A two-step automated quality assessment for liver MR images based on convolutional neural network-Reference-Cited by-同舟云学术

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A two-step automated quality assessment for liver MR images based on convolutional neural network

Published:2020-03 Issue: Volume:124 Page:108822
ISSN:0720-048X
Container-title:European Journal of Radiology
language:en
Short-container-title:European Journal of Radiology

Author:

Wang Yida,Song Yang,Wang Fang,Sun Jingjing,Gao Xinyi,Han Zhe,Shi Lei,Shao Guoliang,Fan Mingxia,Yang Guang

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Reference22 articles.

1. Quantitative and qualitative comparison of 1.5 and 3.0 tesla MRI in patients with chronic liver diseases;Ramalho;J. Magn. Reson. Imaging,2009

2. MRI of the Liver: A Practical Guide;P.J.A.R,2016

3. Automated reference ‑ free detection of motion artifacts in magnetic resonance images;Küstner;Magn. Reson. Mater. Phys. Biol. Med.,2018

4. Review of medical image quality assessment;Chow;Biomed. Signal Process. Control,2016

5. Measurement of signal-to-Noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters;Olaf Dietrich;J. Magn. Reson. Imaging,2007

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1. Deep Learning Approaches for Automatic Quality Assurance of Magnetic Resonance Images Using ACR Phantom;BMC Medical Imaging;2023-11-29

2. Chest X-Ray Quality Assessment Method With Medical Domain Knowledge Fusion;IEEE Access;2023

3. Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations;Engineering Applications of Artificial Intelligence;2023-01

4. A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images;Journal of Imaging;2022-06-04

5. Emerging artificial intelligence applications in liver magnetic resonance imaging;World Journal of Gastroenterology;2021-10-28

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