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European Radiology

, Volume 29, Issue 12, pp 6550–6558 | Cite as

Liver CT perfusion: which is the relevant delay that reduces radiation dose and maintains diagnostic accuracy?

  • Alessandro Bevilacqua
  • Silvia Malavasi
  • Valérie VilgrainEmail author
Computed Tomography
  • 97 Downloads

Abstract

Objectives

High radiation dose during CT perfusion (CTp) studies contributes to prevent CTp application in daily clinical practice. This work evaluates the consequences of scan delay on perfusion parameters and provides guidelines to help reducing the radiation dose by choosing the most appropriate delay.

Methods

Fifty-nine patients (34 men, 25 women; mean age 68 ± 12) with colorectal cancer, without underlying liver disease, underwent liver CTp, with the acquisition starting simultaneously with iodinated contrast agent injection. Blood flow (BF) and hepatic perfusion index (HPI) were computed on the acquired examinations and compared with those of the same examinations when a variable scan delay (τ) is introduced. Dose length product, CT dose index, and effective dose were also computed on original and delayed examinations.

Results

Altogether, three groups of delays (τ ≤ 4 s, 5 s ≤ τ ≤ 9 s, τ ≥ 10 s) were identified, yielding increasing radiation dose saving (RDS) (RDS ≤ 9.5%, 11.9% ≤ RDS ≤ 21.4%, RDS ≥ 23.8%) and decreasing perfusion accuracy (high (τ ≤ 4 s), medium (5 s ≤ τ ≤ 9 s), low (τ ≥ 10 s)). In particular, single-input and arterial BF and HPI were more insensitive to delay as regards the absolute variations (only 1 ml/min/100 g and 1%, respectively, for τ ≤ 9 s), than portal and total BF.

Conclusion

Using delays lower than 4 s does not change perfusion accuracy and conveys unnecessary dose to patients. Conversely, starting the acquisition 9 s after contrast agent injection yields a RDS of about 21%, with no significant losses in perfusion accuracy.

Key Points

• Scan delays lower than 4 s do not alter perfusion accuracy and deliver an unnecessary radiation dose to patients.

• Radiation dose delivered to patients can be reduced by 21.4% by introducing a 9-s scan delay, while keeping accurate perfusion values.

• Using scan delays higher than 10 s, some perfusion parameters (portal and total BF) were inaccurate.

Keywords

Contrast media Colorectal neoplasms Liver diseases Radiation dosage Tomography, X-ray computed 

Abbreviations

aBF

Arterial blood flow

ADC

Cohort-oriented absolute differences

ADP

Patient-oriented absolute differences

BF

Blood flow

CTDIvol

Volumetric CT dose index

CTp

CT perfusion

DLP

Dose length product

Ea

Examination acquired

Eτ

Delayed examination

ED

Effective dose

HPI

Hepatic perfusion index

N

Integer number for statistical differences

pBF

Portal blood flow

PDC

Cohort-oriented percentage differences

PDP

Patient-oriented percentage differences

p.u.

Perfusion unit

RDS

Radiation dose saved

tBF

Total blood flow

TCCs

Time concentration curves

Notes

Funding

This study has received funding by a grant from the Programme Hospitalier de Recherche Clinique - PHRC 2007 no. AOM07228, France, and sponsored by Assistance-Publique Hôpitaux de Paris (APHP).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Prof. Valérie Vilgrain.

Conflict of interest

The 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

Alessandro Bevilacqua, MS, PhD, kindly provided statistical advice for this manuscript and is one of the authors of this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

Supplementary material

330_2019_6259_MOESM1_ESM.docx (27 kb)
ESM 1 (DOCX 27 kb)

References

  1. 1.
    Oğul H, Kantarcı M, Genç B et al (2014) Perfusion CT imaging of the liver: review of clinical applications. Diagn Interv Radiol 20:379–389CrossRefGoogle Scholar
  2. 2.
    Kartalis N, Brehmer K, Loizou L (2017) Multi-detector CT: liver protocol and recent developments. Eur J Radiol 97:101–109CrossRefGoogle Scholar
  3. 3.
    Kim SH, Kamaya A, Willmann JK (2014) CT perfusion of the liver: principles and applications in oncology. Radiology 272:322–344CrossRefGoogle Scholar
  4. 4.
    Ippolito D, Querques G, Okolicsanyi S et al (2018) Dynamic contrast enhanced perfusion CT imaging: a diagnostic biomarker tool for survival prediction of tumour response to antiangiogenetic treatment in patients with advanced HCC lesions. Eur J Radiol 106:62–68CrossRefGoogle Scholar
  5. 5.
    Nakamura Y, Kawaoka T, Higaki T et al (2018) Hepatocellular carcinoma treated with sorafenib: arterial tumor perfusion in dynamic contrast-enhanced CT as early imaging biomarkers for survival. Eur J Radiol 98:41–49CrossRefGoogle Scholar
  6. 6.
    Horowitz JM, Venkatesh SK, Ehman RL et al (2017) Evaluation of hepatic fibrosis: a review from the society of abdominal radiology disease focus panel. Abdom Radiol (NY) 42:2037–2053CrossRefGoogle Scholar
  7. 7.
    Thaiss WM, Sannwald L, Kloth C et al (2018) Quantification of hemodynamic changes in chronic liver disease: correlation of perfusion-CT data with histopathologic staging of fibrosis. Acad Radiol.  https://doi.org/10.1016/j.acra.2018.11.009
  8. 8.
    De Robertis R, D’Onofrio M, Demozzi E, Crosara S, Canestrini S, Pozzi Mucelli R (2014) Noninvasive diagnosis of cirrhosis: a review of different imaging modalities. World J Gastroenterol 20:7231–7241CrossRefGoogle Scholar
  9. 9.
    Fischer MA, Marquez HP, Gordic S et al (2017) Arterio-portal shunts in the cirrhotic liver: perfusion computed tomography for distinction of arterialized pseudolesions from hepatocellular carcinoma. Eur Radiol 27:1074–1080CrossRefGoogle Scholar
  10. 10.
    Marquez HP, Karalli A, Haubenreisser H et al (2017) Computed tomography perfusion imaging for monitoring transarterial chemoembolization of hepatocellular carcinoma. Eur J Radiol 91:160–167CrossRefGoogle Scholar
  11. 11.
    Fischer MA, Kartalis N, Grigoriadis A et al (2015) Perfusion computed tomography for detection of hepatocellular carcinoma in patients with liver cirrhosis. Eur Radiol 25:3123–3132CrossRefGoogle Scholar
  12. 12.
    Ippolito D, Querques G, Okolicsanyi S, Franzesi CT, Strazzabosco M, Sironi S (2017) Diagnostic value of dynamic contrast-enhanced CT with perfusion imaging in the quantitative assessment of tumor response to sorafenib in patients with advanced hepatocellular carcinoma: a feasibility study. Eur J Radiol 90:34–41CrossRefGoogle Scholar
  13. 13.
    Perisinakis K, Tzedakis A, Pouli S, Spanakis K, Hatzidakis A, Damilakis J (2019) Comparison of patient dose from routine multi-phase and dynamic liver perfusion CT studies taking into account the effect of iodinated contrast administration. Eur J Radiol 110:39–44CrossRefGoogle Scholar
  14. 14.
    Marquez HP, Puippe G, Mathew RP, Alkadhi H, Pfammatter T, Fischer MA (2017) CT perfusion for early response evaluation of radiofrequency ablation of focal liver lesions: first experience. Cardiovasc Intervent Radiol 40:90–98CrossRefGoogle Scholar
  15. 15.
    Kalra MK, Sodickson AD, Mayo-Smith WW (2015) CT radiation: key concepts for gentle and wise use. Radiographics 35:1706–1721CrossRefGoogle Scholar
  16. 16.
    Lell MM, Wildberger JE, Alkadhi H, Damilakis J, Kachelriess M (2015) Evolution in computed tomography: the battle for speed and dose. Invest Radiol 50:629–644CrossRefGoogle Scholar
  17. 17.
    Ramirez-Giraldo JC, Thompson SM, Krishnamurthi G et al (2013) Evaluation of strategies to reduce radiation dose in perfusion CT imaging using a reproducible biologic phantom. AJR Am J Roentgenol 200:W621–W627CrossRefGoogle Scholar
  18. 18.
    Ng CS, Hobbs BP, Chandler AG et al (2013) Metastases to the liver from neuroendocrine tumors: effect of duration of scan acquisition on CT perfusion values. Radiology 269:758–767CrossRefGoogle Scholar
  19. 19.
    Ng CS, Chandler AG, Yao JC et al (2014) Effect of pre-enhancement set-point on CT perfusion values in normal liver and metastases to the liver from neuroendocrine tumors. J Comput Assist Tomogr 38:526–534CrossRefGoogle Scholar
  20. 20.
    Lee DH, Lee JM, Klotz E, Han JK (2016) Multiphasic dynamic computed tomography evaluation of liver tissue perfusion characteristics using the dual maximum slope model in patients with cirrhosis and hepatocellular carcinoma: a feasibility study. Invest Radiol 51:430–434CrossRefGoogle Scholar
  21. 21.
    Fischer MA, Brehmer K, Svensson A, Aspelin P, Brismar TB (2016) Renal versus splenic maximum slope based perfusion CT modelling in patients with portal-hypertension. Eur Radiol 26:4030–4036CrossRefGoogle Scholar
  22. 22.
    Kako Y, Yamakado K, Jomoto W et al (2017) Changes in liver perfusion and function before and after percutaneous occlusion of spontaneous portosystemic shunt. Jpn J Radiol 35:366–372CrossRefGoogle Scholar
  23. 23.
    Jiang T, Kambadakone A, Kulkarni NM, Zhu AX, Sahani DV (2012) 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 47:11–17CrossRefGoogle Scholar
  24. 24.
    Tamandl D, Waneck F, Sieghart W et al (2017) Early response evaluation using CT-perfusion one day after transarterial chemoembolization for HCC predicts treatment response and long-term disease control. Eur J Radiol 90:73–80CrossRefGoogle Scholar
  25. 25.
    Gill AB, Hilliard NJ, Hilliard ST, Graves MJ, Lomas DJ, Shaw A (2017) A semi-automatic method for the extraction of the portal venous input function in quantitative dynamic contrast-enhanced CT of the liver. Br J Radiol.  https://doi.org/10.1259/bjr.20160875
  26. 26.
    Mulé S, Pigneur F, Quelever R et al (2018) Can dual-energy CT replace perfusion CT for the functional evaluation of advanced hepatocellular carcinoma? Eur Radiol 28:1977–1985CrossRefGoogle Scholar
  27. 27.
    Miles KA, Hayball MP, Dixon AK (1993) Functional images of hepatic perfusion obtained with dynamic CT. Radiology 188:405–411CrossRefGoogle Scholar
  28. 28.
    Bevilacqua A, Barone D, Malavasi S, Gavelli G (2014) Quantitative assessment of effects of motion compensation for liver and lung tumors in CT perfusion. Acad Radiol 21:1416–1426CrossRefGoogle Scholar
  29. 29.
    Gibaldi A, Barone D, Gavelli G, Malavasi S, Bevilacqua A (2015) Effects of guided random sampling of TCCs on blood flow values in CT perfusion studies of lung tumors. Acad Radiol 22:58–69CrossRefGoogle Scholar
  30. 30.
    Blomley MJ, Coulden R, Dawson P et al (1995) Liver perfusion studied with ultrafast CT. J Comput Assist Tomogr 19:424–433CrossRefGoogle Scholar
  31. 31.
    Menzel H, Schibilla H, Teunen D (2000) European guidelines on quality criteria for computed tomography. European Commission Publication, Luxembourg No. EUR 16262 EN. Available via https://publications.europa.eu/en/publication-detail/-/publication/d229c9e1-a967-49de-b169-59ee68605f1a. Accessed 10 Mar 2019
  32. 32.
    Malavasi S, Barone D, Gavelli G, Bevilacqua A (2017) Multislice analysis of blood flow values in CT perfusion studies of lung cancer. Biomed Res Int 2017:3236893CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Alessandro Bevilacqua
    • 1
    • 2
  • Silvia Malavasi
    • 2
    • 3
  • Valérie Vilgrain
    • 4
    • 5
    Email author
  1. 1.DISI (Department of Computer Science and Engineering)University of BolognaBolognaItaly
  2. 2.ARCES (Advanced Research Center on Electronic Systems)University of BolognaBolognaItaly
  3. 3.CIG (Interdepartmental Centre “L. Galvani” for integrated studies of Bioinformatics, Biophysics and Biocomplexity)University of BolognaBolognaItaly
  4. 4.Department of Radiology, Assistance-Publique Hôpitaux de Paris, APHP, HUPNVSHôpital BeaujonClichyFrance
  5. 5.Sorbonne Paris Cité, INSERM CRIUniversité Paris DiderotParisFrance

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