CT Liver Imaging: What is New?

  • Nicolaus A. Wagner-BartakEmail author
  • Aran M. Toshav
  • Eric P. Tamm
  • Ott Le
  • Sheela Agarwal
  • Chaan Ng
  • Aliya Qayyum
Abdominal CT-An Update on Applications and New Developments (H S Teh, Section Editor)
Part of the following topical collections:
  1. Abdominal CT-An Update on Applications and New Developments


This review article aims to bring the reader up to date on advances in liver CT imaging, with an emphasis on the literature from the past year. Recent studies and developments in hepatic imaging using dual-energy CT, perfusion CT, low-tube-voltage imaging, and iterative reconstruction techniques are discussed.


Liver CT Dual-energy CT Perfusion CT Low-tube-voltage Iterative reconstruction Hepatocellular carcinoma 



Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. 1.
    Patel BN, Kumbla RA, Berland LL, Fineberg NS, Morgan DE. Material density hepatic steatosis quantification on intravenous contrast-enhanced rapid kilovolt (peak)-switching single-source dual-energy computed tomography. J Comput Assist Tomogr. 2013;37:904–10.CrossRefPubMedGoogle Scholar
  2. 2.
    • Joe E, Kim SH, Lee KB, et al. Feasibility and accuracy of dual-source dual-energy CT for noninvasive determination of hepatic iron accumulation. Radiology. 2012; 262:126–35. This study evaluated DECT for quantifying hepatic iron accumulation and showed that DECT performed on par with MRI. Google Scholar
  3. 3.
    Altenbernd J, Heusner T, Ringelstein A, Ladd S, Forsting M, Antoch G. Dual-energy-CT of hypervascular liver lesions in patients with HCC: investigation of image quality and sensitivity. Eur Radiol. 2011;21:738–43.CrossRefPubMedGoogle Scholar
  4. 4.
    Robinson E, Babb J, Chandarana H, MacAri M. Dual source dual energy MDCT: comparison of 80 kVp and weighted average 120 kVp data for conspicuity of hypo-vascular liver metastases. Invest Radiol. 2010;45:413–8.PubMedGoogle Scholar
  5. 5.
    Hayano K, Desai GS, Kambadakone AR, Fuentes JM, Tanabe KK, Sahani DV. Quantitative characterization of hepatocellular carcinoma and metastatic liver tumor by CT perfusion. Cancer Imaging. 2013;13:512–9.CrossRefPubMedCentralPubMedGoogle Scholar
  6. 6.
    Goh V, Ng QS, Miles K. Computed tomography perfusion imaging for therapeutic assessment: has it come of age as a biomarker in oncology? Invest Radiol. 2012;47:2–4.CrossRefPubMedGoogle Scholar
  7. 7.
    • Ippolito D, Fior D, Bonaffini PA, et al. Quantitative evaluation of CT-perfusion map as indicator of tumor response to transarterial chemoembolization and radiofrequency ablation in HCC patients. Eur J Radiol. 2014; 83:1665–71. This study showed that perfusion CT is a reproducible quantitative technique for the evaluation of treatment response of HCC following TACE and RFA. Google Scholar
  8. 8.
    Morsbach F, Sah BR, Spring L, et al. Perfusion CT best predicts outcome after radioembolization of liver metastases: a comparison of radionuclide and CT imaging techniques. Eur Radiol. 2014;24:1455–65.CrossRefPubMedGoogle Scholar
  9. 9.
    Reiner CS, Morsbach F, Sah BR, et al. Early treatment response evaluation after yttrium-90 radioembolization of liver malignancy with CT perfusion. J Vasc Interv Radiol. 2014;25:747–59.CrossRefPubMedGoogle Scholar
  10. 10.
    Hao XJ, Li JP, Jiang HJ, et al. CT assessment of liver hemodynamics in patients with hepatocellular carcinoma after argon–helium cryoablation. Hepatob Pancreat Dis. 2013;12:617–21.CrossRefGoogle Scholar
  11. 11.
    Morsbach F, Pfammatter T, Reiner CS, et al. Computed tomographic perfusion imaging for the prediction of response and survival to transarterial radioembolization of liver metastases. Invest Radiol. 2013;48:787–94.CrossRefPubMedGoogle Scholar
  12. 12.
    Kordolaimi SD, Argentos S, Pantos I, Kelekis NL, Efstathopoulos EP. A new era in computed tomographic dose optimization: the impact of iterative reconstruction on image quality and radiation dose. J Comput Assist Tomogr. 2013;37:924–31.CrossRefPubMedGoogle Scholar
  13. 13.
    Alvarez RE, Macovski A. Energy-selective reconstructions in X-ray computerized tomography. Phys Med Biol. 1976;21:733–44.CrossRefPubMedGoogle Scholar
  14. 14.
    Kalender WA, Perman WH, Vetter JR, Klotz E. Evaluation of a prototype dual-energy computed tomographic apparatus I. Phantom studies. Med Phys. 1986;13:334–9.CrossRefPubMedGoogle Scholar
  15. 15.
    Kaza RK, Platt JF, Cohan RH, Caoili EM, Al-Hawary MM, Wasnik A. Dual-energy CT with single- and dual-source scanners: current applications in evaluating the genitourinary tract. Radiographics. 2012;32:353–69.CrossRefPubMedGoogle Scholar
  16. 16.
    Lee JM, Yoon JH, Joo I, Woo HS. Recent advances in CT and MR imaging for evaluation of hepatocellular carcinoma. Liver Cancer. 2012;1:22–40.CrossRefPubMedCentralPubMedGoogle Scholar
  17. 17.
    De Cecco CN, Darnell A, Macias N, et al. Virtual unenhanced images of the abdomen with second-generation dual-source dual-energy computed tomography: image quality and liver lesion detection. Invest Radiol. 2013;48:1–9.CrossRefPubMedGoogle Scholar
  18. 18.
    • Marin D, Nelson RC, Samei E, et al. Hypervascular liver tumors: low tube voltage, high tube current multidetector CT during late hepatic arterial phase for detection–initial clinical experience. Radiology. 2009; 251:771–79. Low tube voltage (80 kVp), high tube current CT was shown to increase the conspicuity of malignant hypervascular liver tumors compared to standard 140 kVp CT, while reducing radiation dose. Google Scholar
  19. 19.
    Fletcher JG, Takahashi N, Hartman R, et al. Dual-energy and dual-source CT: is there a role in the abdomen and pelvis? Radiol Clin North Am. 2009;47:41–57.CrossRefPubMedGoogle Scholar
  20. 20.
    Johnson TR, Krauss B, Sedlmair M, et al. Material differentiation by dual energy CT: initial experience. Eur Radiol. 2007;17:1510–7.CrossRefPubMedGoogle Scholar
  21. 21.
    Graser A, Johnson TR, Bader M, et al. Dual energy CT characterization of urinary calculi: initial in vitro and clinical experience. Invest Radiol. 2008;43:112–9.CrossRefPubMedGoogle Scholar
  22. 22.
    Sahni VA, Shinagare AB, Silverman SG. Virtual unenhanced CT images acquired from dual-energy CT urography: accuracy of attenuation values and variation with contrast material phase. Clin Radiol. 2013;68:264–71.CrossRefPubMedGoogle Scholar
  23. 23.
    De Cecco CN, Darnell A, Rengo M, et al. Dual-energy CT: oncologic applications. AJR Am J Roentgenol. 2012;199:S98–105.CrossRefPubMedGoogle Scholar
  24. 24.
    Megibow AJ, Chandarana H, Hindman NM. Increasing the precision of CT measurements with dual-energy scanning. Radiology. 2014;272:618–21.CrossRefPubMedGoogle Scholar
  25. 25.
    Heye T, Nelson RC, Ho LM, Marin D, Boll DT. Dual-energy CT applications in the abdomen. AJR Am J Roentgenol. 2012;199:S64–70.CrossRefPubMedGoogle Scholar
  26. 26.
    • Agrawal MD, Pinho DF, Kulkarni NM, Hahn PF, Guimaraes AR, Sahani DV. Oncologic applications of dual-energy CT in the abdomen. Radiographics. 2014; 34:589–612. This review article discusses the principles of DECT including the utility of postprocessed image sets. The role of DECT for lesion characterization and oncologic treatment planning and monitoring is reviewed. Google Scholar
  27. 27.
    Morgan DE. Dual-energy CT of the abdomen. Abdom Imaging. 2014;39:108–34.CrossRefPubMedGoogle Scholar
  28. 28.
    Barrett T, Bowden DJ, Shaida N, et al. Virtual unenhanced second generation dual-source CT of the liver: is it time to discard the conventional unenhanced phase? Eur J Radiol. 2012;81:1438–45.CrossRefPubMedGoogle Scholar
  29. 29.
    Kim JE, Lee JM, Baek JH, Han JK, Choi BI. Initial assessment of dual-energy CT in patients with gallstones or bile duct stones: can virtual nonenhanced images replace true nonenhanced images? AJR Am J Roentgenol. 2012;198:817–24.CrossRefPubMedGoogle Scholar
  30. 30.
    Yeh BM, Shepherd JA, Wang ZJ, Teh HS, Hartman RP, Prevrhal S. Dual-energy and low-kVp CT in the abdomen. AJR Am J Roentgenol. 2009;193:47–54.CrossRefPubMedCentralPubMedGoogle Scholar
  31. 31.
    Schindera ST, Nelson RC, Mukundan S Jr, et al. Hypervascular liver tumors: low tube voltage, high tube current multi-detector row CT for enhanced detection—phantom study. Radiology. 2008;246:125–32.CrossRefPubMedGoogle Scholar
  32. 32.
    Agrawal MD, Agarwal S, Fuentes-Oreego JM, Hayano K, Sahani DV. New liver imaging techniques. Curr Radiol Rep. 2013;1:294–306.CrossRefGoogle Scholar
  33. 33.
    Graser A, Johnson TR, Chandarana H, Macari M. Dual energy CT: preliminary observations and potential clinical applications in the abdomen. Eur Radiol. 2009;19:13–23.CrossRefPubMedGoogle Scholar
  34. 34.
    Meyer M, Hohenberger P, Apfaltrer P, et al. CT-based response assessment of advanced gastrointestinal stromal tumor: dual energy CT provides a more predictive imaging biomarker of clinical benefit than RECIST or Choi criteria. Eur J Radiol. 2013;82:923–8.CrossRefPubMedGoogle Scholar
  35. 35.
    Apfaltrer P, Meyer M, Meier C, et al. Contrast-enhanced dual-energy CT of gastrointestinal stromal tumors: is iodine-related attenuation a potential indicator of tumor response? Invest Radiol. 2012;47:65–70.CrossRefPubMedGoogle Scholar
  36. 36.
    Sudarski S, Apfaltrer P, Nance JW Jr, et al. Objective and subjective image quality of liver parenchyma and hepatic metastases with virtual monoenergetic dual-source dual-energy CT reconstructions: an analysis in patients with gastrointestinal stromal tumor. Acad Radiol. 2014;21:514–22.CrossRefPubMedGoogle Scholar
  37. 37.
    • Shuman WP, Green DE, Busey JM, et al. Dual-energy liver CT: effect of monochromatic imaging on lesion detection, conspicuity, and contrast-to-noise ratio of hypervascular lesions on late arterial phase. AJR Am J Roentgenol. 2014; 203:601–6. This prospective study assessed the optimal DECT monochromatic image set for the evaluation of hyperenhancing liver lesions in the arterial phase. The results showed the greatest subjective lesion conspicuity and highest CNR at 50 keV, but without a statistically significant increase in lesions detected compared to the 77-keV images. Google Scholar
  38. 38.
    Yamada Y, Jinzaki M, Tanami Y, Abe T, Kuribayashi S. Virtual monochromatic spectral imaging for the evaluation of hypovascular hepatic metastases: the optimal monochromatic level with fast kilovoltage switching dual-energy computed tomography. Invest Radiol. 2012;47:292–8.CrossRefPubMedGoogle Scholar
  39. 39.
    Targher G, Day CP, Bonora E. Risk of cardiovascular disease in patients with nonalcoholic fatty liver disease. New Engl J Med. 2010;363:1341–50.CrossRefPubMedGoogle Scholar
  40. 40.
    Lazo M, Hernaez R, Eberhardt MS, et al. Prevalence of nonalcoholic fatty liver disease in the United States: the Third National Health and Nutrition Examination Survey, 1988–1994. Am J Epidemiol. 2013;178:38–45.CrossRefPubMedCentralPubMedGoogle Scholar
  41. 41.
    Pickhardt PJ, Park SH, Hahn L, Lee S-G, Bae KT, Yu ES. Specificity of unenhanced CT for non-invasive diagnosis of hepatic steatosis: implications for the investigation of the natural history of incidental steatosis. Eur Radiol. 2012;22:1075–82.CrossRefPubMedGoogle Scholar
  42. 42.
    Hur BY, Lee JM, Hyunsik W, et al. Quantification of the fat fraction in the liver using dual-energy computed tomography and multimaterial decomposition. J Comput Assist Tomogr. 2014;38(6):845–52.CrossRefPubMedGoogle Scholar
  43. 43.
    Sun T, Lin X, Chen K. Evaluation of hepatic steatosis using dual-energy CT with MR comparison. Front Biosci. 2014;19:1377.CrossRefGoogle Scholar
  44. 44.
    Artz NS, Hines CD, Brunner ST, et al. Quantification of hepatic steatosis with dual-energy computed tomography: comparison with tissue reference standards and quantitative magnetic resonance imaging in the ob/ob mouse. Invest Radiol. 2012;47:603.CrossRefPubMedCentralPubMedGoogle Scholar
  45. 45.
    Fischer MA, Gnannt R, Raptis D, et al. Quantification of liver fat in the presence of iron and iodine: an ex vivo dual-energy CT study. Invest Radiol. 2011;46:351–8. doi: 10.1097/RLI.1090b1013e31820e31486.CrossRefPubMedGoogle Scholar
  46. 46.
    Ramm GA, Ruddell RG. Hepatotoxicity of iron overload: mechanisms of iron-induced hepatic fibrogenesis. Semin Liver Dis. 2005;25:433–49.CrossRefPubMedGoogle Scholar
  47. 47.
    Alla V, Bonkovsky HL. Iron in nonhemochromatotic liver disorders. Semin Liver Dis. 2005;25:461–72.CrossRefPubMedGoogle Scholar
  48. 48.
    Tsai YS, Chen JS, Wang CK, et al. Quantitative assessment of iron in heart and liver phantoms using dual-energy computed tomography. Exp Ther Med. 2014;8:907–12.PubMedCentralPubMedGoogle Scholar
  49. 49.
    Jiang T, Kambadakone A, Kulkarni NM, Zhu AX, Sahani DV. Monitoring response to antiangiogenic treatment and predicting outcomes in advanced hepatocellular carcinoma using image biomarkers, CT perfusion, tumor density, and tumor size (RECIST). Invest Radiol. 2012;47:11–7.CrossRefPubMedGoogle Scholar
  50. 50.
    • Kim SH, Kamaya A, Willmann JK. CT perfusion of the liver: principles and applications in oncology. Radiology. 2014; 272:322–44. A comprehensive review article that discusses perfusion CT of the liver in oncology. Technical details of perfusion imaging including the various analytic methods and models for calculating perfusion indices are described. The article elaborates upon the role of perfusion CT for the early detection of liver tumors, assessment of prognosis, monitoring of therapy, and the diagnosis of tumor recurrence. Google Scholar
  51. 51.
    Lee JM, Yoon J-H, Kim KW. Diagnosis of hepatocellular carcinoma: newer radiological tools. Semin Oncol. 2012;39:399–409.CrossRefPubMedGoogle Scholar
  52. 52.
    Hayano K, Fuentes-Orrego JM, Sahani DV. New approaches for precise response evaluation in hepatocellular carcinoma. World J Gastroenterol. 2014;20:3059–68.CrossRefPubMedCentralPubMedGoogle Scholar
  53. 53.
    Ogul H, Kantarci M, Genc B, et al. Perfusion CT imaging of the liver: review of clinical applications. Diagn Interv Radiol. 2014;20:379–89.Google Scholar
  54. 54.
    Negi N, Yoshikawa T, Ohno Y, et al. Hepatic CT perfusion measurements: a feasibility study for radiation dose reduction using new image reconstruction method. Eur J Radiol. 2012;81:3048–54.CrossRefPubMedGoogle Scholar
  55. 55.
    Jensen NK, Lock M, Fisher B, et al. Prediction and reduction of motion artifacts in free-breathing dynamic contrast enhanced CT perfusion imaging of primary and metastatic intrahepatic tumors. Acad Radiol. 2013;20:414–22.CrossRefPubMedGoogle Scholar
  56. 56.
    Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127:2893–917.CrossRefPubMedGoogle Scholar
  57. 57.
    Ramsey DE, Kernagis LY, Soulen MC, Geschwind JF. Chemoembolization of hepatocellular carcinoma. J Vasc Interv Radiol. 2002;13:S211–21.CrossRefPubMedGoogle Scholar
  58. 58.
    Shiina S, Tateishi R, Arano T, et al. Radiofrequency ablation for hepatocellular carcinoma: 10-year outcome and prognostic factors. Am J Gastroenterol. 2012;107:569–77.CrossRefPubMedCentralPubMedGoogle Scholar
  59. 59.
    Kinugasa H, Nouso K, Takeuchi Y, et al. Risk factors for recurrence after transarterial chemoembolization for early-stage hepatocellular carcinoma. J Gastroenterol. 2012;47:421–6.CrossRefPubMedGoogle Scholar
  60. 60.
    Frampas E, Lassau N, Zappa M, Vullierme MP, Koscielny S, Vilgrain V. Advanced hepatocellular carcinoma: early evaluation of response to targeted therapy and prognostic value of perfusion CT and dynamic contrast enhanced-ultrasound. Preliminary results. Eur J Radiol. 2013;82:e205–11.CrossRefPubMedGoogle Scholar
  61. 61.
    Sacco R, Faggioni L, Bargellini I, et al. Assessment of response to sorafenib in advanced hepatocellular carcinoma using perfusion computed tomography: results of a pilot study. Dig Liver Dis. 2013;45:776–81.CrossRefPubMedGoogle Scholar
  62. 62.
    de Berrington Gonzalez A, Mahesh M, Kim KP, et al. Projected cancer risks from computed tomographic scans performed in the United States in 2007. Arch Intern Med. 2009;169:2071–7.CrossRefGoogle Scholar
  63. 63.
    Mettler FA Jr, Bhargavan M, Faulkner K, et al. Radiologic and nuclear medicine studies in the United States and worldwide: frequency, radiation dose, and comparison with other radiation sources–1950-2007. Radiology. 2009;253:520–31.CrossRefPubMedGoogle Scholar
  64. 64.
    Maldjian PD, Goldman AR. Reducing radiation dose in body CT: a primer on dose metrics and key CT technical parameters. AJR Am J Roentgenol. 2013;200:741–7.CrossRefPubMedGoogle Scholar
  65. 65.
    National Research Council, Committee on Health Effects of Exposure to Low Levels of Ionizing Radiations (BEIR VII). Health effects of exposure to low levels of ionizing radiations: time for reassessment? Washington DC: National Academy Press; 1998.Google Scholar
  66. 66.
    Mettler FA, Huda W, Yoshizumi TT, Mahesh M. Effective doses in radiology and diagnostic nuclear medicine: a catalog. Radiology. 2008;248:254–63.CrossRefPubMedGoogle Scholar
  67. 67.
    Ehman EC, Guimaraes LS, Fidler JL, et al. Noise reduction to decrease radiation dose and improve conspicuity of hepatic lesions at contrast-enhanced 80-kV hepatic CT using projection space denoising. AJR Am J Roentgenol. 2012;198:405–11.CrossRefPubMedGoogle Scholar
  68. 68.
    Hur S, Lee JM, Kim SJ, Park JH, Han JK, Choi BI. 80-kVp CT using Iterative Reconstruction in Image Space algorithm for the detection of hypervascular hepatocellular carcinoma: phantom and initial clinical experience. Korean J Radiol. 2012;13:152–64.CrossRefPubMedCentralPubMedGoogle Scholar
  69. 69.
    Marin D, Nelson RC, Schindera ST, et al. Low-tube-voltage, high-tube-current multidetector abdominal CT: improved image quality and decreased radiation dose with adaptive statistical iterative reconstruction algorithm–initial clinical experience. Radiology. 2010;254:145–53.CrossRefPubMedGoogle Scholar
  70. 70.
    Lee KH, Lee JM, Moon SK, et al. Attenuation-based automatic tube voltage selection and tube current modulation for dose reduction at contrast-enhanced liver CT. Radiology. 2012;265:437–47.CrossRefPubMedGoogle Scholar
  71. 71.
    Park JH, Kim SH, Park HS, et al. Added value of 80 kVp images to averaged 120 kVp images in the detection of hepatocellular carcinomas in liver transplantation candidates using dual-source dual-energy MDCT: results of JAFROC analysis. Eur J Radiol. 2011;80:e76–85.CrossRefPubMedGoogle Scholar
  72. 72.
    Raman SP, Johnson PT, Deshmukh S, Mahesh M, Grant KL, Fishman EK. CT dose reduction applications: available tools on the latest generation of CT scanners. J Am Coll Radiol. 2013;10:37–41.CrossRefPubMedGoogle Scholar
  73. 73.
    Hur BY, Lee JM, Joo I, et al. Liver computed tomography with low tube voltage and model-based iterative reconstruction algorithm for hepatic vessel evaluation in living liver donor candidates. J Comput Assist Tomogr. 2014;38:367–75.CrossRefPubMedGoogle Scholar
  74. 74.
    Marin D, Choudhury KR, Gupta RT, et al. Clinical impact of an adaptive statistical iterative reconstruction algorithm for detection of hypervascular liver tumours using a low tube voltage, high tube current MDCT technique. Eur Radiol. 2013;23:3325–35.CrossRefPubMedGoogle Scholar
  75. 75.
    Husarik DB, Schindera ST, Morsbach F, et al. Combining automated attenuation-based tube voltage selection and iterative reconstruction: a liver phantom study. Eur Radiol. 2014;24:657–67.CrossRefPubMedGoogle Scholar
  76. 76.
    Yu MH, Lee JM, Yoon JH, et al. Low tube voltage intermediate tube current liver MDCT: sinogram-affirmed iterative reconstruction algorithm for detection of hypervascular hepatocellular carcinoma. AJR Am J Roentgenol. 2013;201:23–32.CrossRefPubMedGoogle Scholar
  77. 77.
    Namimoto T, Oda S, Utsunomiya D, et al. Improvement of image quality at low-radiation dose and low-contrast material dose abdominal CT in patients with cirrhosis: intraindividual comparison of low tube voltage with iterative reconstruction algorithm and standard tube voltage. J Comput Assist Tomogr. 2012;36:495–501.CrossRefPubMedGoogle Scholar
  78. 78.
    Yasaka K, Katsura M, Akahane M, Sato J, Matsuda I, Ohtomo K. Dose-reduced CT with model-based iterative reconstruction in evaluations of hepatic steatosis: how low can we go? Eur J Radiol. 2014;83:1063–8.CrossRefPubMedGoogle Scholar
  79. 79.
    Vardhanabhuti V, Loader RJ, Mitchell GR, Riordan RD, Roobottom CA. Image quality assessment of standard- and low-dose chest CT using filtered back projection, adaptive statistical iterative reconstruction, and novel model-based iterative reconstruction algorithms. AJR Am J Roentgenol. 2013;200:545–52.CrossRefPubMedGoogle Scholar
  80. 80.
    Volders D, Bols A, Haspeslagh M, Coenegrachts K. Model-based iterative reconstruction and adaptive statistical iterative reconstruction techniques in abdominal CT: comparison of image quality in the detection of colorectal liver metastases. Radiology. 2013;269:469–74.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Nicolaus A. Wagner-Bartak
    • 1
    Email author
  • Aran M. Toshav
    • 2
  • Eric P. Tamm
    • 1
  • Ott Le
    • 1
  • Sheela Agarwal
    • 3
  • Chaan Ng
    • 1
  • Aliya Qayyum
    • 1
  1. 1.Division of Diagnostic Imaging, Department of Diagnostic RadiologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  2. 2.LSUHSC New OrleansNew OrleansUSA
  3. 3.Division of Abdominal Imaging and Intervention, Department of RadiologyMassachusetts General HospitalBostonUSA

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