Presentation 2021-03-15
Real valued CNN based MR image reconstruction Robust to spatial phase variation
Shohei Ouchi, Satoshi Ito,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) MR images have spatial phase distribution due to the inhomogeneities of static magnetic field strength and the difference of magnetic susceptibility. Therefore, it is required to consider the spatial phase variation in the image reconstruction of compressed sensing, however, few studies have been done so far. In this study, we proposed a novel CNN image reconstruction method in which complex images can be reconstructed using real-valued CNN without the estimation of spatial phase variation. Reconstruction experiments showed that image quality was improved in proposed method with higher PSNR and SSIM compared to other reconstruction methods.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) compressed sensing / CNN / phase variation
Paper # MI2020-57
Date of Issue 2021-03-08 (MI)

Conference Information
Committee MI
Conference Date 2021/3/15(3days)
Place (in Japanese) (See Japanese page)
Place (in English) Online
Topics (in Japanese) (See Japanese page)
Topics (in English) Medical Imaging
Chair Yoshiki Kawata(Tokushima Univ.)
Vice Chair Takayuki Kitasaka(Aichi Inst. of Tech.) / Hidekata Hontani(Nagoya Inst. of Tech.)
Secretary Takayuki Kitasaka(Yamaguchi Univ.) / Hidekata Hontani(Univ. of Hyogo)
Assistant Hotaka Takizawa(Tsukuba Univ.) / Yoshito Otake(NAIST)

Paper Information
Registration To Technical Committee on Medical Imaging
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Real valued CNN based MR image reconstruction Robust to spatial phase variation
Sub Title (in English)
Keyword(1) compressed sensing
Keyword(2) CNN
Keyword(3) phase variation
1st Author's Name Shohei Ouchi
1st Author's Affiliation Utsunomiya University(Utsunomiya Univ.)
2nd Author's Name Satoshi Ito
2nd Author's Affiliation Utsunomiya University(Utsunomiya Univ.)
Date 2021-03-15
Paper # MI2020-57
Volume (vol) vol.120
Number (no) MI-431
Page pp.pp.46-50(MI),
#Pages 5
Date of Issue 2021-03-08 (MI)