Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience

2020 ◽  
Vol 215 (1) ◽  
pp. 50-57 ◽  
Author(s):  
Corey T. Jensen ◽  
Xinming Liu ◽  
Eric P. Tamm ◽  
Adam G. Chandler ◽  
Jia Sun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Initial Experience ◽  
Abdominal Ct

scholarly journals Explicit-implicit dual stream network for image quality assessment

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Guangyi Yang ◽  
Xingyu Ding ◽  
Tian Huang ◽  
Kun Cheng ◽  
Weizheng Jin
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Frequency Domain ◽  
Image Quality Assessment ◽  
Feature Fusion ◽  
Learning Model ◽  
Stream Network ◽  
Perception System ◽  
Deep Learning Model

Abstract Communications industry has remarkably changed with the development of fifth-generation cellular networks. Image, as an indispensable component of communication, has attracted wide attention. Thus, finding a suitable approach to assess image quality is important. Therefore, we propose a deep learning model for image quality assessment (IQA) based on explicit-implicit dual stream network. We use frequency domain features of kurtosis based on wavelet transform to represent explicit features and spatial features extracted by convolutional neural network (CNN) to represent implicit features. Thus, we constructed an explicit-implicit (EI) parallel deep learning model, namely, EI-IQA model. The EI-IQA model is based on the VGGNet that extracts the spatial domain features. On this basis, the number of network layers of VGGNet is reduced by adding the parallel wavelet kurtosis value frequency domain features. Thus, the training parameters and the sample requirements decline. We verified, by cross-validation of different databases, that the wavelet kurtosis feature fusion method based on deep learning has a more complete feature extraction effect and a better generalisation ability. Thus, the method can simulate the human visual perception system better, and subjective feelings become closer to the human eye. The source code about the proposed EI-IQA model is available on github https://github.com/jacob6/EI-IQA.

Fusing deep learning and statistical visual features for no-reference image quality assessment

2020 ◽  
Vol 29 (04) ◽  
pp. 1
Author(s):  
Yin Zhang ◽  
Junhua Yan ◽  
Xuan Du ◽  
Xuehan Bai ◽  
Xiyang Zhi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Visual Features ◽  
Reference Image

SU-F-207-02: Use of Postmortem Subjects for Subjective Image Quality Assessment in Abdominal CT Protocols with Iterative Reconstruction

2015 ◽  
Vol 42 (6Part26) ◽  
pp. 3542-3542
Author(s):  
A Mench ◽  
I Lipnharski ◽  
C Carranza ◽  
L Sinclair ◽  
R Lamoureux ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Iterative Reconstruction ◽  
Image Quality Assessment ◽  
Abdominal Ct ◽  
Subjective Image Quality
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