Sparsity-Aware OCT Volumetric Data Restoration Using Optical Synthesis Model

Kobayashi, Ruiki; Fujii, Genki; Yoshida, Yuta; Ota, Takeru; Nin, Fumiaki; Hibino, Hiroshi; Choi, Samuel; Ono, Shunsuke; Muramatsu, Shogo

doi:https://doi.org/10.1109/TCI.2022.3183396

Sparsity-Aware OCT Volumetric Data Restoration Using Optical Synthesis Model

Kobayashi, Ruiki; Fujii, Genki; Yoshida, Yuta et al.

IEEE Transactions on Computational Imaging, 2022, 8, 505-520

Number of Access:1,074（2025-08-15 20:53 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/93363

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Title	Sparsity-Aware OCT Volumetric Data Restoration Using Optical Synthesis Model
Creator	CreatorName Kobayashi, Ruiki
	CreatorName Fujii, Genki
	CreatorName Yoshida, Yuta
	NRID 1000030790571 1000030790571 ORCID 0000-0003-0011-8925 0000-0003-0011-8925 researchmap permalink otatakeru otatakeru Scopus Author ID 26642091900 26642091900 CreatorName Ota, Takeru
	NRID 1000080644905 1000080644905 CreatorName Nin, Fumiaki
	NRID 1000070314317 1000070314317 researchmap permalink hiroshi_hibino hiroshi_hibino Scopus Author ID 7007020846 7007020846 CreatorName Hibino, Hiroshi
	NRID 1000060568418 1000060568418 CreatorName Choi, Samuel
	NRID 1000060752269 1000060752269 CreatorName Ono, Shunsuke
	NRID 1000030295472 1000030295472 CreatorName Muramatsu, Shogo
Subject	Image denoising
	image restoration
	inverse problems
	optical coherence tomography
	optimization
	system identification
Description	Kobayashi R., Fujii G., Yoshida Y., et al. Sparsity-Aware OCT Volumetric Data Restoration Using Optical Synthesis Model. IEEE Transactions on Computational Imaging 8, 505 (2022); https://doi.org/10.1109/TCI.2022.3183396.
Abstract	In this study, a novel restoration model for the data of optical coherence tomography (OCT) is proposed. An OCT device acquires a tomographic image of a specimen at the scale of a few micrometers using a near-infrared laser and has been frequently adopted to measure the structures of bio-tissues. In certain applications, OCT devices face the problem of extremely weak reflected light and require the help of image processing to estimate the distribution of reflected light hidden in various noises. OCT identifies tomographic structures by searching for peak interference locations and their intensities. Therefore, the challenge of OCT data restoration involves the problem of identifying the interference function and its deconvolution. In this study, a restoration method is given by reducing the problem to a regularized least-squares problem with a hard constraint for the latent refractive index distributions, and an algorithm is derived using a primal-dual splitting (PDS) framework. The PDS has the advantage of requiring no inverse matrix operation and is able to handle high-dimensional data. The significance of the proposed method is verified through simulations using artificial data, followed by an experiment conducted using actual observation of 64 × 64 × 5000 sized voxels.
Publisher	Institute of Electrical and Electronics Engineers Inc.
Source Title	IEEE Transactions on Computational Imaging
Volume (Issue)	8
Page	505-520
Date of Issued	2022-06-16
Language	English
Handle URL	https://hdl.handle.net/11094/93363
PISSN	25730436
eISSN	23339403
Relation.isVersionOf	DOI (Publisher Version) https://doi.org/10.1109/TCI.2022.3183396
Access Rights	open access
CopyRight	© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
oaire:version	Accepted Manuscript
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCMI Type	Text
DCTERMS.bibliographicCitation	IEEE Transactions on Computational Imaging. 2022, 8, p. 505-520
DC.title	Sparsity-Aware OCT Volumetric Data Restoration Using Optical Synthesis Model
DC.creator	Kobayashi, Ruiki
	Fujii, Genki
	Yoshida, Yuta
	Ota, Takeru
	Nin, Fumiaki
	Hibino, Hiroshi
	Choi, Samuel
	Ono, Shunsuke
	Muramatsu, Shogo
DC.publisher	Institute of Electrical and Electronics Engineers Inc.
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2022-06-16
DC.identifier	https://doi.org/10.1109/TCI.2022.3183396
DC.subject	Image denoising
	image restoration
	inverse problems
	optical coherence tomography
	optimization
	system identification
DC.description	Kobayashi R., Fujii G., Yoshida Y., et al. Sparsity-Aware OCT Volumetric Data Restoration Using Optical Synthesis Model. IEEE Transactions on Computational Imaging 8, 505 (2022); https://doi.org/10.1109/TCI.2022.3183396.
DCTERMS.abstract	In this study, a novel restoration model for the data of optical coherence tomography (OCT) is proposed. An OCT device acquires a tomographic image of a specimen at the scale of a few micrometers using a near-infrared laser and has been frequently adopted to measure the structures of bio-tissues. In certain applications, OCT devices face the problem of extremely weak reflected light and require the help of image processing to estimate the distribution of reflected light hidden in various noises. OCT identifies tomographic structures by searching for peak interference locations and their intensities. Therefore, the challenge of OCT data restoration involves the problem of identifying the interference function and its deconvolution. In this study, a restoration method is given by reducing the problem to a regularized least-squares problem with a hard constraint for the latent refractive index distributions, and an algorithm is derived using a primal-dual splitting (PDS) framework. The PDS has the advantage of requiring no inverse matrix operation and is able to handle high-dimensional data. The significance of the proposed method is verified through simulations using artificial data, followed by an experiment conducted using actual observation of 64 × 64 × 5000 sized voxels.
DC.rights	© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DC.format	93363
citation_title	Sparsity-Aware OCT Volumetric Data Restoration Using Optical Synthesis Model
citation_author	Kobayashi, Ruiki
	Fujii, Genki
	Yoshida, Yuta
	Ota, Takeru
	Nin, Fumiaki
	Hibino, Hiroshi
	Choi, Samuel
	Ono, Shunsuke
	Muramatsu, Shogo
citation_publisher	Institute of Electrical and Electronics Engineers Inc.
citation_language	English
citation_date	2022-06-16
citation_journal_title	IEEE Transactions on Computational Imaging
citation_volume	8
citation_firstpage	505
citation_lastpage	520
citation_doi	https://doi.org/10.1109/TCI.2022.3183396
citation_public_url	https://hdl.handle.net/11094/93363

Sparsity-Aware OCT Volumetric Data Restoration Using Optical Synthesis Model

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