Development of a 3D-Printed Chest Phantom with Simulation of Lung Nodules for Studying Ultra-Low-Dose Computed Tomography Protocols
Year: 2024
Type: article
Abstract: This study aimed to 3D print a patient-specific chest phantom simulating multiple lung nodules to optimise low-dose CT protocols for lung cancer screening. The chest phantom, which was developed from a selected patient’s chest CT images was fabricated using a variety of materials, including PLA, Glow-PLA, ABS, and polyurethane resin. The phantom was scanned under different low-dose (LDCT) and ultra-low-dose CT (ULDCT) protocols by varying the kVp and mAs. Subjective image quality was evaluated by three radiologists using a 5-point Likert scale, while objective image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The phantom's lung tissue, lung nodules, and diaphragm demonstrated radiation attenuation comparable to patient tissue, as measured in Hounsfield Units (HU). However, significant variations in HU were observed for the skin, subcutaneous fat, muscle, bone, heart, lung vessels, and blood vessels compared to patient tissues, with values ranging from 93.9 HU to -196 HU (p < 0.05). Both SNR and CNR decreased as the effective dose was reduced, with a strong positive linear correlation (r = 0.92 and r = 0.93, respectively, p < 0.001). The median subjective image quality score from radiologists was 4, indicating good diagnostic confidence across all CT protocols (κ=-0.398, 95% CI [-0.644 to -0152], p < .002.). An optimal protocol of 80 kVp and 30 mAs was identified for lung nodule detection, delivering a dose of only 0.23 mSv, which represents a 96% reduction compared to standard CT protocols. These findings highlight the potential for significant dose reductions in lung cancer screening programs. Further studies are recommended to improve the phantom by selecting more tissue-equivalent materials.
Source: Applied Sciences
DOI: 10.3390/app15010309
Authors: Jenna Silberstein, Steven Tran, Yin How Wong, Chai Hong Yeong, Zhonghua Sun
Institutions: Curtin University, Taylor's University
Language: English
Topic: Radiation Dose and Imaging
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4405927438