Related Experiment Video
Updated: Aug 23, 2025
Tissue-simulating Phantoms for Assessing Potential Near-infrared Fluorescence Imaging Applications in Breast Cancer Surgery
Published on: September 19, 2014
3D-Printed Tumor Phantoms for Assessment of In Vivo Fluorescence Imaging Analysis Methods
Ethan P M LaRochelle1, Samuel S Streeter2,3, Eammon A Littler2
1QUEL Imaging, 85 N. Main Street Suite 142, White River Junction, VT, 05001, USA. ethan@QUELimaging.com.
Purpose:
Interventional fluorescence imaging is increasingly being utilized to quantify cancer biomarkers in both clinical and preclinical models, yet absolute quantification is complicated by many factors. The use of optical phantoms has been suggested by multiple professional organizations for quantitative performance assessment of fluorescence guidance imaging systems. This concept can be further extended to provide standardized tools to compare and assess image analysis metrics.
Procedures:
3D-printed fluorescence phantoms based on solid tumor models were developed with representative bio-mimicking optical properties. Phantoms were produced with discrete tumors embedded with an NIR fluorophore of fixed concentration and either zero or 3% non-specific fluorophore in the surrounding material. These phantoms were first imaged by two fluorescence imaging systems using two methods of image segmentation, and four assessment metrics were calculated to demonstrate variability in the quantitative assessment of system performance. The same analysis techniques were then applied to one tumor model with decreasing tumor fluorophore concentrations.
Results:
These anatomical phantom models demonstrate the ability to use 3D printing to manufacture anthropomorphic shapes with a wide range of reduced scattering (μs': 0.24-1.06 mm-1) and absorption (μa: 0.005-0.14 mm-1) properties. The phantom imaging and analysis highlight variability in the measured sensitivity metrics associated with tumor visualization.
Conclusions:
3D printing techniques provide a platform for demonstrating complex biological models that introduce real-world complexities for quantifying fluorescence image data. Controlled iterative development of these phantom designs can be used as a tool to advance the field and provide context for consensus-building beyond performance assessment of fluorescence imaging platforms, and extend support for standardizing how quantitative metrics are extracted from imaging data and reported in literature.
