ArticleScientific reports2023
Development of a patient-specific chest computed tomography imaging phantom with realistic lung lesions using silicone casting and three-dimensional printing.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Part-solid pulmonary nodule phantoms with realistic morphology and densities by stereolithography-based 3D-printing: from design to validation.European radiology experimental · 2025Article
- Hybrid phantom for lung CT: Design and validation.Medical physics · 2025Article
- Towards a more realistic anthropomorphic chest phantom using 3D-printed and cork-integrated components.Medical physics · 2025Article
- Tissue mimicking hydrogel foam materials with mechanical and radiological properties equivalent to human lung.Scientific reports · 2025Article
- Patient-specific estimation of distal lung morphometry using aerosol deposition data.Frontiers in physiology · 2025Article
- The Role of Biophysical Factors in Organ Development: Insights from Current Organoid Models.Bioengineering (Basel, Switzerland) · 2024Review
- A customized anthropomorphic 3D-printed phantom to reproducibility assessment in computed tomography: an oncological case study.Frontiers in oncology · 2023Article
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Authors and funding
4 authors.
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Abstract
The validation of the accuracy of the quantification software in computed tomography (CT) images is very challenging. Therefore, we proposed a CT imaging phantom that accurately represents patient-specific anatomical structures and randomly integrates various lesions including disease-like patterns and lesions of various shapes and sizes using silicone casting and three-dimensional (3D) printing. Six nodules of various shapes and sizes were randomly added to the patient's modeled lungs to evaluate the accuracy of the quantification software. By using silicone materials, CT intensities suitable for the lesions and lung parenchyma were realized, and their Hounsfield unit (HU) values were evaluated on a CT scan of the phantom. As a result, based on the CT scan of the imaging phantom model, the measured HU values for the normal lung parenchyma, each nodule, fibrosis, and emphysematous lesions were within the target value. The measurement error between the stereolithography model and 3D-printing phantoms was 0.2 ± 0.18 mm. In conclusion, the use of 3D printing and silicone casting allowed the application and evaluation of the proposed CT imaging phantom for the validation of the accuracy of the quantification software in CT images, which could be applied to CT-based quantification and development of imaging biomarkers.
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