Improvement of Motion Artifacts in Brain MRI Using Deep Learning by Simulation Training Data-Reference-Cited by-同舟云学术

Improvement of Motion Artifacts in Brain MRI Using Deep Learning by Simulation Training Data

Published:2022-01-20 Issue:1 Volume:78 Page:13-22
ISSN:0369-4305
Container-title:Japanese Journal of Radiological Technology
language:en
Short-container-title:Nippon Hoshasen Gijutsu Gakkai Zasshi

Author:

Muro Isao¹,Shimizu Syuntaro¹,Tsukamoto Hikari¹

Affiliation:

1. Division of Radiology, Department of Clinical Technology, Tokai University Hospital

Publisher

Japanese Society of Radiological Technology

Subject

General Medicine

Link

https://www.jstage.jst.go.jp/article/jjrt/78/1/78_780108/_pdf

Reference11 articles.

1. 1） Isogawa K, Ida T, Shiodera T, et al. Noise level adaptive deep convolutional neural network for image denoising. Proceedings of the 26th Annual Meeting of ISMRM, Paris 2018; 2797.

2. 2） Kidoh M, Shinoda K, Kitajima M, et al. Deep learning based noise reduction for brain MR imaging: tests on phantoms and healthy volunteers. Magn Reson Med Sci 2020; 19(3): 195–206.

3. 3） Liu J, Kocak M, Supanich M, et al. Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB). Magn Reson Imaging 2020; 71: 69–79.

4. 4） Muro I, Saito T, Shimizu S. Reduction of motion artifacts in brain MRI images by deep learning using simulation training data. RSNA 2020: 106th Annual Meeting; November 29-December 4, 2020; Chicago.

5. 5）塚本ひかり，室伊三男．頭部MRI領域における深層学習のためのモーションアーチファクトジェネレータの開発　日放技学誌2021; 77(5): 463–470.

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