 26, 2023
PURPOSE: In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning...
Automatic Myocardial Contrast Echocardiography Image Quality Assessment Using Deep Learning: Impact on Myocardial Perfusion Evaluation.
Ultrasound in medicine & biology
Jul 24, 2023
OBJECTIVE: The image quality of myocardial contrast echocardiography (MCE) is critical for precise myocardial perfusion evaluation but challenging for echocardiographers. Differences in quality may lead to diagnostic heterogeneity. This study was aim...
Machine Learning Classification of Body Part, Imaging Axis, and Intravenous Contrast Enhancement on CT Imaging.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Jul 13, 2023
The development and evaluation of machine learning models that automatically identify the body part(s) imaged, axis of imaging, and the presence of intravenous contrast material of a CT series of images. This retrospective study included 6955 serie...
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