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Related Experiment Video

Updated: Jun 16, 2026

Fabrication and Characterization of Optical Tissue Phantoms Containing Macrostructure

Fabrication and Characterization of Optical Tissue Phantoms Containing Macrostructure

Published on: February 12, 2018

In-silico CT simulations of deep learning generated heterogeneous phantoms.

Cornelio Salvador Salinas¹, Kirti Magudia², Aman Sangal³

¹Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Duke University, United States of America.

Biomedical Physics & Engineering Express

|June 30, 2025

Summary

This summary is machine-generated.

This study introduces a deep learning model to create realistic virtual imaging phantoms with intra-organ textures. The generated heterogeneous phantoms improve the fidelity of in silico trials for medical imaging simulations.

Keywords:

CGAN CT organ textures CT synthesis deep learning generative AI virtual CT scanner virtual trials

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Last Updated: Jun 16, 2026

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Area of Science:

Medical Imaging
Computational Phantoms
Artificial Intelligence

Background:

Current virtual imaging phantoms lack realistic intra-organ texture and material variation.
Biological tissues exhibit inherent heterogeneity, necessitating more complex virtual models.

Purpose of the Study:

To develop and train a deep learning model for generating realistic heterogeneous virtual imaging phantoms.
To incorporate intra-organ texture and material variation into virtual phantoms for enhanced realism.

Main Methods:

Training two 3D Double U-Net conditional generative adversarial networks (3D DUC-GAN) on CT image-segmentation pairs.
Generating sixteen unique organ textures for torso organs.
Simulating virtual CT scans of generated phantoms using DukeSim.

Main Results:

The 3D DUC-GAN model synthesized realistic heterogeneous phantoms with a mean absolute difference of 46.15 ± 1.06 HU compared to original CT scans.
Achieved a structural similarity index (SSIM) of 0.86 ± 0.004 and peak signal-to-noise ratio (PSNR) of 28.62 ± 0.14.
Demonstrated significant improvements over homogeneous texture methods, with metrics showing 27-28% enhancement.

Conclusions:

The generated heterogeneous phantoms offer a significant step toward more realistic in silico trials.
Enhanced simulation of imaging procedures with greater fidelity to true anatomical variation is now possible.
The deep learning approach provides a robust method for creating anatomically accurate and texturally realistic virtual phantoms.