A Low-cost Microvascular Phantom for Photoacoustic Imaging Using Loofah

<p>Existing photoacoustic phantoms are inadequate for mimicking the complex microvascular structures found in human tissue due to their inability to replicate varying sizes and distri- butions of vasculature. This limitation underscores the need for a new material capable of replicating intricate microvascular networks. In this thesis, we introduce loofah as a novel natural phantom material with complex fiber networks ranging from 50 to 400 μm, enabling the fabrication of phantoms with controlled optical properties comparable to those of human microvasculature.</p> <p>By incorporating a controllable chromophore into the loofah material, we adjusted its ab- sorption properties to match desired specifications. The vasculature-mimetic capabilities and stability in photoacoustic signal generation of the loofah were evaluated using co-registered ultrasound, acoustic-resolution photoacoustic microscopy (ARPAM), and optical-resolution photoacoustic microscopy (ORPAM).</p> <p>Our 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 to commonly used tissue-mimetic phantoms. The dominant diameter range of the phantom’s microvasculature was between 100–250 μm,</p> <p>aligning well with the targeted range and facilitating meaningful comparisons with human vascular structures. Furthermore, we experimented with mixed chromophores to extend the loofah phantom’s application for testing the extended functional capabilities of photoacoustic imaging.</p> <p>In conclusion, loofah material provides a low-cost and effective method for creating submil- limeter 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. Addi- tionally, data obtained from this realistic microvascular phantom offer greater opportunities for training machine learning models.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Biomedical Engineering

Year dc:date.available

2024

Author dc:creator

• Xu, Jinhua

Contributors dc:contributor

• Quing Zhu
• Song Hu; Chao Zhou

• Photoacoustic
• Phantom
• Loofah
• Engineering

Statement dc:rights

• I have not registered my thesis with the U.S. Copyright Office, and do not intend to.

Language dc:language

English (en)

Identifier

https://openscholarship.wustl.edu/eng_etds/1109

OAI identifier oai:identifier

oai:openscholarship.wustl.edu:eng_etds-2183