Cross-sectional imaging of speed-of-sound distribution using photoacoustic reversal beacons.

Yang Wang, Danni Wang, Liting Zhong, Yi Zhou, Qing Wang, Wufan Chen, Li Qi

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Article in Photoacoustics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Yang WangSchool of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.

Danni WangSchool of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.

Liting ZhongSchool of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.

Yi ZhouSchool of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.

Qing WangSchool of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.

Wufan ChenSchool of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.

Li QiSchool of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Photoacoustic tomography (PAT) enables non-invasive cross-sectional imaging of biological tissues, but it fails to map the spatial variation of speed-of-sound (SOS) within tissues. While SOS is intimately linked to density and elastic modulus of tissues, the imaging of SOS distribution serves as a complementary imaging modality to PAT. Moreover, an accurate SOS map can be leveraged to correct for PAT image degradation arising from acoustic heterogeneities. Herein, we propose a method for SOS imaging using scanned photoacoustic beacons excited by short laser pulse with inversion reconstruction. Our method is based on photoacoustic reversal beacons (PRBs), which are small light-absorbing targets with strong photoacoustic contrast. We excite and scan a number of PRBs positioned at the periphery of the target, and the generated photoacoustic waves propagate through the target from various directions, thereby achieve spatial sampling of the internal SOS. By picking up the PRB signal using a graph-based dynamic programing algorithm, we formulate a linear inverse model for pixel-wise SOS reconstruction and solve it with iterative optimization technique. We validate the feasibility of the proposed method through simulations, phantoms, and

Indexed as

Image reconstructionPhotoacoustic reversal beaconPhotoacoustic tomographySpeed of sound distribution

Identifiers

PMID39850092

DOI10.1016/j.pacs.2024.100666

PMCPMC11754137

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.