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Intra-patient variability of iodine quantification across different dual-energy CT platforms: assessment of normalization techniques

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To investigate intra-patient variability of iodine concentration (IC) between three different dual-energy CT (DECT) platforms and to test different normalization approaches.

Methods

Forty-four patients who underwent portal venous phase abdominal DECT on a dual-source (dsDECT), a rapid kVp switching (rsDECT), and a dual-layer detector platform (dlDECT) during cancer follow-up were retrospectively included. IC in the liver, pancreas, and kidneys and different normalized ICs (NICPV:portal vein; NICAA:abdominal aorta; NICALL:overall iodine load) were compared between the three DECT scanners for each patient. A longitudinal mixed effects analysis was conducted to elucidate the effect of the scanner type, scan order, inter-scan time, and contrast media amount on normalized iodine concentration.

Results

Variability of IC was highest in the liver (dsDECT vs. dlDECT 28.96 (14.28–46.87) %, dsDECT vs. rsDECT 29.08 (16.59–62.55) %, rsDECT vs. dlDECT 22.85 (7.52–33.49) %), and lowest in the kidneys (dsDECT vs. dlDECT 15.76 (7.03–26.1) %, dsDECT vs. rsDECT 15.67 (8.86–25.56) %, rsDECT vs. dlDECT 10.92 (4.92–22.79) %). NICALL yielded the best reduction of IC variability throughout all tissues and inter-scanner comparisons, yet did not reduce the variability between dsDECT vs. dlDECT and rsDECT, respectively, in the liver. The scanner type remained a significant determinant for NICALL in the pancreas and the liver (F-values, 12.26 and 23.78; both, p < 0.0001).

Conclusions

We found tissue-specific intra-patient variability of IC across different DECT scanner types. Normalization mitigated variability by reducing physiological fluctuations in iodine distribution. After normalization, the scanner type still had a significant effect on iodine variability in the pancreas and liver.

Clinical relevance statement

Differences in iodine quantification between dual-energy CT scanners can partly be mitigated by normalization, yet remain relevant for specific tissues and inter-scanner comparisons, which should be taken into account at clinical routine imaging.

Key Points

• Iodine concentration showed the least variability between scanner types in the kidneys (range 10.92–15.76%) and highest variability in the liver (range 22.85–29.08%).

• Normalizing tissue-specific iodine concentrations against the overall iodine load yielded the greatest reduction of variability between scanner types for 2/3 inter-scanner comparisons in the liver and for all (3/3) inter-scanner comparisons in the kidneys and pancreas, respectively.

• However, even after normalization, the dual-energy CT scanner type was found to be the factor significantly influencing variability of iodine concentration in the liver and pancreas.

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Abbreviations

DECT:

Dual-energy CT

dlDECT:

Dual-layer detector dual-energy CT

dsDECT:

Dual-source dual-energy CT

HU:

Hounsfield units

IC:

Iodine concentration

keV:

Kiloelectron volt

kV:

Kilovolt

mGy:

Milligray

NICAA :

Iodine concentration normalized to the abdominal aorta

NICALL :

Iodine concentration normalized to the overall iodine load

NICPV :

Iodine concentration normalized to the portal vein

ROI:

Region of interest

rsDECT:

Rapid kV switching dual-energy CT

References

  1. McCollough CH, Leng S, Yu L, Fletcher JG (2015) Dual- and multi-energy CT: principles, technical approaches, and clinical applications. Radiology 276:637

    Article  PubMed  Google Scholar 

  2. Lewin M, Laurent-Bellue A, Desterke C et al (2022) Evaluation of perfusion CT and dual-energy CT for predicting microvascular invasion of hepatocellular carcinoma. Abdom Radiol (NY) 47:2115–2127. https://doi.org/10.1007/s00261-022-03511-7

    Article  PubMed  Google Scholar 

  3. 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 91. https://doi.org/10.1259/bjr.20170351

  4. Lennartz S, Le Blanc M, Zopfs D et al (2019) Dual-energy CT-derived iodine maps: use in assessing pleural carcinomatosis. Radiology 290:796–804. https://doi.org/10.1148/radiol.2018181567

    Article  PubMed  Google Scholar 

  5. Nagayama Y, Inoue T, Oda S et al (2020) Adrenal adenomas versus metastases: diagnostic performance of dual-energy spectral CT virtual noncontrast imaging and iodine maps. Radiology 192227. https://doi.org/10.1148/radiol.2020192227

  6. Marcon J, Graser A, Horst D et al (2020) Papillary vs clear cell renal cell carcinoma. Differentiation and grading by iodine concentration using DECT—correlation with microvascular density. Eur Radiol 30:1–10. https://doi.org/10.1007/s00330-019-06298-2

    Article  CAS  PubMed  Google Scholar 

  7. Altenbernd J, Wetter A, Forsting M, Umutlu L (2016) Treatment response after radioembolisation in patients with hepatocellular carcinoma—an evaluation with dual energy computed-tomography. Eur J Radiol Open 3:230–235. https://doi.org/10.1016/j.ejro.2016.08.002

    Article  PubMed  PubMed Central  Google Scholar 

  8. Meyer M, Hohenberger P, Overhoff D et al (2022) Dual-energy CT vital iodine tumor burden for response assessment in patients with metastatic GIST undergoing TKI therapy: comparison with standard CT and FDG PET/CT criteria. AJR Am J Roentgenol 218:659–669. https://doi.org/10.2214/AJR.21.26636

    Article  PubMed  Google Scholar 

  9. Hellbach K, Sterzik A, Sommer W et al (2017) Dual energy CT allows for improved characterization of response to antiangiogenic treatment in patients with metastatic renal cell cancer. Eur Radiol 27:2532–2537. https://doi.org/10.1007/s00330-016-4597-7

    Article  CAS  PubMed  Google Scholar 

  10. Zopfs D, Reimer RP, Sonnabend K et al (2021) Intraindividual consistency of iodine concentration in dual-energy computed tomography of the chest and abdomen. Invest Radiol 56:181–187. https://doi.org/10.1097/RLI.0000000000000724

    Article  CAS  PubMed  Google Scholar 

  11. Lennartz S, Abdullayev N, Zopfs D et al (2019) Intra-individual consistency of spectral detector CT-enabled iodine quantification of the vascular and renal blood pool. Eur Radiol 29:6581–6590. https://doi.org/10.1007/s00330-019-06266-w

    Article  Google Scholar 

  12. Cai LM, Hippe DS, Zamora DA et al (2022) A method for reducing variability across dual-energy CT manufacturers in quantification of low iodine content levels. AJR Am J Roentgenol 218:746–755. https://doi.org/10.2214/AJR.21.26714

    Article  PubMed  Google Scholar 

  13. Patel BN, Vernuccio F, Meyer M et al (2019) Dual-energy CT material density iodine quantification for distinguishing vascular from nonvascular renal lesions: normalization reduces intermanufacturer threshold variability. AJR Am J Roentgenol 212:366–376. https://doi.org/10.2214/AJR.18.20115

    Article  PubMed  Google Scholar 

  14. Majeed NF, Braschi Amirfarzan M, Wald C, Wortman JR (2021) Spectral detector CT applications in advanced liver imaging. Br J Radiol 94:20201290. https://doi.org/10.1259/bjr.20201290

    Article  PubMed  PubMed Central  Google Scholar 

  15. 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

    Article  PubMed  PubMed Central  Google Scholar 

  16. Jacobsen MC, Cressman ENK, Tamm EP et al (2019) Dual-energy CT: lower limits of iodine detection and quantification. Radiology 292:414–419. https://doi.org/10.1148/radiol.2019182870

    Article  PubMed  Google Scholar 

  17. 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

    Article  PubMed  Google Scholar 

  18. Sauter AP, Kopp FK, Münzel D et al (2018) Accuracy of iodine quantification in dual-layer spectral CT: influence of iterative reconstruction, patient habitus and tube parameters. Eur J Radiol 102:83–88. https://doi.org/10.1016/j.ejrad.2018.03.009

    Article  PubMed  Google Scholar 

  19. Bargellini I, Crocetti L, Turini FM et al (2018) Response assessment by volumetric iodine uptake measurement: preliminary experience in patients with intermediate-advanced hepatocellular carcinoma treated with Yttrium-90 radioembolization. Cardiovasc Intervent Radiol 41:1373–1383. https://doi.org/10.1007/s00270-018-1962-8

    Article  PubMed  Google Scholar 

  20. Lennartz S, Parakh A, Cao J et al (2021) Inter-scan and inter-scanner variation of quantitative dual-energy CT: evaluation with three different scanner types. Eur Radiol 31:4438–4451. https://doi.org/10.1007/s00330-020-07611-0

    Article  Google Scholar 

  21. King AP, Eckersley RJ (2019) Statistics for biomedical engineers and scientists. Academic Press. https://doi.org/10.1016/B978-0-08-102939-8.00008-6

  22. Carlström M, Wilcox CS, Arendshorst WJ (2015) Renal autoregulation in health and disease. Physiol Rev 95:405–511. https://doi.org/10.1152/physrev.00042.2012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Patino M, Prochowski A, Agrawal MD (2016) Material separation using dual-energy CT: current and emerging applications. Radiographics 36:1087

    Article  PubMed  Google Scholar 

  24. Lu X, Lu Z, Yin J et al (2019) Effects of radiation dose levels and spectral iterative reconstruction levels on the accuracy of iodine quantification and virtual monochromatic CT numbers in dual-layer spectral detector CT: an iodine phantom study. Quant Imaging Med Surg 9:188–200. https://doi.org/10.21037/qims.2018.11.12

    Article  PubMed  PubMed Central  Google Scholar 

  25. Dai X, Schlemmer H-P, Schmidt B et al (2013) Quantitative therapy response assessment by volumetric iodine-uptake measurement: initial experience in patients with advanced hepatocellular carcinoma treated with sorafenib. Eur J Radiol 82:327–334. https://doi.org/10.1016/j.ejrad.2012.11.013

    Article  PubMed  Google Scholar 

  26. Cai LM, Hippe DS, Zamora DA et al (2022) A method for reducing variability across dual-energy CT manufacturers in quantification of low iodine content levels. AJR Am J Roentgenol 218:746–755. https://doi.org/10.2214/AJR.21.26714

    Article  PubMed  Google Scholar 

  27. Obmann MM, Kelsch V, Cosentino A et al (2019) Interscanner and intrascanner comparison of virtual unenhanced attenuation values derived from twin beam dual-energy and dual-source, dual-energy computed tomography. Invest Radiol 54:1–6. https://doi.org/10.1097/RLI.0000000000000501

    Article  PubMed  Google Scholar 

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Funding

This study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; LE 4401/1-1 to Simon Lennartz (Project Number 426969820), and FI 773/15-1) and Philips (Grant Number 2018A006560 to Avinash Kambadakone).

Author information

Authors and Affiliations

  1. Department of Radiology, Abdominal Radiology Division, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA

    Simon Lennartz, Jinjin Cao, Nisanard Pisuchpen, Shravya Srinivas-Rao, Anushri Parakh, Peter F. Hahn & Avinash Kambadakone

  2. Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany

    Simon Lennartz

  3. Department of Radiology, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand

    Nisanard Pisuchpen

  4. Harvard Catalyst Biostatistical Unit, Harvard Medical School/Massachusetts General Hospital, Boston, MA, USA

    Joseph J. Locascio

  5. Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA

    Achille Mileto

  6. Department of Radiology, University of Washington, UWMC Radiology RR218, 1959 NE Pacific St, Seattle, WA, 98195, USA

    Dushyant Sahani

Authors
  1. Simon Lennartz
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  2. Jinjin Cao
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  4. Shravya Srinivas-Rao
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  5. Joseph J. Locascio
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  6. Anushri Parakh
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  7. Peter F. Hahn
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  10. Avinash Kambadakone
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Corresponding author

Correspondence to Avinash Kambadakone.

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Guarantor

The scientific guarantor of this publication is Avinash Kambadakone.

Conflict of interest

Dushyant Sahani: Payment or honoraria from Philips Healthcare, Canon Medical Systems.

Achille Mileto: Consulting fees from Bayer Healthcare.

Avinash Kambadakone: Research grant from Philips Healthcare, GE Healthcare, and PanCAN; AI advisory board for Bayer; honorarium from Philips Healthcare; course director for ACR Education Center Course; travel support from Siemens Healthcare for the SOMATOM Force Summit in 2019.

Simon Lennartz: Authorship and speaker honoraria from Amboss.

Simon Lennartz is a member of the European Radiology Editorial Board. He has not taken part in the review or selection process of this article.

Statistics and biometry

One of the authors has significant statistical expertise: Joseph J Locascio, Harvard Catalyst Biostatistical Unit.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

In a previous study from our group, DECT-derived virtual unenhanced images have been investigated in the same patient cohort (Lennartz S, Pisuchpen N, Parakh A, et al Virtual unenhanced images: qualitative and quantitative comparison between different dual-energy CT scanners in a patient and phantom study. Invest Radiol. 2022;57(1):52–61. https://doi.org/10.1097/RLI.0000000000000802).

In another study focusing on phantom-based correction methods for increasing inter-scanner consistency of iodine at low levels, data from 30 patients examined in this study were used as a validation cohort. (Cai et al: A method for reducing variability across dual-energy CT manufacturers in quantification of low iodine content levels, Am J Roentgenol., 2022 Apr;218(4):746-755. https://doi.org/10.2214/AJR.21.26714).

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Lennartz, S., Cao, J., Pisuchpen, N. et al. Intra-patient variability of iodine quantification across different dual-energy CT platforms: assessment of normalization techniques. Eur Radiol (2024). https://doi.org/10.1007/s00330-023-10560-z

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  • DOI: https://doi.org/10.1007/s00330-023-10560-z

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