Design and Fabrication of Lung Phantoms Using High-Precision Three-Dimensional Printing

Abstract

AbstractObjective To develop a 3D-printed phantom that accurately replicates patient-specific anatomical geometry, tissue textures, and attenuation characteristics derived from CT scans, thereby enabling precise lung tissue properties simulation for radiological medical imaging applications. Materials and Methods A streamlined workflow was developed to convert DICOM-format CT images into printer-executable G-code, eliminating conventional segmentation and intermediate file formats (e.g., STL). Using fused deposition modeling (FDM) with a 0.2-mm nozzle and polylactic acid (PLA) filament, the algorithm dynamically adjusts nozzle speed (5–50 mm/s) and extrusion rates to control line width (0.1–1 mm), thereby managing voxel density and replicating Hounsfield Unit (HU) values (-900 to 100 HU). Validation experiments used patient-specific lung CT data, and phantom accuracy was assessed through geometric measurements and HU value comparisons between printed models and original patient scans. Results The printed phantoms demonstrated a linear correlation (R² > 0.95) between designed fill rates and measured HU values, achieving submillimeter geometric accuracy in replicating lung vasculature and parenchymal structures. Manual measurements of 10 regions of interest (ROIs) revealed less than 5% deviation in dimensional fidelity, while HU distributions in phantom scans matched patient data within clinically acceptable margins (±50 HU). The method successfully simulated heterogeneous tissue textures, with printer parameters allowing for precise control over density gradients critical for radiometric applications. Conclusions This study introduces a breakthrough in patient-specific phantom fabrication, providing a rapid and cost-effective solution for validating CT-based techniques without exposure to ionizing radiation. The direct DICOM-to-G-code workflow ensures high anatomical and radiological fidelity and has applications in radiotherapy dosimetry, imaging protocol optimization, and medical training. Future research will expand to include multi-material printing and dynamic motion simulation to improve physiological realism.