Thin-slice 2D MR Imaging of the Shoulder Joint Using Denoising Deep Learning Reconstruction Provides Higher Image Quality Than 3D MR Imaging-Reference-Cited by-同舟云学术

Thin-slice 2D MR Imaging of the Shoulder Joint Using Denoising Deep Learning Reconstruction Provides Higher Image Quality Than 3D MR Imaging

Published:2024 Issue: Volume: Page:
ISSN:1347-3182
Container-title:Magnetic Resonance in Medical Sciences
language:en
Short-container-title:MRMS

Author:

Kakigi Takahide¹,Sakamoto Ryo¹²,Arai Ryuzo³,Yamamoto Akira¹⁴,Kuriyama Shinichi⁵,Sano Yuichiro⁶,Imai Rimika⁶,Numamoto Hitomi⁷⁸,Miyake Kanae Kawai¹⁸,Saga Tsuneo¹⁸,Matsuda Shuichi⁵,Nakamoto Yuji¹

Affiliation:

1. Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan

2. Department of Real World Data Research and Development, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan

3. Department of Orthopaedic Surgery, Kyoto Katsura Hospital, Kyoto, Kyoto, Japan

4. Center for Medical Education, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan

5. Department of Orthopaedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan

6. MRI Systems Division, Canon Medical Systems Corporation, Otawara, Tochigi, Japan

7. Division of Clinical Radiology Service, Kyoto University Hospital, Kyoto, Kyoto, Japan

8. Department of Advanced Medical Imaging Research, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan

Publisher

Japanese Society for Magnetic Resonance in Medicine

Link

https://www.jstage.jst.go.jp/article/mrms/advpub/0/advpub_mp.2023-0115/_pdf

Reference31 articles.

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4. 4. Kakigi T, Sakamoto R, Tagawa H, et al. Diagnostic advantage of thin slice 2D MRI and multiplanar reconstruction of the knee joint using deep learning based denoising approach. Sci Rep 2022; 12:10362.

5. 5. Obama Y, Ohno Y, Yamamoto K, et al. MR imaging for shoulder diseases: Effect of compressed sensing and deep learning reconstruction on examination time and imaging quality compared with that of parallel imaging. Magn Reson Imaging 2022; 94:56–63.