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Updated: May 11, 2026

Fabrication and Characterization of Optical Tissue Phantoms Containing Macrostructure

Fabrication and Characterization of Optical Tissue Phantoms Containing Macrostructure

Published on: February 12, 2018

Histology-based Microstructural Tissue Phantoms for Realistic Ultrasound Simulation.

Daniek A C van Aarle¹, Richard G P Lopata¹, Hans-Martin Schwab¹

¹Photoacoustics and Ultrasound Laboratory Eindhoven (PULS/e), Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.

Ultrasonic Imaging

|December 30, 2025

Summary

This summary is machine-generated.

This study introduces a new method for creating realistic ultrasound simulations using tissue microstructure. The advanced numerical phantoms improve the accuracy of ultrasound imaging and data generation.

Keywords:

data generation in silico modeling numerical tissue phantom texture analysis ultrasound simulation

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Area of Science:

Medical Imaging
Biomedical Engineering
Computational Science

Background:

Ultrasound simulation is crucial for transducer design and image analysis.
Current simulations lack realism due to simplified tissue models.
Realistic phantoms are needed for training data with ground truth.

Purpose of the Study:

To develop a novel framework for constructing realistic 2-D numerical tissue phantoms.
To improve the accuracy and realism of ultrasound simulations.
To generate high-fidelity ultrasound training data.

Main Methods:

Histology images of various tissues were segmented to identify microstructural components.
Acoustic properties (density, speed of sound) were spatially mapped based on segmentation.
A pseudospectral wave solver was used for ultrasound simulations.
Simulations were validated against ex vivo data using quantitative metrics.

Main Results:

The novel framework successfully generated realistic 2-D numerical tissue phantoms.
Simulations using these phantoms showed improved speckle pattern realism compared to baseline.
Quantitative analysis confirmed enhanced realism in ultrasound simulations.
Integration with CT data shows potential for patient-specific ultrasound datasets.

Conclusions:

The developed framework enables accurate and realistic ultrasound simulations based on histological data.
This approach significantly enhances the fidelity of in silico phantoms for ultrasound research.
The method holds promise for generating realistic ultrasound datasets for various applications.