Evidence map›Paper›PMID 40454259›Full record

ArticlePhotoacoustics2025

Iterative optimization algorithm with structural prior for artifacts removal of photoacoustic imaging.

Yu Zhang, Shuang Li, Yibing Wang, Yu Sun, Tingting Huang, Wenyi Xiang, Changhui Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Photoacoustics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

7 authors.

Yu ZhangDepartment of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China.
Shuang LiDepartment of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China.
Yibing WangDepartment of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China.
Yu SunDepartment of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China.
Tingting HuangDepartment of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China.
Wenyi XiangSchool of Electrical and Electronic Engineering, Nanyang Technological University, , Singapore, Singapore.
Changhui LiDepartment of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In reality, photoacoustic imaging (PAI) is generally influenced by artifacts caused by sparse array or limited view. In this work, to balance the computing cost and artifact removal performance, we propose an iterative optimization method that does not need to repeat solving forward model for every iteration circle, called the regularized iteration method with structural prior (RISP). The structural prior is a probability matrix derived from multiple reconstructed images via randomly selecting partial array elements. High-probability values indicate high coherency among multiple reconstruction results at those positions, suggesting a high likelihood of representing true imaging results. In contrast, low-probability values indicate higher randomness, leaning more towards artifacts or noise. As a structural prior, this probability matrix, together with the original PAI result using all array elements, guides the regularized iteration of the PAI results. The simulation and real animal and human PAI study results demonstrated our method can substantially reduce image artifacts, as well as noise.

Indexed as

Artifact removalImperfect distributed arraysIterative regularization methodProbability matrixRandom partial arraySparse sensor distributionStructural prior

Identifiers

PMID40454259
PMCPMC12124597

What OpenQuestion holds

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LicenceCC BY
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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.