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European Radiology

, Volume 29, Issue 4, pp 2098–2106 | Cite as

Precision and reliability of liver iodine quantification from spectral detector CT: evidence from phantom and patient data

  • Nils Große HokampEmail author
  • Nuran Abdullayev
  • Thorsten Persigehl
  • Max Schlaak
  • Christian Wybranski
  • Jasmin A Holz
  • Thomas Streichert
  • Hatem Alkadhi
  • David Maintz
  • Stefan Haneder
Computed Tomography
  • 118 Downloads

Abstract

Objective

To comprehensively assess precision, reproducibility, and repeatability of iodine maps from spectral detector CT (SDCT) in a phantom and in patients with repetitive examination of the abdomen.

Methods

Seventy-seven patients who underwent examination two (n = 52) or three (n = 25) times according to clinical indications were included in this IRB-approved, retrospective study. The anthropomorphic liver phantom and all patients were scanned with a standardized protocol (SSDE in patients 15.8 mGy). In patients, i.v. contrast was administered and portal venous images were acquired using bolus-tracking technique. The phantom was scanned three times at three time points; in one acquisition, image reconstruction was repeated three times. Region of interest (ROI) were placed automatically (phantom) or manually (patients) in the liver parenchyma (mimic) and the portal vein; attenuation in conventional images (CI [HU]) and iodine map concentrations (IM [mg/ml]) were recorded. The coefficient of variation (CV [%]) was used to compare between repetitive acquisitions. If present, additional ROI were placed in cysts (n = 29) and hemangioma (n = 29).

Results

Differences throughout all phantom examinations were < 2%. In patients, differences between two examinations were higher (CV for CI/IM: portal vein, 2.5%/3.2%; liver parenchyma, -0.5%/-3.0% for CI/IM). In 80% of patients, these differences were within a ± 20% limit. Differences in benign liver lesions were even higher (68% and 38%, for CI and IM, respectively).

Conclusions

Iodine maps from SDCT allow for reliable quantification of iodine content in phantoms; while in patients, rather large differences between repetitive examinations are likely due to differences in biological distribution. This underlines the need for careful clinical interpretation and further protocol optimization.

Key Points

Spectral detector computed tomography allows for reliable quantification of iodine in phantoms.

In patients, the offset between repetitive examinations varies by 20%, likely due to differences in biological distribution.

Clinically, iodine maps should be interpreted with caution and should take the intra-individual variability of iodine distribution over time into account.

Keywords

Contrast media Reproducibility of results Phantoms, imaging Liver Tomography, X-ray computed 

Abbreviations

Acq

Acquisition number

CI

Conventional images

CV(mod)

(modified) Coefficient of variation

DECT

Dual-energy computed tomography

HCC

Hepatocellular carcinoma

IM

Iodine maps

RECIST

Response evaluation criteria in solid tumors

Reco

Reconstruction number

SDCT

Spectral detector computed tomography

TP

Time point

Notes

Funding

Parts of this study were funded under a research agreement between Case Western Reserve University, University Hospitals Cleveland Medical Center, and Philips Healthcare.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Dr. Nils Große Hokamp.

Conflict of interest

NGH and DM are on the speaker’s bureau of Philips Healthcare. DM received speaker’s honoraria from Philips Healthcare, not related to this project. NA, MS, CW, TS, AJH, TP, AH, and SH have nothing to disclose.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was obtained.

Methodology

• retrospective

• observational

• performed at one institution

Supplementary material

330_2018_5744_MOESM1_ESM.docx (2.8 mb)
ESM 1 (DOCX 2865 kb)

References

  1. 1.
    Giardino A, Gupta S, Olson E et al (2017) Role of imaging in the era of precision medicine. Acad Radiol 24:639–649.  https://doi.org/10.1016/j.acra.2016.11.021 CrossRefPubMedGoogle Scholar
  2. 2.
    Yankeelov TE, Mankoff DA, Schwartz LH et al (2016) Quantitative imaging in cancer clinical trials. Clin Cancer Res 22:284–290.  https://doi.org/10.1158/1078-0432.CCR-14-3336 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Tirkes T, Hollar MA, Tann M, Kohli MD, Akisik F, Sandrasegaran K (2013) Response criteria in oncologic imaging: review of traditional and new criteria. Radiographics 33:1323–1341.  https://doi.org/10.1148/rg.335125214
  4. 4.
    Lencioni R, Llovet JM (2010) Modified RECIST (mRECIST) assessment for hepatocellular carcinoma. Semin Liver Dis 30:52–60.  https://doi.org/10.1055/s-0030-1247132 CrossRefPubMedGoogle Scholar
  5. 5.
    Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577.  https://doi.org/10.1148/radiol.2015151169 CrossRefPubMedGoogle Scholar
  6. 6.
    Gatenby RA, Grove O, Gillies RJ (2013) Quantitative imaging in cancer evolution and ecology. Radiology 269:8–15.  https://doi.org/10.1148/radiol.13122697 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Faggioni L, Gabelloni M (2016) Iodine concentration and optimization in computed tomography angiography current issues. Invest Radiol 51:816–822.  https://doi.org/10.1097/RLI.0000000000000283 CrossRefPubMedGoogle Scholar
  8. 8.
    Nagayama Y, Nakaura T, Oda S, et al (2017) Dual-layer DECT for multiphasic hepatic CT with 50 percent iodine load: a matched-pair comparison with a 120 kVp protocol. Eur Radiol  https://doi.org/10.1007/s00330-017-5114-3
  9. 9.
    McCollough CH, Leng S, Yu L, Fletcher JG (2015) Dual- and multi-energy CT: principles, technical approaches, and clinical applications. Radiology 276:637–653.  https://doi.org/10.1148/radiol.2015142631 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Große Hokamp N, Salem J, Hesse A, et al (2018) Low-dose characterization of kidney stones using spectral detector computed tomography: an ex vivo study. Invest Radiol.  https://doi.org/10.1097/RLI.0000000000000468
  11. 11.
    Ananthakrishnan L, Rajiah P, Ahn R et al (2017) Spectral detector CT-derived virtual non-contrast images: comparison of attenuation values with unenhanced CT. Abdom Radiol (NY) 42:702–709.  https://doi.org/10.1007/s00261-016-1036-9 CrossRefGoogle Scholar
  12. 12.
    Pourmorteza A, Symons R, Sandfort V et al (2016) Abdominal imaging with contrast-enhanced photon-counting CT: first human experience. Radiology 279:239–245.  https://doi.org/10.1148/radiol.2016152601 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Haneder S, Siedek F, Doerner J, et al (2018) Thoracic-abdominal imaging with a novel dual-layer spectral detector CT: intra-individual comparison of image quality and radiation dose with 128-row single-energy acquisition. Acta Radiol.  https://doi.org/10.1177/0284185118762611
  14. 14.
    Hojjati M, Van Hedent S, Rassouli N et al (2017) Quality of routine diagnostic abdominal images generated from a novel detector-based spectral CT scanner: a technical report on a phantom and clinical study. Abdom Radiol (NY).  https://doi.org/10.1007/s00261-017-1170-z
  15. 15.
    Rassouli N, Chalian H, Rajiah P, Dhanantwari A, Landeras L (2017) Assessment of 70-keV virtual monoenergetic spectral images in abdominal CT imaging: a comparison study to conventional polychromatic 120-kVp images. Abdom Radiol (NY).  https://doi.org/10.1007/s00261-017-1151-2
  16. 16.
    Große Hokamp N, Höink AJ, Doerner J, et al (2017) Assessment of arterially hyper-enhancing liver lesions using virtual monoenergetic images from spectral detector CT: phantom and patient experience. Abdom Radiol (NY).  https://doi.org/10.1007/s00261-017-1411-1
  17. 17.
    ICH Expert Working Group (1994) Validation of analytical procedures. 1–17. http://www.ich.org/products/guidelines/quality/quality-single/article/validation-of-analyticalprocedures-text-and-methodology.html. Accessed 30 May 2018
  18. 18.
    Pelgrim GJ, van Hamersvelt RW, Willemink MJ et al (2017) Accuracy of iodine quantification using dual energy CT in latest generation dual source and dual layer CT. Eur Radiol 27:3904–3912.  https://doi.org/10.1007/s00330-017-4752-9 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Ehn S, Sellerer T, Muenzel D, et al (2017) Assessment of quantification accuracy and image quality of a full-body dual-layer spectral CT system. J Appl Clin Med Phys.  https://doi.org/10.1002/acm2.12243
  20. 20.
    Jacobsen MC, Schellingerhout D, Wood CA et al (2018) Intermanufacturer comparison of dual-energy CT iodine quantification and monochromatic attenuation: a phantom study. Radiology 287:224–234.  https://doi.org/10.1148/radiol.2017170896 CrossRefPubMedGoogle Scholar
  21. 21.
    Green RM, Flamm S (2002) AGA technical review on the evaluation of liver chemistry tests. Gastroenterology 123:1367–1384.  https://doi.org/10.1053/gast.2002.36061 CrossRefPubMedGoogle Scholar
  22. 22.
    Kim WR, Flamm SL, Di Bisceglie AM, Bodenheimer HC (2008) Serum activity of alanine aminotransferase (ALT) as an indicator of health and disease. Hepatology 47:1363–1370.  https://doi.org/10.1002/hep.22109 CrossRefPubMedGoogle Scholar
  23. 23.
    Leng S, Shiung M, Duan X, Yu L, Zhang Y, McCollough CH (2015) Size-specific dose estimates for chest, abdominal, and pelvic CT: effect of intrapatient variability in water-equivalent diameter. Radiology 276:184–190.  https://doi.org/10.1148/radiol.15142160
  24. 24.
    American Association of Physicists in Medicine (Task Group 204) (2011) Size-specific dose estimates (SSDE) in pediatric and adult body CT examinations. American Association of Physicists in Medicine, One Physics Ellipse, College Park, MDGoogle Scholar
  25. 25.
    Reed GF, Lynn F, Meade BD (2002) Use of coefficient of variation in assessing variability of quantitative assays. Clin Diagn Lab Immunol 9:1235–1239PubMedPubMedCentralGoogle Scholar
  26. 26.
    Fleiss JL, Cohen J (1973) The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educ Psychol Meas 33:613–619.  https://doi.org/10.1177/001316447303300309 CrossRefGoogle Scholar
  27. 27.
    Kim H, Goo JM, Kang CK, Chae KJ, Park CM (2018) Comparison of iodine density measurement among dual-energy computed tomography scanners from 3 vendors. Invest Radiol 53:321–327.  https://doi.org/10.1097/RLI.0000000000000446
  28. 28.
    Hua CH, Shapira N, Merchant TE, Klahr P, Yagil Y (2018) Accuracy of electron density, effective atomic number, and iodine concentration determination with a dual-layer dual-energy computed tomography system. Med Phys.  https://doi.org/10.1002/mp.12903
  29. 29.
    Kalisz K, Rassouli N, Dhanantwari A, Jordan D, Rajiah P (2018) Noise characteristics of virtual monoenergetic images from a novel detector-based spectral CT scanner. Eur J Radiol 98:118–125.  https://doi.org/10.1016/j.ejrad.2017.11.005
  30. 30.
    Sellerer T, Noël PB, Patino M, et al (2018) Dual-energy CT: a phantom comparison of different platforms for abdominal imaging. Eur Radiol.  https://doi.org/10.1007/s00330-017-5238-5
  31. 31.
    Skornitzke S, Fritz F, Mayer P et al (2018) Dual-energy CT iodine maps as an alternative quantitative imaging biomarker to abdominal CT perfusion: determination of appropriate trigger delays for acquisition using bolus tracking. Br J Radiol.  https://doi.org/10.1259/bjr.20170351
  32. 32.
    Stiller W, Skornitzke S, Fritz F et al (2015) Correlation of quantitative dual-energy computed tomography iodine maps and abdominal computed tomography perfusion measurements: are single-acquisition dual-energy computed tomography iodine maps more than a reduced-dose surrogate of conventional compute. Investig Radiol 50:703–708.  https://doi.org/10.1097/RLI.0000000000000176 CrossRefGoogle Scholar
  33. 33.
    Gordic S, Puippe GD, Krauss B et al (2016) Correlation between dual-energy and perfusion CT in patients with hepatocellular carcinoma. Radiology 280:78–87.  https://doi.org/10.1148/radiol.2015151560 CrossRefPubMedGoogle Scholar
  34. 34.
    Graser A, Johnson TR, Hecht EM et al (2009) Dual-energy CT in patients suspected of having renal masses: can virtual nonenhanced images replace true nonenhanced images? Radiology 252:433–440.  https://doi.org/10.1148/radiol.2522080557 CrossRefPubMedGoogle Scholar
  35. 35.
    Mileto A, Marin D, Alfaro-Cordoba M et al (2014) Iodine quantification to distinguish clear cell from papillary renal cell carcinoma at dual-energy multidetector CT: a multireader diagnostic performance study. Radiology 273:813–820.  https://doi.org/10.1148/radiol.14140171 CrossRefPubMedGoogle Scholar
  36. 36.
    Dai C, Cao Y, Jia Y et al (2018) Differentiation of renal cell carcinoma subtypes with different iodine quantification methods using single-phase contrast-enhanced dual-energy CT: areal vs. volumetric analyses. Abdom Radiol (NY) 43:672–678.  https://doi.org/10.1007/s00261-017-1253-x
  37. 37.
    Wei J, Zhao J, Zhang X et al (2018) Analysis of dual energy spectral CT and pathological grading of clear cell renal cell carcinoma (ccRCC). PLoS One 13:e0195699.  https://doi.org/10.1371/journal.pone.0195699 CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Park SY, Kim CK, Park BK (2014) Dual-energy CT in assessing therapeutic response to radiofrequency ablation of renal cell carcinomas. Eur J Radiol 83:e73–e79.  https://doi.org/10.1016/j.ejrad.2013.11.022 CrossRefPubMedGoogle Scholar
  39. 39.
    Uhrig M, Simons D, Ganten M, Hassel J, Schlemmer HP (2014) Dual-energy CT for therapy monitoring: histogram analyses of iodine maps reveal typical pattern of enhancement. Cancer Imaging 14:P17.  https://doi.org/10.1186/1470-7330-14-S1-P17

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Nils Große Hokamp
    • 1
    • 2
    • 3
    Email author
  • Nuran Abdullayev
    • 1
  • Thorsten Persigehl
    • 1
  • Max Schlaak
    • 4
  • Christian Wybranski
    • 1
  • Jasmin A Holz
    • 1
  • Thomas Streichert
    • 5
  • Hatem Alkadhi
    • 6
  • David Maintz
    • 1
  • Stefan Haneder
    • 1
  1. 1.Institute for Diagnostic and Interventional RadiologyUniversity Hospital CologneCologneGermany
  2. 2.Department of RadiologyCase Western Reserve UniversityClevelandUSA
  3. 3.Department of RadiologyUniversity Hospitals Medical CenterClevelandUSA
  4. 4.Department of DermatologyUniversity Hospital CologneCologneGermany
  5. 5.Institute for Laboratory MedicineUniversity Hospital CologneCologneGermany
  6. 6.Institute of Diagnostic and Interventional Radiology, University Hospital ZurichUniversity of ZurichZurichSwitzerland

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