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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Related Experiment Video

Updated: Sep 19, 2025

Multimodal 3D Printing of Phantoms to Simulate Biological Tissue

Multimodal 3D Printing of Phantoms to Simulate Biological Tissue

Published on: January 11, 2020

3D-printable phantoms for quantitative dynamic contrast-enhanced MRI.

M Sulaiman Sarwar^1,2, Antoine Vallatos^1,3, Cher Hon Lau⁴

¹Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.

Magnetic Resonance in Medicine

|June 16, 2025

Summary

This summary is machine-generated.

Novel 3D-printed phantoms offer a new method for validating quantitative dynamic contrast-enhanced MRI (DCE-MRI) measurements. These phantoms enable consistent quality assurance and multi-site harmonization for DCE-MRI data.

Keywords:

3D printing DCE MRI perfusion permeability phantoms

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Area of Science:

Biomedical Engineering
Medical Imaging
Materials Science

Background:

Quantitative dynamic contrast-enhanced MRI (DCE-MRI) requires robust validation and standardization.
Current phantoms may not fully replicate complex physiological parameters relevant to DCE-MRI analysis.

Purpose of the Study:

To introduce a novel 3D-printed phantom design and methodology for technical validation, quality assurance, and multi-site harmonization of quantitative DCE-MRI.
To assess the correlation between 3D-printing parameters and DCE-MRI model outputs.

Main Methods:

Phantoms were fabricated using 3D-printing (3DP) of gels with integrated channels and pores to mimic blood vessels and extracellular space.
A flow circuit generated clinically relevant arterial input functions.
Nine gels with varying porosity and channel sizes were analyzed using the extended Tofts (ET) model, with parameters correlated to physical properties via regression.

Main Results:

Phantoms produced realistic arterial input functions and tissue-like signal enhancement curves accurately modeled by the ET model.
Blood plasma volume fraction (v_p) positively correlated with channel volume fraction (v_chan) and negatively with gel porosity (v_pore).
Vascular permeability-surface area product (PS) correlated positively with both v_chan and v_pore. Extravascular extracellular space (EES) volume fraction (v_e) correlated positively with v_pore.
Fitted DCE-MRI parameters demonstrated high reproducibility (2.1%-3.2% coefficient of variation).

Conclusions:

Tailorable 3D-printed porous gel phantoms effectively mimic tissue characteristics for DCE-MRI.
These phantoms show significant potential for supporting the validation, quality assurance, and multi-site harmonization of quantitative DCE-MRI measurements.