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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.
Loofah natural fibers create realistic microvascular phantoms for photoacoustic imaging. This low-cost material effectively mimics human vasculature, aiding system calibration and machine learning model training.
Area of Science:
- Biomedical Engineering
- Optical Imaging
- Materials Science
Background:
- Existing photoacoustic phantoms lack the complex microvascular structures needed for accurate imaging.
- There is a need for versatile phantom materials that can replicate intricate vascular networks.
Purpose of the Study:
- To introduce loofah as a novel, natural phantom material for photoacoustic imaging.
- To fabricate phantoms with controlled optical properties mimicking human microvasculature using loofah.
Main Methods:
- Incorporated controllable chromophores into loofah to tailor absorption properties.
- Evaluated loofah phantom performance using co-registered ultrasound, acoustic-resolution photoacoustic microscopy (ARPAM), and optical-resolution photoacoustic microscopy (ORPAM).
Main Results:
- Optical-resolution photoacoustic microscopy confirmed controlled chromophore distribution and consistent photoacoustic signals.
- Acoustic-resolution photoacoustic microscopy demonstrated loofah phantoms effectively replicate vascular structures (100-500 µm diameter), outperforming conventional phantoms.
- Loofah phantoms exhibited stability in photoacoustic signal generation.
Conclusions:
- Loofah offers a low-cost, effective method for creating submillimeter microvascular phantoms for photoacoustic imaging.
- The material's morphology and customizability enable diverse vascular network configurations, improving phantom imaging fidelity.
- These realistic phantoms aid in system calibration, validation, and provide data for training machine learning models.
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