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

Number of Access:9（2025-11-25 02:17 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/102922

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Title	Deep-Learning Generation of High-Resolution Images of Live Cells in Culture Using Tri-Frequency Acoustic Images
Creator	CreatorName Fujiwara, Natsumi
	CreatorName Uno, Midori
	ORCID 0009-0002-0738-285X 0009-0002-0738-285X CreatorName Fukuda, Hiroki
	NRID 1000070751113 1000070751113 ORCID 0000-0001-7494-5099 0000-0001-7494-5099 researchmap permalink nagakubo nagakubo Scopus Author ID 55329400300 55329400300 CreatorName Nagakubo, Akira
	CreatorName Tan, Shao Ying
	NRID 1000040234314 1000040234314 ORCID 0000-0002-4912-5811 0000-0002-4912-5811 researchmap permalink read0013779 read0013779 Scopus Author ID 57790526500 57790526500 CreatorName Kino-Oka, Masahiro
	NRID 1000090252626 1000090252626 ORCID 0000-0002-9602-5194 0000-0002-9602-5194 researchmap permalink read0042771 read0042771 Scopus Author ID 35563683500 35563683500 CreatorName Ogi, Hirotsugu
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.
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.
Publisher	American Physical Society
Source Title	Physical Review X
Volume (Issue)	15(2)
Page	021015
Date of Issued	2025-04-15
Language	English
Handle URL	https://hdl.handle.net/11094/102922
eISSN	21603308
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1103/PhysRevX.15.021015
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version	Version of Record
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCMI Type	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	English
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	English
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

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