Development of a 3D-Printed Chest Phantom with Simulation of Lung Nodules for Studying Ultra-Low-Dose Computed Tomography Protocols
This study aimed to 3D print a patient-specific chest phantom simulating multiple lung nodules to optimise low-dose Computed Tomography (CT) protocols for lung cancer screening. The chest phantom, which was developed from a single patient’s chest CT images, was fabricated using a variety of material...
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2024-12-01
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| author | Jenna Silberstein Steven Tran Yin How Wong Chai Hong Yeong Zhonghua Sun |
| author_facet | Jenna Silberstein Steven Tran Yin How Wong Chai Hong Yeong Zhonghua Sun |
| author_sort | Jenna Silberstein |
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| description | This study aimed to 3D print a patient-specific chest phantom simulating multiple lung nodules to optimise low-dose Computed Tomography (CT) protocols for lung cancer screening. The chest phantom, which was developed from a single patient’s chest CT images, was fabricated using a variety of materials, including polylactic acid (PLA), Glow-PLA, acrylonitrile butadiene styrene (ABS), and polyurethane resin. The phantom was scanned under different low-dose (LDCT) and ultra-low-dose CT (ULDCT) protocols by varying the kilovoltage peak (kVp) and milliampere-seconds (mAs). Subjective image quality of each scan (656 images) was evaluated by three radiologists using a five-point Likert scale, while objective image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Anatomical conformance was assessed by comparing tissue diameters of the phantom and patient scans using Bland–Altman analysis. 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 (<i>p</i> < 0.05). Both SNR and CNR decreased as the effective dose was reduced, with a strong positive linear correlation (r = 0.927 and r = 0.931, respectively, <i>p</i> < 0.001, Jamovi, version 2.3.28). 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 −0.152], <i>p</i> < 0.002, SPSS Statistics, version 30). 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. The measurement error between patient and phantom scans was −0.03 ± 0.14 cm. 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. |
| format | Article |
| id | doaj-art-0e34fc3dd52848b182db3297c3bc8502 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
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| series | Applied Sciences |
| spelling | doaj-art-0e34fc3dd52848b182db3297c3bc85022025-01-10T13:15:06ZengMDPI AGApplied Sciences2076-34172024-12-0115130910.3390/app15010309Development of a 3D-Printed Chest Phantom with Simulation of Lung Nodules for Studying Ultra-Low-Dose Computed Tomography ProtocolsJenna Silberstein0Steven Tran1Yin How Wong2Chai Hong Yeong3Zhonghua Sun4Discipline of Medical Radiation Science, Curtin Medical School, Curtin University, Perth, WA 6845, AustraliaDiscipline of Medical Radiation Science, Curtin Medical School, Curtin University, Perth, WA 6845, AustraliaSchool of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, Subang Jaya 47500, MalaysiaSchool of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, Subang Jaya 47500, MalaysiaDiscipline of Medical Radiation Science, Curtin Medical School, Curtin University, Perth, WA 6845, AustraliaThis study aimed to 3D print a patient-specific chest phantom simulating multiple lung nodules to optimise low-dose Computed Tomography (CT) protocols for lung cancer screening. The chest phantom, which was developed from a single patient’s chest CT images, was fabricated using a variety of materials, including polylactic acid (PLA), Glow-PLA, acrylonitrile butadiene styrene (ABS), and polyurethane resin. The phantom was scanned under different low-dose (LDCT) and ultra-low-dose CT (ULDCT) protocols by varying the kilovoltage peak (kVp) and milliampere-seconds (mAs). Subjective image quality of each scan (656 images) was evaluated by three radiologists using a five-point Likert scale, while objective image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Anatomical conformance was assessed by comparing tissue diameters of the phantom and patient scans using Bland–Altman analysis. 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 (<i>p</i> < 0.05). Both SNR and CNR decreased as the effective dose was reduced, with a strong positive linear correlation (r = 0.927 and r = 0.931, respectively, <i>p</i> < 0.001, Jamovi, version 2.3.28). 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 −0.152], <i>p</i> < 0.002, SPSS Statistics, version 30). 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. The measurement error between patient and phantom scans was −0.03 ± 0.14 cm. 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.https://www.mdpi.com/2076-3417/15/1/3093D-printchest modellow-dose CTultra-low-dose CTlung cancer screeninglung nodules |
| spellingShingle | Jenna Silberstein Steven Tran Yin How Wong Chai Hong Yeong Zhonghua Sun Development of a 3D-Printed Chest Phantom with Simulation of Lung Nodules for Studying Ultra-Low-Dose Computed Tomography Protocols Applied Sciences 3D-print chest model low-dose CT ultra-low-dose CT lung cancer screening lung nodules |
| title | Development of a 3D-Printed Chest Phantom with Simulation of Lung Nodules for Studying Ultra-Low-Dose Computed Tomography Protocols |
| title_full | Development of a 3D-Printed Chest Phantom with Simulation of Lung Nodules for Studying Ultra-Low-Dose Computed Tomography Protocols |
| title_fullStr | Development of a 3D-Printed Chest Phantom with Simulation of Lung Nodules for Studying Ultra-Low-Dose Computed Tomography Protocols |
| title_full_unstemmed | Development of a 3D-Printed Chest Phantom with Simulation of Lung Nodules for Studying Ultra-Low-Dose Computed Tomography Protocols |
| title_short | Development of a 3D-Printed Chest Phantom with Simulation of Lung Nodules for Studying Ultra-Low-Dose Computed Tomography Protocols |
| title_sort | development of a 3d printed chest phantom with simulation of lung nodules for studying ultra low dose computed tomography protocols |
| topic | 3D-print chest model low-dose CT ultra-low-dose CT lung cancer screening lung nodules |
| url | https://www.mdpi.com/2076-3417/15/1/309 |
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