Integrating visual state space models with frequency selection for artifact reduction in photoacoustic tomography.

Xiaoli Yang, Jinzhuang Xu, Xiaoxue Wang, Yu Wang, Moritz Wildgruber, Nian Liu, Jianshe Zhao, Xiaopeng Ma

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Article in Photoacoustics, 2026. 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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8 authors.

Xiaoli YangSchool of Information Science and Engineering, University of Jinan, Jinan, Shandong 250022, China.

Jinzhuang XuSchool of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China.

Xiaoxue WangSchool of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China.

Yu WangSchool of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China.

Moritz WildgruberDepartment of Radiology, University Hospital, LMU Munich, Munich, D-81337, Germany.

Nian LiuDepartment of Nuclear Medicine, The 960th Hospital of PLA, Jinan, China.

Jianshe ZhaoChildren's Hospital Affiliated to Shandong University, Jinan, Shandong 250022, China.

Xiaopeng MaSchool of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Photoacoustic tomography (PAT) with sparse angular sampling suffers from structured artifacts and loss of fine details. We propose the Visual Mamba and Frequency Selection Network (VMFSNet), an image-domain restoration framework that combines visual state-space modeling for long-range spatial dependencies with content-adaptive frequency selection. The frequency-selection module dynamically generates low-pass and residual high-pass features, enabling input-conditioned frequency modulation without predefined transform bases. A compound loss further constrains spatial intensity, Fourier-domain consistency, and structural similarity. VMFSNet was evaluated on simulated vessel, phantom, and

Indexed as

Artifact reductionFrequency selectionPhotoacoustic tomographySparse samplingVisual state space model

Identifiers

PMID42893229

DOI10.1016/j.pacs.2026.100888

PMCPMC13676139

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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.