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Updated: Jun 1, 2025

Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging
Published on: June 16, 2023
Low-cost microvascular phantom for photoacoustic imaging using loofah
Jinhua Xu1,2, Yixiao Lin1, Sanskar Thakur1,2
1Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri, United States.
Significance:
Existing photoacoustic phantoms are unable to mimic complex microvascular structures with varying sizes and distributions. A suitable material with structures that mimic intricate microvascular networks is needed.
Aim:
Our aim is to introduce loofah as a natural phantom material with complex fiber networks ranging from 50 to , enabling the fabrication of phantoms with controlled optical properties comparable to those of human microvasculature.
Approach:
By introducing a controllable chromophore into the loofah material, we controlled its absorption properties. The loofah's vasculature-mimetic capabilities and stability in photoacoustic signal generation were evaluated using co-registered ultrasound, acoustic-resolution photoacoustic microscopy (ARPAM), and optical-resolution photoacoustic microscopy (ORPAM).
Results:
ORPAM results confirmed the loofah's ability to control chromophore distribution, leading to consistent and regulated photoacoustic signals. ARPAM results demonstrated that the loofah phantom effectively replicates vascular structures, exhibiting superior performance in mimicking microvascular networks compared with commonly used tissue-mimetic phantoms. The dominant diameter range of the phantom's microvasculature was between 100 and , aligning well with the targeted range and facilitating meaningful comparisons with human vascular structures.
Conclusions:
The loofah material provides a low-cost and effective method for creating submillimeter microvascular phantoms for photoacoustic imaging. Its exceptional morphology and customizability allow it to be shaped into various vascular network configurations, enhancing the fidelity of phantom imaging and assisting in system calibration and validation. In addition, data obtained from this realistic microvascular phantom can offer greater opportunities for training machine learning models.
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