Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/116671
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dc.contributorDepartment of Biomedical Engineeringen_US
dc.contributorCollege of Professional and Continuing Educationen_US
dc.contributorJoint Research Centre for Biosensing and Precision Theranosticsen_US
dc.creatorPang, Wen_US
dc.creatorZhou, Qen_US
dc.creatorQiu, Yen_US
dc.creatorHuang, Hen_US
dc.creatorChen, Jen_US
dc.creatorZhong, Ten_US
dc.creatorZhou, Yen_US
dc.creatorNie, Len_US
dc.creatorLai, Pen_US
dc.date.accessioned2026-01-12T05:59:39Z-
dc.date.available2026-01-12T05:59:39Z-
dc.identifier.urihttp://hdl.handle.net/10397/116671-
dc.language.isoenen_US
dc.publisherInstitute of Physics Publishing Ltd.en_US
dc.rights© 2025 The Author(s). Published by IOP Publishing Ltd.en_US
dc.rightsOriginal content from this work may be used under the terms of the Creative Commons Attribution 4.0 license (https://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.en_US
dc.rightsThe following publication Pang, W., Zhou, Q., Qiu, Y., Huang, H., Chen, J., Zhong, T., ... & Lai, P. (2025). Multi-parametric photoacoustic elastomicroscopy: quantitative elasticity mapping and microstructural analysis for early-stage hepatic fibrosis detection. Journal of Physics: Photonics, 7(4), 045038 is available at https://doi.org/10.1088/2515-7647/ae0541.en_US
dc.subjectElastic sensing and measurementen_US
dc.subjectLiver fibrosisen_US
dc.subjectMulti-parametric evaluationen_US
dc.subjectPhotoacoustic elastomicroscopy (PAEM)en_US
dc.subjectTime of flight (ToF)en_US
dc.subjectTissue stiffnessen_US
dc.subjectTumor marginsen_US
dc.titleMulti-parametric photoacoustic elastomicroscopy : quantitative elasticity mapping and microstructural analysis for early-stage hepatic fibrosis detectionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume7en_US
dc.identifier.issue4en_US
dc.identifier.doi10.1088/2515-7647/ae0541en_US
dcterms.abstractEarly detection of hepatic fibrosis remains a critical unmet need due to the limited sensitivity of conventional elastography in capturing microstructural and biomechanical changes. In this study, we developed photoacoustic elastomicroscopy (PAEM), a multi-parametric imaging platform that synergizes high-resolution photoacoustic microscopy with time-of-flight (ToF)-based elastography to quantitatively map tissue stiffness and visualize fibrotic microarchitecture. Validated using PDMS phantoms and a drug-induced murine fibrosis model, PAEM can detect early-stage fibrosis through microstructural biomarkers—pseudo-lobule formation and crevice-area expansion, with a relatively high area under the curve (AUC) > 0.91. However, architectural ambiguity in advanced fibrotic stages gradually reduces PAEM’s diagnostic accuracy, necessitating complementary reliance on ToF-based measurements for auxiliary staging. In our results, ToF-based elasticity biomarkers revealed progressive stiffness increases with a significant velocity increase of 3.7% in 1-week fibrosis. Furthermore, experimental PAEM outperformed shear wave elastography (SWE) in early-stage sensitivity by identifying significant stiffness changes, quantitatively 7-fold greater velocity differential sensitivity than SWE (5.39% vs. 0.77% change), between healthy and 3-week fibrotic liver tissue. All-stage fibrosis exhibited a considerable stiffness rise (AUC > 0.95), correlating strongly with histopathological severity and serum examination. By integrating structural and mechanical biomarkers, PAEM offers a translational tool for early diagnosis, longitudinal monitoring, and staging of hepatic fibrosis, which can potentially be extended for wider applications in tumor margin delineation and other fibrotic pathologies in soft tissue.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of physics : photonics, Oct. 2025, v. 7, no. 4, 045038en_US
dcterms.isPartOfJournal of physics : photonicsen_US
dcterms.issued2025-10-
dc.identifier.scopus2-s2.0-105019533341-
dc.identifier.eissn2515-7647en_US
dc.identifier.artn045038en_US
dc.description.validate202601 bcjzen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_TA-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextWe acknowledge the supporting of the National Natural Science Foundation of China (NSFC) (81930048, 82330061, 82372010), National Key R&D Program of China (2023YFF0715303), Guangdong Science and Technology Commission (2019BT02X105), Hong Kong Research Grant Council (15217721, 15125724, C7074-21GF), Hong Kong Polytechnic University (P0038180, P0039517, P0043485, P0045762, P0049101), College of Professional and Continuing Education Research Fund (SEHS-2023-308(I)), and Faculty Development Scheme (UGC/FDS24/M02/24). During the manuscript revision phase, AI-assisted tools (ChatGPT, DeepSeek) were employed to enhance the linguistic refinement of the text.en_US
dc.description.pubStatusPublisheden_US
dc.description.TAIOP (2025)en_US
dc.description.oaCategoryTAen_US
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