Tissue Mimicking
Tissue Mimicking - Related Videos
06:33
Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement
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Cited by 1 •
2013
This paper describes in-house procedures of constructing a preclinical multimodality phantom made of tissue-mimicking (TM) materials for quality assurance (QA) of tumor size measurement in animal imaging modalities such as ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI).
09:25
Agarose-based Tissue Mimicking Optical Phantoms for Diffuse Reflectance Spectroscopy
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Cited by 32 •
2018
Here, we demonstrate how agarose-based tissue-mimicking optical phantoms are made and how their optical properties are determined using a conventional optical system with an integrating sphere.
08:24
Preparation and Characterization of Novel HDL-mimicking Nanoparticles for Nerve Growth Factor Encapsulation
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Cited by 4 •
2017
Simple homogenization was used to prepare novel, high-density, lipoprotein-mimicking nanoparticles to encapsulate nerve growth factor. Challenges, detailed protocols for nanoparticle preparation, in vitro characterization, and in vivo studies are described in this article.
03:06
Encapsulating Nerve Growth Factor in High-Density Lipoprotein-Mimicking Nanoparticles
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2025
Source: Zhu, J., et. al., Preparation and Characterization of Novel HDL-mimicking Nanoparticles for Nerve Growth Factor Encapsulation. J. Vis. Exp. (2017)This video demonstrates the stepwise assembly of NGF-loaded HDL-mimicking nanoparticles using protamine-assisted encapsulation and lipid-based self-assembly for potential neurotrophic delivery applications.
09:32
Manufacturing Abdominal Aorta Hydrogel Tissue-Mimicking Phantoms for Ultrasound Elastography Validation
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Cited by 12 •
2018
Here we describe a method to manufacture aneurysmal, aortic tissue-mimicking phantoms for the use in testing ultrasound elastography. The combined use of computer-aided design (CAD) and 3-dimensional (3D) printing techniques produce aortic phantoms with predictable, complex geometries to validate the elastographic imaging algorithms with controlled experiments.
FAQs
Design begins by identifying which properties matter for the intended bioengineering task. Depending on the application, the model may need to approximate stiffness, elasticity, acoustic response, optical behavior, or composition. Selecting only relevant characteristics helps align the material with a specific imaging, sensing, surgical, or therapeutic-device evaluation rather than attempting to reproduce every feature of living tissue.
Stiffness and elasticity determine how a model responds to applied forces, making them important for evaluating interactions with surgical tools, sensors, and therapeutic devices. Matching these mechanical characteristics can make testing more representative of the target tissue. It also allows investigators to compare device behavior under controlled conditions and identify performance differences without relying exclusively on biological samples.
Acoustic properties are central when a model is used to calibrate or validate medical imaging systems that depend on sound, while optical behavior matters for technologies that interact with tissue through light. Material formulations can therefore be selected to approximate the relevant response. This application-specific matching supports more meaningful system testing than using a material with unrelated physical characteristics.
A typical workflow starts by identifying the target tissue characteristics and the device or system to be tested. Researchers then formulate a material using polymers, hydrogels, or other components chosen to approximate the required properties. The resulting model can be used under reproducible conditions to calibrate or validate equipment and to assess device behavior before further development.
Tissue-mimicking materials support calibration and validation of medical imaging systems, surgical tools, sensors, and therapeutic devices. Their controlled properties provide a consistent test platform for examining whether equipment responds as intended. Because the same type of model can be used repeatedly, investigators can evaluate performance systematically and reduce dependence on variable biological samples during early testing.
These models provide a controlled setting for studying tissue-device interactions before technologies are advanced toward clinical use. Reproducible testing can reveal how a tool, sensor, or therapeutic device behaves against relevant material properties while reducing the need to begin with biological samples. The resulting evidence can support refinement, improve experimental safety, and inform later translation efforts.