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基于多尺度方法对不同体积分数的胶体悬浮液中的光声压力产生进行建模。

Modeling photoacoustic pressure generation in colloidal suspensions at different volume fractions based on a multi-scale approach.

作者信息

Fujii Hiroyuki, Terabayashi Iori, Kobayashi Kazumichi, Watanabe Masao

机构信息

Division of Mechanical and Space Engineering, Faculty of Engineering, Hokkaido University, Kita 13 Nishi 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan.

出版信息

Photoacoustics. 2022 May 14;27:100368. doi: 10.1016/j.pacs.2022.100368. eCollection 2022 Sep.

DOI:10.1016/j.pacs.2022.100368
PMID:35646589
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9130529/
Abstract

Further development of quantitative photoacoustic tomography requires understanding the photoacoustic pressure generation by modeling the generation process. This study modeled the initial photoacoustic pressure in colloidal suspensions, used as tissue phantoms, at different volume fractions on a multi-scale approach. We modeled the thermodynamic and light scattering properties on a microscopic scale with/without treating the hard-sphere interaction between colloidal particles. Meanwhile, we did the light energy density on a macroscopic scale. We showed that the hard-sphere interaction significantly influences the initial pressure and related quantities at a high volume fraction except for the thermodynamic properties. We also showed the initial pressure at the absorber inside the medium logarithmically decreases with increasing the volume fractions. This result is mainly due to the decay of the light energy density with light scattering. Our numerical results suggest that modeling light scattering and propagation is crucial over modeling thermal expansion.

摘要

定量光声层析成像技术的进一步发展需要通过对光声压力产生过程进行建模来理解其产生机制。本研究采用多尺度方法,对用作组织模型的胶体悬浮液在不同体积分数下的初始光声压力进行了建模。我们在微观尺度上对热力学和光散射特性进行了建模,考虑了胶体颗粒之间的硬球相互作用和未考虑该相互作用的两种情况。同时,我们在宏观尺度上计算了光能密度。结果表明,除了热力学性质外,硬球相互作用在高体积分数下对初始压力及相关量有显著影响。我们还表明,介质内部吸收体处的初始压力随体积分数的增加呈对数下降。这一结果主要是由于光能密度随光散射而衰减。我们的数值结果表明,对光散射和传播进行建模比对热膨胀进行建模更为关键。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/8043bf4fc1fc/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/87455211a225/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/23c1e4c3c74f/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/7e574003e512/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/4cdd8a4a208e/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/4d570f8ccdc1/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/84b1e2636015/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/8043bf4fc1fc/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/87455211a225/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/23c1e4c3c74f/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/7e574003e512/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/4cdd8a4a208e/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/4d570f8ccdc1/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/84b1e2636015/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/527b/9130529/8043bf4fc1fc/gr6.jpg

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Opt Express. 2020 Jul 20;28(15):22962-22977. doi: 10.1364/OE.398582.
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End-to-end deep neural network for optical inversion in quantitative photoacoustic imaging.用于定量光声成像中光反演的端到端深度神经网络。
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