[1] L.R. Koetzier, D. Mastrodicasa, T.P. Szczykutowicz, N.R. van der Werf, A.S. Wang, V. Sandfort, A.J.vander Molen, D. Fleischmann, and M.J. Willemink, 2023. Deep learning image reconstruction for CT: technicalprinciples and clinical prospects. Radiology, 306(3), p.e221257. https://doi.org/10.1148/radiol.221257
[2] ICRP 154: Optimisation of Radiological Protection in Digital Radiology Techniques for Medical Imaging. (2023), Vol.52, nº3. ISBN 9781036206000
[3] Siegel RL, et al. Cancer Statistics (CA) , (2025); vol 75(1): 10-45.
[4] Harry J. de Koning et al. “Reduced Lung-Cancer Mortality with Volume CTScreening in a Randomized Trial”. In: New England Journal of Medicine 382:503–513. issn: 0028-4793. doi: 10.1056/nejmoa1911793.
[5] Multi-national project set to boost lung cancer screening in Europe - europeanlung.org. https://europeanlung.org/solace/2023/04/25/multi-nationalproject- set - to - boost - lung - cancer - screening - in - europe/.
[6] Valeria Filippou and Charalampos Tsoumpas. “Recent advances on the development of phantoms using 3D printing for imaging with CT, MRI, PET, SPECT, and ultrasound”. In: Medical physics 45.9 (2018), e740–e760.
[7] Irene Hernandez-Giron et al. “Development of a 3D printed anthropomorphiclung phantom for image quality assessment in CT”. In: Physica Medica 57 (2019), vol.57: 47-57
[8] Justin Solomon, François Bochud, and Ehsan Samei. “Design of anthropomorphic textured phantoms for CT performance evaluation”. In: Medical Imaging 2014: Physics of Medical Imaging. Vol. 9033. SPIE. 2014, pp. 510–520.
[9] Mei, K., M. Geagan, L. Roshkovan, H.I. Litt, G.J. Gang, N. Shapira, J.W. Stayman and P.B. Noël, 2022. Threeâ€Âdimensional printing of patientâ€Âspecific lung phantoms for CT imaging: emulating lung tissue with accurate attenuation profiles and textures. Medical physics, 49(2), pp.825-835.
https://doi.org/10.1002/mp.15407
[10] I. Hernandez-Giron, P. McHale, A. Gaffney, B. Snow, J. Egan, C. D'Helft, R. Byrne, W. Veldkamp, J. den Harder, 2024. RPS 313-4: Anthropomophic 3D printed lung vessel phantoms combined with lung. ECR 2024 Book of Abstracts. Insights Imaging 15 (Suppl 2), 223 (2024). doi: 10.1186/s13244-024-01766-w
[11] S. G. Armato III et al. Data From LIDC-IDRI [Data set]. 2015. https://www.cancerimagingarchive.net/collection/lidc-idri/
[12] Matthew C Hancock and Jerry F Magnan. “Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods”. In: Journal of Medical Imaging 3 (4 2016), p. 44504.doi: 10.1117/1.JMI.3.4.044504. url: https://doi.org/10.1117/1.JMI.3.4.044504
[13] E.R. Weibel and D.M. Gomez, 1962. Architecture of the human lung: use of quantitative methods establishes fundamental relations between size and number of lung structures. Science, 137(3530), pp.577-585.
[14] https://www.materialise.com/en
[15] https://www.sculpteo.com/en/
[16] https://www.kyotokagaku.com/en/products_data/ph-1c_en/