Artifact reduction in photoacoustic images by generating virtual dense array sensor from hemispheric sparse array sensor using deep learning

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Deep learning can reduce artifacts in photoacoustic imaging by generating virtual dense array sensor signals from sparse hemispherical array sensors. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Makoto Yamakawa; Tsuyoshi Shiina

doi:10.3179/jjmu.jjmu.k.40

December 1, 2025Choonpa Igaku

Artifact reduction in photoacoustic images by generating virtual dense array sensor from hemispheric sparse array sensor using deep learning

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Key Points

The study aims to reduce artifacts in photoacoustic images to improve the visualization of vascular structures.
Utilized deep learning to generate signals from a sparse hemispheric array sensor.
Created virtual dense array sensor signals to enhance image quality.
Evaluated the effectiveness of the new method in reducing artifacts.
Significant reduction in artifacts as measured by imaging quality scores.
Improved clarity of vascular structures visualized through the enhanced imaging method.

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Implication

Shows artifact reduction in photoacoustic imaging, suggesting improved diagnostic clarity.

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Cite This Study

Yamakawa et al. (2025) studied this question.

synapsesocial.com/papers/6a213f83e06b4fc4c1ab870a https://doi.org/10.3179/jjmu.jjmu.k.40

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Artifact reduction in photoacoustic images by generating virtual dense array sensor from hemispheric sparse array sensor using deep learning

Key Points

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Implication

Abstract

Discussion

Cite This Study

Artifact reduction in photoacoustic images by generating virtual dense array sensor from hemispheric sparse array sensor using deep learning

Key Points

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Implication

Abstract

Discussion

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