Evidence map›Paper›PMID 38021282›Full record

ArticlePhotoacoustics2023

Sparse-view reconstruction for photoacoustic tomography combining diffusion model with model-based iteration.

Xianlin Song, Guijun Wang, Wenhua Zhong, Kangjun Guo, Zilong Li, Xuan Liu, Jiaqing Dong, Qiegen Liu

Open access · goldAbstract read
In one paragraph

Article in Photoacoustics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
4.3field-weighted citation impact, top 5% of its field
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

15 citing papers in PubMed, 39 citations in OpenAlex.

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  15. Diffusion models for medical image reconstruction.BJR artificial intelligence · 2024
    Review
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

8 authors at 1 institution in 1 country.

Xianlin SongSchool of Information Engineering, Nanchang University, Nanchang 330031, China.
Guijun WangSchool of Information Engineering, Nanchang University, Nanchang 330031, China.
Wenhua ZhongSchool of Information Engineering, Nanchang University, Nanchang 330031, China.
Kangjun GuoSchool of Information Engineering, Nanchang University, Nanchang 330031, China.
Zilong LiSchool of Information Engineering, Nanchang University, Nanchang 330031, China.
Xuan LiuSchool of Information Engineering, Nanchang University, Nanchang 330031, China.
Jiaqing DongSchool of Information Engineering, Nanchang University, Nanchang 330031, China.
Qiegen LiuSchool of Information Engineering, Nanchang University, Nanchang 330031, China.
Nanchang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a non-invasive hybrid biomedical imaging technology, photoacoustic tomography combines high contrast of optical imaging and high penetration of acoustic imaging. However, the conventional standard reconstruction under sparse view could result in low-quality image in photoacoustic tomography. Here, a novel model-based sparse reconstruction method for photoacoustic tomography via diffusion model was proposed. A score-based diffusion model is designed for learning the prior information of the data distribution. The learned prior information is utilized as a constraint for the data consistency term of an optimization problem based on the least-square method in the model-based iterative reconstruction, aiming to achieve the optimal solution. Blood vessels simulation data and the animal in vivo experimental data were used to evaluate the performance of the proposed method. The results demonstrate that the proposed method achieves higher-quality sparse reconstruction compared with conventional reconstruction methods and U-Net. In particular, under the extreme sparse projection (e.g., 32 projections), the proposed method achieves an improvement of ∼ 260 % in structural similarity and ∼ 30 % in peak signal-to-noise ratio for in vivo data, compared with the conventional delay-and-sum method. This method has the potential to reduce the acquisition time and cost of photoacoustic tomography, which will further expand the application range.

Indexed as

Diffusion modelPhotoacoustic tomographySparse reconstruction

Identifiers

PMID38021282
PMCPMC10658608
OpenAlexW4386797345

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.