Evidence map›Paper›PMID 41685117›Full record

ArticlePhotoacoustics2026

Ultrasound-guided sound speed correction for photoacoustic computed tomography.

Xuanhao Zhang, Zheng Qu, Bin Ouyang, Lidai Wang

Abstract read
In one paragraph

Article in Photoacoustics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Xuanhao ZhangDepartment of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong Special Administrative Region of China.
Zheng QuDepartment of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong Special Administrative Region of China.
Bin OuyangDepartment of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong Special Administrative Region of China.
Lidai WangDepartment of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong Special Administrative Region of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Photoacoustic computed tomography (PACT) reconstructs high-resolution images of various chromophores in deep biological tissue. A key to high-quality reconstruction is accurate compensation for the spatially heterogeneous speed of sound (SoS) in tissue. Existing computational methods often estimate or compensate SoS by tuning it directly in the image domain, for example by optimizing sharpness or contrast of reconstructed PA images. However, because the PA signal-to-noise ratio (SNR) decays rapidly with depth due to optical attenuation, such image-domain cues become less informative in deeper regions, limiting SoS accuracy there. Here, we present a dual-modal deep learning framework to correct the heterogeneous SoS via joint processing co-registered PA and ultrasound (US) images. We estimate the spatially varying SoS map from the US image and then fuse the SoS map with the PA image to compute a reduced-aberration photoacoustic image. This method takes advantages of the rich speckle and high SNR in the co-registered US image - and thus can compensate for SoS with high accuracy and efficiency. We tested this method on numerical and tissue-mimicking phantoms, demonstrating cross-domain generalization. In-vivo results demonstrate that incorporation of the predicted SoS maps significantly improved PA image quality, enhancing structural detail and reducing acoustic artifacts. Via fusing the US and PA images, our method produces high-contrast PA images with significantly reduced SoS distortion and artifacts.

Indexed as

Aberration correctionDeep learningPhotoacoustic computed tomographySpeed of sound image reconstruction

Identifiers

PMID41685117
PMCPMC12890839

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Registered trials

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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.