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Micro-CT acquisition and image processing to track and characterize pulmonary nodules in mice

Nature Protocols volume 18, pages 990–1015 (2023)Cite this article

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An Addendum to this article was published on 24 April 2023

Abstract

X-ray computed tomography is a reliable technique for the detection and longitudinal monitoring of pulmonary nodules. In preclinical stages of diagnostic or therapeutic development, the miniaturized versions of the clinical computed tomography scanners are ideally suited for carrying out translationally-relevant research in conditions that closely mimic those found in the clinic. In this Protocol, we provide image acquisition parameters optimized for low radiation dose, high-resolution and high-throughput computed tomography imaging using three commercially available micro-computed tomography scanners, together with a detailed description of the image analysis tools required to identify a variety of lung tumor types, characterized by specific radiological features. For each animal, image acquisition takes 4–8 min, and data analysis typically requires 10–30 min. Researchers with basic training in animal handling, medical imaging and software analysis should be able to implement this protocol across a wide range of lung cancer models in mice for investigating the molecular mechanisms driving lung cancer development and the assessment of diagnostic and therapeutic agents.

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Fig. 1: Common radiological characteristics of lung tumor models.
Fig. 2: Summary of the workflow for the lung tumor imaging with micro-CT and tumor volume analysis.
Fig. 3: Micro-CT acquisition.
Fig. 4: Differentiation of tumors from normal structure in 3D.
Fig. 5: Individual tumor nodule segmentation using CTAn software.
Fig. 6: Individual tumor volume measurements using CTAn software.
Fig. 7: Improving image quality with Analyze software.
Fig. 8: Individual tumor segmentation and quantification using Analyze software.
Fig. 9: Lung volume segmentation using CTAn software.
Fig. 10: Automatic lung segmentation and volume quantification with Analyze software.
Fig. 11: Tracking individual tumor volume changes over time.

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Data availability

The main data discussed in this protocol are available in the supporting primary research papers (https://doi.org/10.1126/scitranslmed.aaw7999 and https://doi.org/10.1038/s41467-021-26214-x). The raw datasets are too large to be publicly shared but are available for research purposes from the corresponding authors upon reasonable request. Additional data and software handling information for CT analysis are available here: https://doi.org/10.6084/m9.figshare.22355029.v2.

References

  1. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 71, 209–249 (2021).

    PubMed  Google Scholar 

  2. Graham, M. L. & Prescott, M. J. The multifactorial role of the 3Rs in shifting the harm-benefit analysis in animal models of disease. Eur. J. Pharmacol. 759, 19–29 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Hounsfield, G. N. Computed medical imaging. Nobel lecture, December 8, 1979. J. Comput. Assist. Tomogr. 4, 665–674 (1980).

    Article  CAS  PubMed  Google Scholar 

  4. Lev, M. H. & Gonzalez, R. G. in Brain Mapping: The Methods (Second Edition) (eds Arthur W. Toga & John C. Mazziotta) 427–484 (Academic Press, 2002).

  5. Bibb, R., Eggbeer, D. & Paterson, A. in Medical Modelling (Second Edition) (eds R. Bibb, D. Eggbeer, & A. Paterson) 7–34 (Woodhead Publishing, 2015).

  6. Jonas, D. E. et al. Screening for lung cancer with low-dose computed tomography: updated evidence report and systematic review for the US preventive services task force. JAMA 325, 971–987 (2021).

    Article  PubMed  Google Scholar 

  7. Castellano, E. et al. Requirement for interaction of PI3-kinase p110α with RAS in lung tumor maintenance. Cancer Cell 24, 617–630 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. de Bruin, E. C. et al. Reduced NF1 expression confers resistance to EGFR inhibition in lung cancer. Cancer Discov. 4, 606–619 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Molina-Arcas, M. et al. Development of combination therapies to maximize the impact of KRAS-G12C inhibitors in lung cancer. Sci. Transl. Med. 11, eaaw7999 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Spiro, J. E. et al. Monitoring treatment effects in lung cancer-bearing mice: clinical CT and clinical MRI compared to micro-CT. Eur. Radiol. Exp. 4, 31 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Rudyanto, R. D. et al. Individual nodule tracking in micro-CT images of a longitudinal lung cancer mouse model. Med. Image Anal. 17, 1095–1105 (2013).

    Article  PubMed  Google Scholar 

  12. Holdsworth, D. W. & Thornton, M. M. Micro-CT in small animal and specimen imaging. Trends Biotechnol. 20, S34–S39 (2002).

    Article  Google Scholar 

  13. Clark, D. P. & Badea, C. T. Micro-CT of rodents: state-of-the-art and future perspectives. Phys. Med. 30, 619–634 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ford, N. L., Wheatley, A. R., Holdsworth, D. W. & Drangova, M. Optimization of a retrospective technique for respiratory-gated high speed micro-CT of free-breathing rodents. Phys. Med. Biol. 52, 5749–5769 (2007).

    Article  PubMed  Google Scholar 

  15. Ertel, D., Kyriakou, Y., Lapp, R. M. & Kalender, W. A. Respiratory phase-correlated micro-CT imaging of free-breathing rodents. Phys. Med. Biol. 54, 3837–3846 (2009).

    Article  PubMed  Google Scholar 

  16. Kumar, M. S. et al. The GATA2 transcriptional network is requisite for RAS oncogene-driven non-small cell lung cancer. Cell 149, 642–655 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. Foster, H. et al. ATMIN is a tumor suppressor gene in lung adenocarcinoma. Cancer Res. 79, 5159–5166 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Boumelha, J. et al. An immunogenic model of KRAS-Mutant lung cancer enables evaluation of targeted therapy and immunotherapy combinations. Cancer Res. 82, 3435–3448 (2022).

  19. Kennel, S. J. et al. High resolution computed tomography and MRI for monitoring lung tumor growth in mice undergoing radioimmunotherapy: correlation with histology. Med. Phys. 27, 1101–1107 (2000).

    Article  CAS  PubMed  Google Scholar 

  20. Fushiki, H. et al. Quantification of mouse pulmonary cancer models by microcomputed tomography imaging. Cancer Sci. 100, 1544–1549 (2009).

    Article  CAS  PubMed  Google Scholar 

  21. van Maldegem, F. et al. Characterisation of tumour microenvironment remodelling following oncogene inhibition in preclinical studies with imaging mass cytometry. Nat. Commun. 12, 5906 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Vande Velde, G. et al. Longitudinal micro-CT provides biomarkers of lung disease that can be used to assess the effect of therapy in preclinical mouse models, and reveal compensatory changes in lung volume. Dis. Model. Mech. 9, 91–98 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Marien, E., Hillen, A., Vanderhoydonc, F., Swinnen, J. V. & Vande Velde, G. Longitudinal microcomputed tomography-derived biomarkers for lung metastasis detection in a syngeneic mouse model: added value to bioluminescence imaging. Lab. Invest. 97, 24–33 (2017).

    Article  CAS  PubMed  Google Scholar 

  24. DuPage, M., Dooley, A. L. & Jacks, T. Conditional mouse lung cancer models using adenoviral or lentiviral delivery of Cre recombinase. Nat. Protoc. 4, 1064–1072 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Westcott, P. M. et al. The mutational landscapes of genetic and chemical models of Kras-driven lung cancer. Nature 517, 489–492 (2015).

    Article  CAS  PubMed  Google Scholar 

  26. Politi, K., Fan, P. D., Shen, R., Zakowski, M. & Varmus, H. Erlotinib resistance in mouse models of epidermal growth factor receptor-induced lung adenocarcinoma. Dis. Model. Mech. 3, 111–119 (2010).

    Article  CAS  PubMed  Google Scholar 

  27. Politi, K. et al. Lung adenocarcinomas induced in mice by mutant EGF receptors found in human lung cancers respond to a tyrosine kinase inhibitor or to down-regulation of the receptors. Genes Dev. 20, 1496–1510 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Bianchi, A. et al. In vivo MRI for effective non-invasive detection and follow-up of an orthotopic mouse model of lung cancer. NMR Biomed. 27, 971–979 (2014).

    Article  PubMed  Google Scholar 

  29. Krupnick, A. S. et al. Quantitative monitoring of mouse lung tumors by magnetic resonance imaging. Nat. Protoc. 7, 128–142 (2012).

    Article  CAS  PubMed  Google Scholar 

  30. Neijenhuis, L. K. A. et al. Near-infrared fluorescence tumor-targeted imaging in lung cancer: a systematic review. Life https://doi.org/10.3390/life12030446 (2022).

  31. Imamura, T., Saitou, T. & Kawakami, R. In vivo optical imaging of cancer cell function and tumor microenvironment. Cancer Sci. 109, 912–918 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Christensen, J., Vonwil, D. & Shastri, V. P. Non-invasive in vivo imaging and quantification of tumor growth and metastasis in rats using cells expressing far-red fluorescence protein. PLoS ONE 10, e0132725 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Kocher, B. & Piwnica-Worms, D. Illuminating cancer systems with genetically engineered mouse models and coupled luciferase reporters in vivo. Cancer Discov. 3, 616–629 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ju, H.-L. et al. Transgenic mouse model expressing P53R172H, luciferase, EGFP and KRASG12D in a single open reading frame for live imaging of tumor. Sci. Rep. 5, 8053 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Yeh, H. H. et al. Molecular imaging of active mutant L858R EGF receptor (EGFR) kinase-expressing nonsmall cell lung carcinomas using PET/CT. Proc. Natl Acad. Sci. USA 108, 1603–1608 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Price, D. N. et al. Longitudinal assessment of lung cancer progression in mice using the sodium iodide symporter reporter gene and SPECT/CT imaging. PloS ONE 11, e0169107 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Marsee, D. K. et al. Imaging of metastatic pulmonary tumors following NIS gene transfer using single photon emission computed tomography. Cancer Gene Ther. 11, 121–127 (2004).

    Article  CAS  PubMed  Google Scholar 

  38. Nielsen, C. H. et al. PET imaging of tumor neovascularization in a transgenic mouse model with a novel 64Cu-DOTA-knottin peptide. Cancer Res. 70, 9022–9030 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Umeda, I. O. et al. High resolution SPECT imaging for visualization of intratumoral heterogeneity using a SPECT/CT scanner dedicated for small animal imaging. Ann. Nucl. Med. 26, 67–76 (2012).

    Article  PubMed  Google Scholar 

  40. Khalil, M. M., Tremoleda, J. L., Bayomy, T. B. & Gsell, W. Molecular SPECT imaging: an overview. Int. J. Mol. Imaging 2011, 796025 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Hekman, M. C. H. et al. Detection of micrometastases using SPECT/fluorescence dual-modality imaging in a CEA-expressing tumor model. J. Nucl. Med. 58, 706–710 (2017).

    Article  CAS  PubMed  Google Scholar 

  42. Zhang, Y. et al. Preliminary application of micro-SPECT/CT imaging by 99mTc-tricine-EDDA-HYNIC-c-Met for non-small-cell lung cancer. Chem. Biol. Drug Des. 93, 447–453 (2019).

    Article  CAS  PubMed  Google Scholar 

  43. Versagli, C. et al. Multimodal optical, X-ray CT, and SPECT imaging of a mouse model of breast cancer lung metastasis. Curr. Mol. Med. https://doi.org/10.2174/1566524011313030006 (2013).

  44. V, G. et al. Development of novel approach to diagnostic imaging of lung cancer with 18F-Nifene PET/CT using A/J mice treated with NNK. J. Cancer Res. Ther. 1, 128–137 (2013).

    Article  Google Scholar 

  45. Puaux, A.-L. et al. A comparison of imaging techniques to monitor tumor growth and cancer progression in living animals. Int. J. Mol. Imaging 2011, 321538 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Yang, Z. et al. Dynamic FDG-PET imaging to differentiate malignancies from inflammation in subcutaneous and in situ mouse model for non-small cell lung carcinoma (NSCLC. PLoS ONE 10, e0139089 (2015).

  47. Molinos, C. et al. Low-dose imaging in a new preclinical total-body PET/CT scanner. Front. Med. https://doi.org/10.3389/fmed.2019.00088 (2019).

  48. Plathow, C. et al. Computed tomography monitoring of radiation-induced lung fibrosis in mice. Investig. Radiol. 39, 600–609 (2004).

    Article  Google Scholar 

  49. Berghen, N. et al. Radiosafe micro-computed tomography for longitudinal evaluation of murine disease models. Sci. Rep. 9, 17598 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Detombe, S. A., Dunmore-Buyze, J., Petrov, I. E. & Drangova, M. X-ray dose delivered during a longitudinal micro-CT study has no adverse effect on cardiac and pulmonary tissue in C57BL/6 mice. Acta Radiol. 54, 435–441 (2013).

    Article  PubMed  Google Scholar 

  51. Vande Velde, G. et al. Longitudinal in vivo microcomputed tomography of mouse lungs: no evidence for radiotoxicity. Am. J. Physiol. Lung Cell. Mol. Physiol. 309, L271–L279 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Li, J. et al. A novel functional CT contrast agent for molecular imaging of cancer. Phys. Med. Biol. 55, 4389–4397 (2010).

    Article  PubMed  Google Scholar 

  53. Gómez-López, S., Whiteman, Z. E. & Janes, S. M. Mapping lung squamous cell carcinoma pathogenesis through in vitro and in vivo models. Commun. Biol. 4, 937 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  54. You, M. S., Rouggly, L. C., You, M. & Wang, Y. Mouse models of lung squamous cell carcinomas. Cancer Metastasis Rev. 32, 77–82 (2013).

    Article  CAS  PubMed  Google Scholar 

  55. Singh, A. P., Adrianzen Herrera, D., Zhang, Y., Perez-Soler, R. & Cheng, H. Mouse models in squamous cell lung cancer: impact for drug discovery. Expert Opin. Drug Discov. 13, 347–358 (2018).

    Article  CAS  PubMed  Google Scholar 

  56. Ruiz, E. J. et al. LUBAC determines chemotherapy resistance in squamous cell lung cancer. J. Exp. Med. 216, 450–465 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Montgomery, M. K. et al. Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography. PLoS ONE 16, e0252950 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Birk, G., Kästle, M., Tilp, C., Stierstorfer, B. & Klee, S. Automatization and improvement of μCT analysis for murine lung disease models using a deep learning approach. Respir. Res. 21, 124 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Haines, B. B. et al. A quantitative volumetric micro-computed tomography method to analyze lung tumors in genetically engineered mouse models. Neoplasia 11, 39–47 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Gallastegui, A., Cheung, J., Southard, T. & Hume, K. R. Volumetric and linear measurements of lung tumor burden from non-gated micro-CT imaging correlate with histological analysis in a genetically engineered mouse model of non-small cell lung cancer. Lab. Anim. 52, 457–469 (2018).

    Article  CAS  PubMed  Google Scholar 

  61. Workman, P. et al. Guidelines for the welfare and use of animals in cancer research. Br. J. Cancer 102, 1555–1577 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Jackson, E. L. et al. The differential effects of mutant p53 alleles on advanced murine lung cancer. Cancer Res. 65, 10280–10288 (2005).

    Article  CAS  PubMed  Google Scholar 

  63. Farncombe, T. H. Software-based respiratory gating for small animal conebeam CT. Med. Phys. 35, 1785–1792 (2008).

    Article  CAS  PubMed  Google Scholar 

  64. Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Poludniowski, G., Landry, G., DeBlois, F., Evans, P. M. & Verhaegen, F. SpekCalc: a program to calculate photon spectra from tungsten anode x-ray tubes. Phys. Med. Biol. 54, N433–N438 (2009).

    Article  CAS  PubMed  Google Scholar 

  66. Poludniowski, G. G. & Evans, P. M. Calculation of x-ray spectra emerging from an x-ray tube. Part I. Electron penetration characteristics in x-ray targets. Med. Phys. 34, 2164–2174 (2007).

    Article  CAS  PubMed  Google Scholar 

  67. Poludniowski, G. G. Calculation of x-ray spectra emerging from an x-ray tube. Part II. X-ray production and filtration in x-ray targets. Med. Phys. 34, 2175–2186 (2007).

    Article  CAS  PubMed  Google Scholar 

  68. Meganck, J. A. & Liu, B. Dosimetry in micro-computed tomography: a review of the measurement methods, impacts, and characterization of the Quantum GX imaging system. Mol. Imaging Biol. 19, 499–511 (2017).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We dedicate this work to Francois Lassailly, who was instrumental in setting up the In Vivo Imaging Facility at the Francis Crick Institute. We thank E. de Bruin (AstraZeneca) for providing images from EGFR mutation model. We thank N. Corps (Skyscan, Bruker), S. Belenkov and J. Sharkey (PerkinElmer) and M. Kovacs (Mediso) for providing technical assistance with the respective scanners and software. We thank the Francis Crick Institute Biological Research facilities for technical assistance. This work was supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001039), the UK Medical Research Council (FC001039) and the Wellcome Trust (FC001039).

Author information

Author notes

  1. These authors contributed equally: May Zaw Thin, Christopher Moore.

Authors and Affiliations

  1. Cancer Stem Cell Laboratory, Institute of Cancer Research, London, UK

    May Zaw Thin & Axel Behrens

  2. Adult Stem Cell Laboratory, The Francis Crick Institute, London, UK

    May Zaw Thin & Axel Behrens

  3. Oncogene Biology Laboratory, The Francis Crick Institute, London, UK

    Christopher Moore & Julian Downward

  4. Imaging Research Facility, The Francis Crick Institute, London, UK

    Thomas Snoeks

  5. Centre for Advanced Biomedical Imaging (CABI), University College London, London, UK

    Tammy Kalber

  6. Lung Cancer Group, Division of Molecular Pathology, Institute of Cancer Research, London, UK

    Julian Downward

  7. Department of Surgery and Cancer, Imperial College London, London, UK

    Axel Behrens

  8. Cancer Research UK Convergence Science Centre, Imperial College London, London, UK

    Axel Behrens

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Contributions

M.Z.T. developed and tested the protocol in the PET/CT scanner. M.Z.T., C.M. and T.S. developed and tested the protocol in two micro-CT scanners. M.Z.T., C.M. and T.S. acquired and analyzed the data. M.Z.T. wrote the manuscript, and C.M. and T.S. provided technical details. T.K., A.B. and J.D. supervised the study. All authors edited the manuscript and approved the final version.

Corresponding authors

Correspondence to May Zaw Thin or Julian Downward.

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Competing interests

J.D. has acted as a consultant for AstraZeneca, Jubilant, Theras, BridgeBio and Vividion, and has funded research agreements with BMS and Revolution Medicines. None of the other authors of this manuscript has a financial interest related to this work.

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Nature Protocols thanks Christian Dullin and Shi-Yang Pan for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Castellano, E. et al. Cancer Cell 24, 617–630 (2013): https://doi.org/10.1016/j.ccr.2013.09.012

Molina-Arcas, M. et al. Sci Transl Med 11, eaaw7999 (2019): https://doi.org/10.1126/scitranslmed.aaw7999

van Maldegem, F. et al. Nat. Commun. 12, 5906 (2021): https://doi.org/10.1038/s41467-021-26214-x

Extended data

Extended Data Fig. 1 Highlighting and binary thresholding of individual lung tumors using Analyze software.

a–e, Screenshots showing how to draw ROI around the tumour using the tool called ‘Draw wall’ (a) and adjust the binary threshold values to display the tumour area as white pixels with a black outline (b) as presented in 3D volume rendered (c), sagittal (d) and coronal images (e).

Extended Data Fig. 2 Isolation and extraction of individual lung tumours using Analyze software.

a–c, Images from 3D volume rendered (a), sagittal (b) and coronal panels (c) showing the tumour attached to the background. d–h, Screenshots showing how to use the tool called ‘Object Separator’ (d) and erase unwanted highlighted areas manually to isolate tumour nodule from the background (e) as presented in volume rendered (f), sagittal (g) and coronal images (h).

Extended Data Fig. 3 Binary thresholding and highlighting of trachea and heart using Analyze software.

a–d, Images from 3D volume rendered (a), sagittal (b) and coronal (c) panels showing part of binary thresholded trachea after adjusting the threshold values (d). e, Screenshot showing how to highlight two regions of the heart by marking and scrolling through the frames with the tool called ‘Draw’.

Extended Data Fig. 4 Calculating signal intensity of trachea, heart and air for automatic lung segmentation with Analyze software.

a, Calculation of the mean signal intensity of trachea and heart by setting parameters. b–d, Images showing step-by-step instructions to calculate signal intensity of air inside the lung starting with creating a calibration curve with results from the trachea and the heart (b), loading Image algebra from selected lung scan (c) and then dragging image into Input picture (arrow) followed by filling equation from the calibration curve for Output and naming output image with underscore (_) at the end (d). e, Image showing how to segment the lung from the background by setting threshold in region grow.

Extended Data Fig. 5 Troubleshooting of the 3D lung volume rendering using CTAn software.

a–e, Unrepresentative structures in 3D lung volume rendering (a) can be avoided by removing fat tissue (circle) (b), gas shadow from stomach (circle) (c), motion artifacts from the ribs (black arrow) (d) and the spine (black arrow) (e) from the ROI of lung.

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Zaw Thin, M., Moore, C., Snoeks, T. et al. Micro-CT acquisition and image processing to track and characterize pulmonary nodules in mice. Nat Protoc 18, 990–1015 (2023). https://doi.org/10.1038/s41596-022-00769-5

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