Deep-Learning Generation of High-Resolution Images of Live Cells in Culture Using Tri-Frequency Acoustic Images

Fujiwara, Natsumi; Uno, Midori; Fukuda, Hiroki; Nagakubo, Akira; Tan, Shao Ying; Kino-Oka, Masahiro; Ogi, Hirotsugu

doi:https://doi.org/10.1103/PhysRevX.15.021015

Deep-Learning Generation of High-Resolution Images of Live Cells in Culture Using Tri-Frequency Acoustic Images

Fujiwara, Natsumi; Uno, Midori; Fukuda, Hiroki et al.

Physical Review X, 2025, 15(2), 021015

アクセス数:9件（2025-11-25 01:06 集計）

固定URL: https://hdl.handle.net/11094/102922

閲覧可能ファイル

ファイル	フォーマット	利用条件	サイズ	閲覧回数	利用開始日	説明	information
PhysRevX_15_2	pdf	なし	2.12 MB	14	2025.10.31

Creative Commons : 表示

論文情報

ファイル出力 EndNote Basic出力 Mendeley出力

タイトル	Deep-Learning Generation of High-Resolution Images of Live Cells in Culture Using Tri-Frequency Acoustic Images
著者	著者名 Fujiwara, Natsumi
	著者名 Uno, Midori
	ORCID 0009-0002-0738-285X 0009-0002-0738-285X 著者名 Fukuda, Hiroki
	NRID 1000070751113 1000070751113 ORCID 0000-0001-7494-5099 0000-0001-7494-5099 researchmap permalink nagakubo nagakubo Scopus Author ID 55329400300 55329400300 著者名 Nagakubo, Akira
	著者名 Tan, Shao Ying
	NRID 1000040234314 1000040234314 ORCID 0000-0002-4912-5811 0000-0002-4912-5811 researchmap permalink read0013779 read0013779 Scopus Author ID 57790526500 57790526500 著者名 Kino-Oka, Masahiro
	NRID 1000090252626 1000090252626 ORCID 0000-0002-9602-5194 0000-0002-9602-5194 researchmap permalink read0042771 read0042771 Scopus Author ID 35563683500 35563683500 著者名 Ogi, Hirotsugu
内容	Fujiwara N., Uno M., Fukuda H., et al. Deep-Learning Generation of High-Resolution Images of Live Cells in Culture Using Tri-Frequency Acoustic Images. Physical Review X 15, 021015 (2025); https://doi.org/10.1103/PhysRevX.15.021015.
抄録	Ultrasound microscopy is the only technique that has the ability to monitor live-cell morphology over a long period of time without causing any damage to the cells, but its longer wavelength prevents one from obtaining high-resolution cell images. Here, we propose a deep-learning (DL) method for generating high-resolution acoustic images. By preparing datasets consisting of many pairs of acoustic and optical-microscope images for the same cells and training them, a high-resolution image comparable to optical microscopy is generated from an acoustic image. Importantly, the most accurate images are generated when three-layer (RGB) images containing not only high-frequency (approximately 180 MHz) images but also lower-frequency (approximately 100 MHz) images are used as the input images, which is attributed to enhanced acoustic absorption in the nucleus because the nucleus resonates in this low-frequency band. The DL scheme with the tri-frequency image input is applied to human mesenchymal stem cells and human induced pluripotent stem cells, and the high image-generation capability is demonstrated. As a result, high-resolution acoustic microscopy images are obtained for the same cells for over 24 h, without the typical cell damage encountered using optical imaging.
出版者	American Physical Society
収録物名	Physical Review X
巻(号)	15(2)
ページ	021015
刊行年月	2025-04-15
言語	英語
ハンドルURL	https://hdl.handle.net/11094/102922
eISSN	21603308
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1103/PhysRevX.15.021015
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	Physical Review X. 2025, 15(2), p. 021015
DC.title	Deep-Learning Generation of High-Resolution Images of Live Cells in Culture Using Tri-Frequency Acoustic Images
DC.creator	Fujiwara, Natsumi
	Uno, Midori
	Fukuda, Hiroki
	Nagakubo, Akira
	Tan, Shao Ying
	Kino-Oka, Masahiro
	Ogi, Hirotsugu
DC.publisher	American Physical Society
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-04-15
DC.identifier	https://doi.org/10.1103/PhysRevX.15.021015
DC.description	Fujiwara N., Uno M., Fukuda H., et al. Deep-Learning Generation of High-Resolution Images of Live Cells in Culture Using Tri-Frequency Acoustic Images. Physical Review X 15, 021015 (2025); https://doi.org/10.1103/PhysRevX.15.021015.
DCTERMS.abstract	Ultrasound microscopy is the only technique that has the ability to monitor live-cell morphology over a long period of time without causing any damage to the cells, but its longer wavelength prevents one from obtaining high-resolution cell images. Here, we propose a deep-learning (DL) method for generating high-resolution acoustic images. By preparing datasets consisting of many pairs of acoustic and optical-microscope images for the same cells and training them, a high-resolution image comparable to optical microscopy is generated from an acoustic image. Importantly, the most accurate images are generated when three-layer (RGB) images containing not only high-frequency (approximately 180 MHz) images but also lower-frequency (approximately 100 MHz) images are used as the input images, which is attributed to enhanced acoustic absorption in the nucleus because the nucleus resonates in this low-frequency band. The DL scheme with the tri-frequency image input is applied to human mesenchymal stem cells and human induced pluripotent stem cells, and the high image-generation capability is demonstrated. As a result, high-resolution acoustic microscopy images are obtained for the same cells for over 24 h, without the typical cell damage encountered using optical imaging.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	102922
citation_title	Deep-Learning Generation of High-Resolution Images of Live Cells in Culture Using Tri-Frequency Acoustic Images
citation_author	Fujiwara, Natsumi
	Uno, Midori
	Fukuda, Hiroki
	Nagakubo, Akira
	Tan, Shao Ying
	Kino-Oka, Masahiro
	Ogi, Hirotsugu
citation_publisher	American Physical Society
citation_language	英語
citation_date	2025-04-15
citation_journal_title	Physical Review X
citation_volume	15
citation_issue	2
citation_firstpage	021015
citation_doi	https://doi.org/10.1103/PhysRevX.15.021015
citation_public_url	https://hdl.handle.net/11094/102922

Deep-Learning Generation of High-Resolution Images of Live Cells in Culture Using Tri-Frequency Acoustic Images

閲覧可能ファイル

論文情報

論文詳細を表示