European Radiology

, Volume 29, Issue 5, pp 2226–2232 | Cite as

Dual-energy CT in early acute pancreatitis: improved detection using iodine quantification

  • Simon S. Martin
  • Franziska Trapp
  • Julian L. WichmannEmail author
  • Moritz H. Albrecht
  • Lukas Lenga
  • James Durden
  • Christian Booz
  • Thomas J. Vogl
  • Tommaso D’Angelo
Computed Tomography



To evaluate the diagnostic performance of a dual-energy computed tomography (DECT)-based technique using iodine quantification and fat fraction analysis for the diagnosis of early acute pancreatitis


In this retrospective study, 45 patients (35 men and 10 women; mean age, 54.9 ± 14.0 years) with early acute pancreatitis were included. Serum lipase levels and follow-up examinations served as the reference standard. A matched control group (n = 45) was assembled for evaluation of material decomposition values of normal pancreatic parenchyma. Three blinded radiologists independently interpreted all cases on conventional grayscale DECT series. In addition, readers re-evaluated all cases by manually performing region-of-interest (ROI) measurements on pancreatic-phase DECT material density images of the head, body, and tail of each patient’s pancreas. Receiver operating characteristic (ROC) curve analysis was performed to estimate the optimal threshold for discriminating between inflammatory and normal pancreas parenchyma.


DECT-based iodine density values showed significant differences between inflammatory (1.8 ± 0.3 mg/mL) and normal pancreatic parenchyma (2.7 ± 0.7 mg/mL) (p ≤ 0.01). Fat fraction measurements showed no significant differences (p = 0.08). The optimal iodine density threshold for the diagnosis of acute pancreatitis was 2.1 mg/mL with a sensitivity of 96% and specificity of 77%. Iodine quantification revealed an area under the curve (AUC) of 0.86, significantly higher compared to standard image evaluation of the radiologists (AUC, 0.80; sensitivity, 78%; specificity, 82%) (p < 0.01).


DECT using iodine quantification allows for diagnosis of early acute pancreatitis with higher sensitivity compared to standard image evaluation.

Key Points

• Iodine density values showed significant differences between inflammatory and normal pancreatic parenchyma.

• DECT using iodine quantification allows for diagnosis of early acute pancreatitis.

• An iodine density of ≤ 2.1 mg/mL optimizes the diagnosis of acute pancreatitis.


Multidetector computed tomography Pancreas Pancreatitis Iodine Diagnostic imaging 



Area under the curve


Computed tomography


Dual-energy computed tomography


Receiver operating characteristics


Region of interest



The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Dr. Julian L. Wichmann.

Conflict of interest

Dr. Julian L. Wichmann received speakers’ fees from GE Healthcare and Siemens Healthcare. However, all data was controlled by the authors (e.g., the corresponding author) without any potential conflict of interest. The other authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

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.


• Retrospective

• Diagnostic or prognostic study

• Performed at one institution


  1. 1.
    Balthazar EJ, Robinson DL, Megibow AJ, Ranson JH (1990) Acute pancreatitis: value of CT in establishing prognosis. Radiology 174:331–336CrossRefGoogle Scholar
  2. 2.
    Balthazar EJ (2002) Acute pancreatitis: assessment of severity with clinical and CT evaluation. Radiology 223:603–613CrossRefGoogle Scholar
  3. 3.
    Bradley EL 3rd (1993) A clinically based classification system for acute pancreatitis: summary of the International Symposium on Acute Pancreatitis, Atlanta, Ga, September 11 through 13, 1992. Arch Surg 128:586–590Google Scholar
  4. 4.
    Mortele KJ, Wiesner W, Intriere L et al (2004) A modified CT severity index for evaluating acute pancreatitis: improved correlation with patient outcome. AJR Am J Roentgenol 183:1261–1265CrossRefGoogle Scholar
  5. 5.
    Johnson TR, Krauss B, Sedlmair M et al (2007) Material differentiation by dual energy CT: initial experience. Eur Radiol 17:1510–1517CrossRefGoogle Scholar
  6. 6.
    Sofue K, Itoh T, Takahashi S et al (2018) Quantification of cisplatin using a modified 3-material decomposition algorithm at third-generation dual-source dual-energy computed tomography: an experimental study. Invest Radiol 53:673–680Google Scholar
  7. 7.
    Baxa J, Matouskova T, Krakorova G et al (2016) Dual-phase dual-energy CT in patients treated with erlotinib for advanced non-small cell lung cancer: possible benefits of iodine quantification in response assessment. Eur Radiol 26:2828–2836CrossRefGoogle Scholar
  8. 8.
    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 273:813–820CrossRefGoogle Scholar
  9. 9.
    Mileto A, Nelson RC, Marin D, Roy Choudhury K, Ho LM (2015) Dual-energy multidetector CT for the characterization of incidental adrenal nodules: diagnostic performance of contrast-enhanced material density analysis. Radiology 274:445–454CrossRefGoogle Scholar
  10. 10.
    Yin Q, Zou X, Zai X et al (2015) Pancreatic ductal adenocarcinoma and chronic mass-forming pancreatitis: differentiation with dual-energy MDCT in spectral imaging mode. Eur J Radiol 84:2470–2476CrossRefGoogle Scholar
  11. 11.
    Banks PA, Bollen TL, Dervenis C et al (2013) Classification of acute pancreatitis—2012: revision of the Atlanta classification and definitions by international consensus. Gut 62:102–111CrossRefGoogle Scholar
  12. 12.
    Thoeni RF (2012) The revised Atlanta classification of acute pancreatitis: its importance for the radiologist and its effect on treatment. Radiology 262:751–764CrossRefGoogle Scholar
  13. 13.
    Fletcher JG, Wiersema MJ, Farrell MA et al (2003) Pancreatic malignancy: value of arterial, pancreatic, and hepatic phase imaging with multi–detector row CT. Radiology 229:81–90CrossRefGoogle Scholar
  14. 14.
    Martin SS, Pfeifer S, Wichmann JL et al (2017) Noise-optimized virtual monoenergetic dual-energy computed tomography: optimization of kiloelectron volt settings in patients with gastrointestinal stromal tumors. Abdom Radiol (NY) 42:718–726CrossRefGoogle Scholar
  15. 15.
    Martin SS, Wichmann JL, Weyer H et al (2017) Endoleaks after endovascular aortic aneurysm repair: improved detection with noise-optimized virtual monoenergetic dual-energy CT. Eur J Radiol 94:125–132CrossRefGoogle Scholar
  16. 16.
    Albrecht MH, Trommer J, Wichmann JL et al (2016) Comprehensive comparison of virtual monoenergetic and linearly blended reconstruction techniques in third-generation dual-source dual-energy computed tomography angiography of the thorax and abdomen. Invest Radiol 51:582–590CrossRefGoogle Scholar
  17. 17.
    Mileto A, Mazziotti S, Gaeta M et al (2012) Pancreatic dual-source dual-energy CT: is it time to discard unenhanced imaging? Clin Radiol 67:334–339CrossRefGoogle Scholar
  18. 18.
    Balthazar EJ, Ranson JH, Naidich DP, Megibow AJ, Caccavale R, Cooper MM (1985) Acute pancreatitis: prognostic value of CT. Radiology 156:767–772CrossRefGoogle Scholar
  19. 19.
    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefGoogle Scholar
  20. 20.
    Ochodo EA, de Haan MC, Reitsma JB, Hooft L, Bossuyt PM, Leeflang MM (2013) Overinterpretation and misreporting of diagnostic accuracy studies: evidence of “spin”. Radiology 267:581–588CrossRefGoogle Scholar
  21. 21.
    Frellesen C, Fessler F, Hardie AD et al (2015) Dual-energy CT of the pancreas: improved carcinoma-to-pancreas contrast with a noise-optimized monoenergetic reconstruction algorithm. Eur J Radiol 84:2052–2058CrossRefGoogle Scholar
  22. 22.
    Patel BN, Thomas JV, Lockhart ME, Berland LL, Morgan DE (2013) Single-source dual-energy spectral multidetector CT of pancreatic adenocarcinoma: optimization of energy level viewing significantly increases lesion contrast. Clin Radiol 68:148–154CrossRefGoogle Scholar
  23. 23.
    Wichmann JL, Majenka P, Beeres M et al (2014) Single-portal-phase low-tube-voltage dual-energy CT for short-term follow-up of acute pancreatitis: evaluation of CT severity index, interobserver agreement and radiation dose. Eur Radiol 24:2927–2935CrossRefGoogle Scholar
  24. 24.
    Tawfik AM, Razek AA, Kerl JM, Nour-Eldin NE, Bauer R, Vogl TJ (2014) Comparison of dual-energy CT-derived iodine content and iodine overlay of normal, inflammatory and metastatic squamous cell carcinoma cervical lymph nodes. Eur Radiol 24:574–580CrossRefGoogle Scholar
  25. 25.
    Rizzo S, Radice D, Femia M et al (2018) Metastatic and non-metastatic lymph nodes: quantification and different distribution of iodine uptake assessed by dual-energy CT. Eur Radiol 28:760–769CrossRefGoogle Scholar
  26. 26.
    Li J, Fang M, Wang R et al (2018) Diagnostic accuracy of dual-energy CT-based nomograms to predict lymph node metastasis in gastric cancer. Eur Radiol.
  27. 27.
    Eibl G, Hotz HG, Faulhaber J, Kirchengast M, Buhr HJ, Foitzik T (2000) Effect of endothelin and endothelin receptor blockade on capillary permeability in experimental pancreatitis. Gut 46:390–394CrossRefGoogle Scholar
  28. 28.
    Bollen TL, Singh VK, Maurer R et al (2012) A comparative evaluation of radiologic and clinical scoring systems in the early prediction of severity in acute pancreatitis. Am J Gastroenterol 107:612–619CrossRefGoogle Scholar
  29. 29.
    Spanier BW, Nio Y, van der Hulst RW, Tuynman HA, Dijkgraaf MG, Bruno MJ (2010) Practice and yield of early CT scan in acute pancreatitis: a Dutch observational multicenter study. Pancreatology 10:222–228CrossRefGoogle Scholar
  30. 30.
    Boland GW, O'Malley ME, Saez M, Fernandez-del-Castillo C, Warshaw AL, Mueller PR (1999) Pancreatic-phase versus portal vein-phase helical CT of the pancreas: optimal temporal window for evaluation of pancreatic adenocarcinoma. AJR Am J Roentgenol 172:605–608CrossRefGoogle Scholar
  31. 31.
    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 53:321–327CrossRefGoogle Scholar
  32. 32.
    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 27:3904–3912CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Simon S. Martin
    • 1
    • 2
  • Franziska Trapp
    • 1
  • Julian L. Wichmann
    • 1
    Email author
  • Moritz H. Albrecht
    • 1
  • Lukas Lenga
    • 1
  • James Durden
    • 2
  • Christian Booz
    • 1
  • Thomas J. Vogl
    • 1
  • Tommaso D’Angelo
    • 1
    • 3
  1. 1.Division of Experimental and Translational Imaging, Department of Diagnostic and Interventional RadiologyUniversity Hospital FrankfurtFrankfurtGermany
  2. 2.Department of Radiology and Radiological ScienceMedical University of South CarolinaCharlestonUSA
  3. 3.Section of Radiological Sciences, Department of Biomedical Sciences and Morphological and Functional ImagingPoliclinico G. Martino—University Hospital MessinaMessinaItaly

Personalised recommendations