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Modeling Lung Architecture in the XCAT Series of Phantoms: Physiologically Based Airways, Arteries and Veins.
IEEE Trans Med Imaging. 2018 Mar;37(3):693-702. doi: 10.1109/TMI.2017.2769640.
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A set of 4D pediatric XCAT reference phantoms for multimodality research.
Med Phys. 2014 Mar;41(3):033701. doi: 10.1118/1.4864238.
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Development of a model of the coronary arterial tree for the 4D XCAT phantom.
Phys Med Biol. 2011 Sep 7;56(17):5651-63. doi: 10.1088/0031-9155/56/17/012. Epub 2011 Aug 10.
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Realistic CT simulation using the 4D XCAT phantom.
Med Phys. 2008 Aug;35(8):3800-8. doi: 10.1118/1.2955743.
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Cardiac motion correction based on partial angle reconstructed images in x-ray CT.
Med Phys. 2015 May;42(5):2560-71. doi: 10.1118/1.4918580.

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In silico modeling of a clinical photon-counting CT system: Verification and validation.
Med Phys. 2025 Jun;52(6):3840-3853. doi: 10.1002/mp.17886. Epub 2025 May 13.
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Effects of Intra-patient Lung Volume Variability on CT-Based Emphysema Quantification: A Virtual Imaging Study.
Acad Radiol. 2025 Aug;32(8):4913-4921. doi: 10.1016/j.acra.2025.04.042. Epub 2025 May 9.
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XCAT 3.0: A comprehensive library of personalized digital twins derived from CT scans.
Med Image Anal. 2025 Jul;103:103636. doi: 10.1016/j.media.2025.103636. Epub 2025 May 3.
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The Role of Harmonization: A Systematic Analysis of Various Task-based Scenarios.
Proc SPIE Int Soc Opt Eng. 2025 Feb;13405. doi: 10.1117/12.3047096. Epub 2025 Apr 8.
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The potential of photon-counting CT for the improved precision of lung nodule radiomics.
Phys Med Biol. 2025 Jan 27;70(3). doi: 10.1088/1361-6560/adaad2.
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AAPM Truth-based CT (TrueCT) reconstruction grand challenge.
Med Phys. 2025 Apr;52(4):1978-1990. doi: 10.1002/mp.17619. Epub 2025 Jan 14.
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Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.
MAGMA. 2024 Aug;37(4):749-763. doi: 10.1007/s10334-024-01199-y. Epub 2024 Aug 21.
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A Physics-Informed Deep Neural Network for Harmonization of CT Images.
IEEE Trans Biomed Eng. 2024 Dec;71(12):3494-3504. doi: 10.1109/TBME.2024.3428399. Epub 2024 Nov 21.
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Quantitative accuracy of lung function measurement using parametric response mapping: A virtual imaging study.
Proc SPIE Int Soc Opt Eng. 2024 Feb;12927. doi: 10.1117/12.3006833. Epub 2024 Apr 3.

本文引用的文献

1
The Effect of Contrast Material on Radiation Dose at CT: Part II. A Systematic Evaluation across 58 Patient Models.
Radiology. 2017 Jun;283(3):749-757. doi: 10.1148/radiol.2017152852. Epub 2017 Mar 13.
2
Developmental model of an automatic production of the human bronchial tree based on L-system.
Comput Methods Programs Biomed. 2016 Aug;132:1-10. doi: 10.1016/j.cmpb.2016.04.021. Epub 2016 Apr 25.
4
Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms.
PLoS One. 2016 Jan 5;11(1):e0146060. doi: 10.1371/journal.pone.0146060. eCollection 2016.
5
Development and Analysis of Patient-Based Complete Conducting Airways Models.
PLoS One. 2015 Dec 11;10(12):e0144105. doi: 10.1371/journal.pone.0144105. eCollection 2015.
10
Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study.
Med Image Anal. 2014 Oct;18(7):1217-32. doi: 10.1016/j.media.2014.07.003. Epub 2014 Jul 23.

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