AIMC Topic: Contrast Media
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Differentiation of breast lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using deep transfer learning based on DenseNet201.
Medicine
Nov 11, 2022
In order to achieve better performance, artificial intelligence is used in breast cancer diagnosis. In this study, we evaluated the efficacy of different fine-tuning strategies of deep transfer learning (DTL) based on the DenseNet201 model to differe...
SVM Communications: Contrast media shortage and Society updates.
Vascular medicine (London, England)
Oct 1, 2022
Signal-Intensity Informed Multi-Coil MRI Encoding Operator for Improved Physics-Guided Deep Learning Reconstruction of Dynamic Contrast-Enhanced MRI.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2022
Dynamic contrast enhanced (DCE) MRI acquires a series of images following the administration of a contrast agent, and plays an important clinical role in diagnosing various diseases. DCE MRI typically necessitates rapid imaging to provide sufficient ...
[Clinical Application of "Three-Low" Technique Combined with Artificial Intelligence Iterative Reconstruction Algorithm in Aortic CT Angiography].
Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
Jul 1, 2022
OBJECTIVE: To explore the application value of the "three-low" technique (low radiation dose, low contrast agent dosage and low contrast agent flow rate) combined with artificial intelligence iterative reconstruction (AIIR) in aortic CT angiography (...
Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models.
Frontiers in bioscience (Landmark edition)
Mar 16, 2022
BACKGROUND: Dynamic contrast-enhanced (DCE) MRI is widely used to assess vascular perfusion and permeability in cancer. In small animal applications, conventional modeling of pharmacokinetic (PK) parameters from DCE MRI images is complex and time con...
Analysis of a Deep Learning-Based Superresolution Algorithm Tailored to Partial Fourier Gradient Echo Sequences of the Abdomen at 1.5 T: Reduction of Breath-Hold Time and Improvement of Image Quality.
Investigative radiology
Mar 1, 2022
OBJECTIVES: The aim of this study was to investigate the feasibility and impact of a novel deep learning superresolution algorithm tailored to partial Fourier allowing retrospectively theoretical acquisition time reduction in 1.5 T T1-weighted gradie...
Can Deep Learning Replace Gadolinium in Neuro-Oncology?: A Reader Study.
Investigative radiology
Feb 1, 2022
MATERIALS AND METHODS: This monocentric retrospective study leveraged 200 multiparametric brain MRIs acquired between November 2019 and February 2020 at Gustave Roussy Cancer Campus (Villejuif, France). A total of 145 patients were included: 107 form...
Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging.
World journal of gastroenterology
Sep 21, 2021
BACKGROUND: The nature of input data is an essential factor when training neural networks. Research concerning magnetic resonance imaging (MRI)-based diagnosis of liver tumors using deep learning has been rapidly advancing. Still, evidence to support...
[Early Assessment of Myocardial Fibrosis of Hypertrophic Cardiomyopathy with Native-T1-Mapping-Based Deep Learning: A Preliminary Study].
Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
Sep 1, 2021
OBJECTIVE: To explore the diagnostic performance of deep learning (DL) model in early detection of the interstitial myocardial fibrosis using native T1 maps of hypertrophic cardiomyopathy (HCM) without late gadolinium enhancement (LGE).
Deep Learning-Based Superresolution Reconstruction for Upper Abdominal Magnetic Resonance Imaging: An Analysis of Image Quality, Diagnostic Confidence, and Lesion Conspicuity.
Investigative radiology
Aug 1, 2021
OBJECTIVES: The aim of this study was to investigate the impact of a deep learning-based superresolution reconstruction technique for T1-weighted volume-interpolated breath-hold examination (VIBESR) on image quality in comparison with standard VIBE i...
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