CT-Based Quantification

  • Ehsan SameiEmail author
  • Jocelyn Hoye


Image quantification is the extraction of quantitative measures from patient images and the relation of the quantitative measures to patient outcomes. For an image quantification to be most effective, it should meet four requirements of (1) relevance, (2) objectivity, (3) robustness, and (4) implementability. A relevant quantification assigns a number to an observable biological or clinical phenomenon. An objective quantification measures the phenomenon accurately. A robust quantification measures the phenomenon precisely. An implementable quantification is one which can be implemented in a timely fashion in a clinical environment and workflow. Relevance can be assessed by studying how the quantification metrics correlate with clinical patient outcomes. Objectivity and robustness can be assessed using phantoms and biological models to test how different feature extraction workflows lead to different biases and variabilities in the feature measurements. The implementation of quantification in a clinical environment should be such that it is automated, intuitive, and efficient. CT-based quantification has the potential to improve consistency and quality of patient care if it is applied using these principles.


Quantification Computed tomography Ground truth Objectivity Robustness 


  1. 1.
    Radiologic Society of North America. Quantitative imaging biomarkers alliance. 2017. Available:
  2. 2.
    Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2015;278:563–77.CrossRefGoogle Scholar
  3. 3.
    Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Cavalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.CrossRefGoogle Scholar
  4. 4.
    Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30:1234–48.CrossRefGoogle Scholar
  5. 5.
    Motta G, Carbone E, Spinelli E, Nahum M, Testa T, Flocchini G. Considerations about tumor size as a factor of prognosis in NSCLC. Ann Ital Chir. 1999;70:893–7.PubMedGoogle Scholar
  6. 6.
    Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45:228–47.CrossRefGoogle Scholar
  7. 7.
    Zwirewich CV, Vedal S, Miller RR, Müller NL. Solitary pulmonary nodule: high-resolution CT and radiologic-pathologic correlation. Radiology. 1991;179:469–76.CrossRefGoogle Scholar
  8. 8.
    Huang Y-H, Chang Y-C, Huang C-S, Wu T-J, Chen J-H, Chang R-F. Computer-aided diagnosis of mass-like lesion in breast MRI: differential analysis of the 3-D morphology between benign and malignant tumors. Comput Methods Prog Biomed. 2013;112:508–17.CrossRefGoogle Scholar
  9. 9.
    Gilhuijs KG, Giger ML, Bick U. Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging. Med Phys. 1998;25:1647–54.CrossRefGoogle Scholar
  10. 10.
    Chalkidou A, O’Doherty MJ, Marsden PK. False discovery rates in PET and CT studies with texture features: a systematic review. PLoS One. 2015;10:e0124165.CrossRefGoogle Scholar
  11. 11.
    Abadi E, Harrawood B, Sharma S, Kapadia A, Segars WP, Samei E, DukeSim: a realistic, rapid, and scanner-specific simulation framework in computed tomography. IEEE Trans Med Imaging. 2018.Google Scholar
  12. 12.
    Hoye J, Solomon J, Sauer TJ, Robins M, Samei E. Systematic analysis of bias and variability of morphologic features for lung lesions in computed tomography. J Med Imaging. 2019;6:013504.CrossRefGoogle Scholar
  13. 13.
    Zheng Y, Solomon J, Choudhury K, Marin D, Samei E. Accuracy and variability of texture-based radiomics features of lung lesions across CT imaging conditions. In SPIE Medical Imaging. Bellingham, Washington; 2017. p 101325F-101325F-7.Google Scholar
  14. 14.
    Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, et al. Measuring CT scanner variability of radiomics features. Investig Radiol. 2015;50:757.CrossRefGoogle Scholar
  15. 15.
    Richards T, Sturgeon GM, Ramirez-Giraldo JC, Rubin GD, Koweek LH, Segars WP, et al. Quantification of uncertainty in the assessment of coronary plaque in CCTA through a dynamic cardiac phantom and 3D-printed plaque model. J Med Imaging. 2018;5:013501.Google Scholar
  16. 16.
    Huang JY, Kerns JR, Nute JL, Liu X, Balter PA, Stingo FC, et al. An evaluation of three commercially available metal artifact reduction methods for CT imaging. Phys Med Biol. 2015;60:1047.CrossRefGoogle Scholar
  17. 17.
    Solomon J, Ba A, Bochud F, Samei E. Comparison of low-contrast detectability between two CT reconstruction algorithms using voxel-based 3D printed textured phantoms. Med Phys. 2016;43:6497–506.CrossRefGoogle Scholar
  18. 18.
    Castella C, Kinkel K, Descombes F, Eckstein MP, Sottas P-E, Verdun FR, et al. Mammographic texture synthesis: second-generation clustered lumpy backgrounds using a genetic algorithm. Opt Express. 2008;16:7595–607.CrossRefGoogle Scholar
  19. 19.
    Abadi E, Segars WP, Sturgeon GM, Roos JE, Ravin CE, Samei E. Modeling lung architecture in the XCAT series of phantoms: physiologically based airways, arteries and veins. IEEE Trans Med Imaging. 2018;37:693–702.CrossRefGoogle Scholar
  20. 20.
    Graff CG. A new, open-source, multi-modality digital breast phantom. In: Medical imaging 2016: physics of medical imaging. Bellingham, Washington; 2016. p. 978309.Google Scholar
  21. 21.
    Solomon J, Samei E. A generic framework to simulate realistic lung, liver and renal pathologies in CT imaging. Phys Med Biol. 2014;59:6637.CrossRefGoogle Scholar
  22. 22.
    Bortolotto C, Eshja E, Peroni C, Orlandi MA, Bizzotto N, Poggi P. 3D printing of CT dataset: validation of an open source and consumer-available workflow. J Digit Imaging. 2016;29:14–21.CrossRefGoogle Scholar
  23. 23.
    Ger RB, Zhou S, Chi P-CM, Lee HJ, Layman RR, Jones AK, et al. Comprehensive investigation on controlling for CT imaging variabilities in radiomics studies. Sci Rep. 8:13047.. 2018/08/29 2018Google Scholar
  24. 24.
    Buckler AJ, Danagoulian J, Johnson K, Peskin A, Gavrielides MA, Petrick N, et al. Inter-method performance study of tumor volumetry assessment on computed tomography test-retest data. Acad Radiol. 2015;22:1393–408.CrossRefGoogle Scholar
  25. 25.
    Robins M, Solomon J, Hoye J, Smith T, Zheng Y, Ebner L, et al. Interchangeability between real and three-dimensional simulated lung tumors in computed tomography: an interalgorithm volumetry study. J Med Imaging. 2018;5:035504.CrossRefGoogle Scholar
  26. 26.
    Bribiesca E. A measure of compactness for 3D shapes. Comput Math Appl. 2000;40:1275–84.CrossRefGoogle Scholar
  27. 27.
    Lafata K, Cai J, Wang C, Hong J, Kelsey CR, Yin F-F. Spatial-temporal variability of radiomic features and its effect on the classification of lung cancer histology. Phys Med Biol. 2018;63:225003.CrossRefGoogle Scholar
  28. 28.
    Balagurunathan Y, Kumar V, Gu Y, Kim J, Wang H, Liu Y, et al. Test–retest reproducibility analysis of lung CT image features. J Digit Imaging. 2014;27:805–23.CrossRefGoogle Scholar
  29. 29.
    Wormanns D, Kohl G, Klotz E, Marheine A, Beyer F, Heindel W, et al. Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility. Eur Radiol. 2004;14:86–92.CrossRefGoogle Scholar
  30. 30.
    Chen B, Barnhart H, Richard S, Colsher J, Amurao M, Samei E. Quantitative CT: technique dependence of volume estimation on pulmonary nodules. Phys Med Biol. 2012;57:1335.CrossRefGoogle Scholar
  31. 31.
    Gavrielides MA, Kinnard LM, Myers KJ, Petrick N. Noncalcified lung nodules: volumetric assessment with thoracic CT. Radiology. 2009;251:26–37.CrossRefGoogle Scholar
  32. 32.
    Li Q, Gavrielides MA, Zeng R, Myers KJ, Sahiner B, Petrick N. Volume estimation of low-contrast lesions with CT: a comparison of performances from a phantom study, simulations and theoretical analysis. Phys Med Biol. 2015;60:671.CrossRefGoogle Scholar
  33. 33.
    Robins M, Solomon J, Sahbaee P, Sedlmair M, Choudhury KR, Pezeshk A, et al. Techniques for virtual lung nodule insertion: volumetric and morphometric comparison of projection-based and image-based methods for quantitative CT. Phys Med Biol. 2017;62:7280.CrossRefGoogle Scholar
  34. 34.
    Robins M, Kalpathy-Cramer J, Obuchowski NA, Buckler A, Athelogou M, Jarecha R et al. Evaluation of Simulated Lesions as Surrogates to Clinical Lesions for Thoracic CT Volumetry: The Results of an International Challenge. Academic radiology. 2019; 26(7):e161–e173.CrossRefGoogle Scholar
  35. 35.
    Euler A, Solomon J, Mazurowski MA, Samei E, Nelson RC. How accurate and precise are CT based measurements of iodine concentration? A comparison of the minimum detectable concentration difference among single source and dual source dual energy CT in a phantom study. European radiology. 2019;29(4):2069–78.CrossRefGoogle Scholar
  36. 36.
    Gavrielides MA, Li Q, Zeng R, Myers KJ, Sahiner B, Petrick N. Minimum detectable change in lung nodule volume in a phantom CT study. Acad Radiol. 2013;20:1364–70.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of RadiologyDuke UniversityDurhamUSA
  2. 2.Carl E. Ravin Advanced Imaging Laboratories (RAI Labs) and Medical Physics Graduate Program, Department of RadiologyDuke UniversityDurhamUSA

Personalised recommendations