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Joome Park

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Proceedings Article | 14 June 2023 Poster + Paper

Deep learning photo-acoustic microscopy with three-dimensional under sampled data reconstruction

Daewoon Seong, Euimin Lee, Youngae Gu, Joome Park, Mansik Jeon, Jeehyun Kim

Proceedings Volume 12523, 125230E (2023) https://doi.org/10.1117/12.2664349

KEYWORDS: 3D image reconstruction, Image restoration, Deep learning, 3D image processing, Data modeling, Photoacoustic microscopy, Biological imaging, 3D modeling, Spatial resolution, Polygon scanners

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Photoacoustic microscopy (PAM) is a non-invasive, label-free functional imaging technique that provides high absorption contrast with high spatial resolution. Spatial sampling density and data size are key determinants of PAM imaging speed. Therefore, under sampling methods that reduce the number of scan points are usually employed to improve the imaging speed of PAM by increasing the scan step size. Because under sampling techniques sacrifice spatial sampling density, deep learning-based reconstruction techniques have been explored as alternatives. However, these methods have been applied to reconstruct two-dimensional PAM images related to spatial sampling density. Therefore, by considering the number of data points, the data size, and the characteristics of PAM to provide three-dimensional (3D) volume data, this study proposes a deep-learning-based complete reconstruction of under sampled 3D PAM data. newly reported to Obtained from real experiments (i.e. not manually generated). Quantitative analysis results show that the proposed method exhibits robustness and outperforms interpolation-based reconstruction methods at various under sampling ratios, resulting in 80x faster imaging speed and 800x smaller data. Improves PAM system performance with size. Furthermore, the applicability of this method is experimentally verified by enlarging a sparsely sampled test dataset. His proposed deep learning-based PAM data reconstruction has been demonstrated to be the closest model available under experimental conditions, significantly reducing the data size for processing and effectively reducing the imaging time.

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