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De-noising of contrast-enhanced MRI sequences by an ensemble of expert deep neural networks
Ariel Benou, Ronel Veksler,
Alon Friedman
, Tammy Riklin Raviv
科研成果
:
图书/报告稿件的类型
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会议稿件
22
引用 （Scopus）
综述
指纹
指纹
探究 'De-noising of contrast-enhanced MRI sequences by an ensemble of expert deep neural networks' 的科研主题。它们共同构成独一无二的指纹。
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Keyphrases
Deep Neural Network
100%
Denoising
100%
Contrast-enhanced MRI
100%
Experts Ensemble
100%
Dynamic Contrast-enhanced Magnetic Resonance Imaging (DCE-MRI)
100%
MRI Sequences
100%
MRI Scan
40%
Dynamic Scan
40%
Blood-brain Barrier
20%
Error-prone
20%
Blood-brain Barrier Permeability
20%
Curve Fitting
20%
Inverse Problem
20%
Input Space
20%
Nonlinear Dynamics
20%
Training Set
20%
Contrast Agent
20%
High Variability
20%
Pharmacokinetic Parameters
20%
Anisotropic Noise
20%
Original Image
20%
Image Denoising
20%
Brain Tumor Patients
20%
Dependent Noise
20%
Curve Smoothing
20%
Denoising Method
20%
Non-white
20%
Washout Curve
20%
Smooth Curve
20%
Medical Image Denoising
20%
Noise Statistics
20%
Imaging Protocol
20%
Pharmacokinetic Model
20%
Noise Characteristics
20%
Noise Model
20%
Computer Science
Deep Neural Network
100%
de-noising
100%
Pharmacokinetics
50%
Smoothing Curve
25%
Fitting Curve
25%
Inverse Problem
25%
Quantitative Assessment
25%
image denoising
25%
Neuroscience
Magnetic Resonance Imaging
100%
Neural Network
100%
Blood Brain Barrier
33%
Pharmacokinetics
33%
Brain Tumor
16%
Contrast Medium
16%