Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck

Fujima, Noriyuki; Nakagawa, Junichi; Kameda, Hiroyuki; Ikebe, Yohei; Harada, Taisuke; Shimizu, Yukie; Tsushima, Nayuta; Kano, Satoshi; Homma, Akihiro; Kwon, Jihun; Yoneyama, Masami; Kudo, Kohsuke

Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck

Fujima, Noriyuki; Nakagawa, Junichi; Kameda, Hiroyuki et al.

Magnetic Resonance Materials in Physics, Biology and Medicine, 2024, 37(3), 439-447

Permalink : https://hdl.handle.net/2115/95784

このアイテムのアクセス数:198件（2026-07-13 21:27 集計）

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論文情報

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アクセス権	open access
URI	https://hdl.handle.net/2115/95784
タイトル	Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck
著者	Fujima, Noriyuki ; Nakagawa, Junichi ; Kameda, Hiroyuki ; Ikebe, Yohei ; Harada, Taisuke ; Shimizu, Yukie ; Tsushima, Nayuta ; Kano, Satoshi ; Homma, Akihiro ; Kwon, Jihun ; Yoneyama, Masami ; Kudo, Kohsuke
e-Rad_Researcher 80431360 著者名 Fujima, Noriyuki
著者名 Nakagawa, Junichi
著者名 Kameda, Hiroyuki
著者名 Ikebe, Yohei
著者名 Harada, Taisuke
著者名 Shimizu, Yukie
著者名 Tsushima, Nayuta
著者名 Kano, Satoshi
e-Rad_Researcher 30312359 著者名 Homma, Akihiro
著者名 Kwon, Jihun
著者名 Yoneyama, Masami
e-Rad_Researcher 10374232 著者名 Kudo, Kohsuke
言語	英語
キーワード	Deep learning
	Diffusion magnetic resonance imaging
	Neck
	Image reconstruction
発行日	2024-07
出版者	Springer
収録物名	Magnetic Resonance Materials in Physics, Biology and Medicine
巻(号)	37(3)
ページ	439-447
抄録	Objectives To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI). Materials and methods We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle. Results Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (p < 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (p = 0.002 similar to p < 0.001). Discussion DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
権利情報	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10334-023-01129-4
PISSN	1352-8661
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1007/s10334-023-01129-4