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CT liver perfusion in patients with hepatocellular carcinoma: can we modify acquisition protocol to reduce patient exposure?

  • Computed Tomography
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Abstract

Objectives

To investigate the potential of decreasing the number of scans and associated radiation exposure involved in CT liver perfusion (CTLP) dynamic studies for hepatocellular carcinoma (HCC) assessment.

Methods

Twenty-four CTLP image datasets of patients with HCC were retrospectively analyzed. All examinations were performed on a modern CT system using a standard acquisition protocol involving 35 scans with 1.7 s interval. A deconvolution-based or a standard algorithm was employed to compute ten perfusion parametric maps. 3D ROIs were positioned on 33 confirmed HCCs and non-malignant parenchyma. Analysis was repeated for two subsampled datasets generated from the original dataset by including only the (a) 18 odd-numbered scans with 3.4 s interval and (b) 18 first scans with 1.7 s interval. Standard and modified datasets were compared regarding the (a) accuracy of calculated perfusion parameters, (b) power of parametric maps to discriminate HCCs from liver parenchyma, and (c) associated radiation exposure.

Results

When the time interval between successive scans was doubled, perfusion parameters of HCCs were found unaffected (p > 0.05) and the discriminating efficiency of parametric maps was preserved (p < 0.05). In contrast, significant differences were found for all perfusion parameters of HCCs when acquisition duration was reduced to half (p < 0.05), while the discriminating efficiency of four parametric maps was significantly deteriorated (p < 0.05). Modified CTLP acquisition protocols were found to involve 48.5% less patient exposure.

Conclusions

Doubling the interscan time interval may considerably reduce radiation exposure from CTLP studies performed for HCC evaluation without affecting the diagnostic efficiency of perfusion maps generated with either standard or deconvolution-based mathematical model.

Key Points

• CT liver perfusion for HCC diagnosis/assessment is not routinely used in clinical practice mainly due to the associated high radiation exposure.

• Two alternative acquisition protocols involving 18 scans of the liver were compared with the standard 35-scan protocol.

• Increasing the time interval between successive scans to 3.4 s was found to preserve the accuracy of computed perfusion parameters derived with a standard or a deconvolution-based model and to reduce radiation exposure by 48.5%.

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Abbreviations

AUC:

Area under the ROC curve

BF:

Blood flow

BV:

Blood volume

CTLP:

Computed tomography liver perfusion

DLP:

Dose length product

DSA:

Digital subtraction angiography

EASL:

European Association for the Study of the Liver

ED:

Effective dose

HaBF:

Hepatic arterial blood flow

HAF:

Hepatic arterial fraction

HBV:

Hepatitis B virus

HCV:

Hepatitis C virus

IRF:

Impulse residue function

IRF-T0:

Contrast arrival delay

IQR:

Interquartile range

MSI:

Mean slope of increase

MTT:

Mean transit time

PEI:

Positive enhancement integral

PS:

Permeability surface

REML:

Restricted maximum likelihood

Tmax:

Transit time to impulse residue peak

TTP:

Time to peak

References

  1. Liapi E, Mahesh M, Sahani DV (2015) Is CT perfusion ready for liver cancer treatment evaluation? J Am Coll Radiol 12:111–113

    Article  Google Scholar 

  2. Ippolito D, Pecorelli A, Querques G et al (2019) Dynamic computed tomography perfusion imaging: complementary diagnostic tool in hepatocellular carcinoma assessment from diagnosis to treatment follow-up. Acad Radiol 26:1675–1685

    Article  Google Scholar 

  3. Kim SH, Kamaya A, Willmann JK (2014) CT perfusion of the liver: principles and applications in oncology. Radiology 272:322–344

    Article  Google Scholar 

  4. Miles KA, Hayball MP, Dixon AK (1993) Functional images of hepatic perfusion obtained with dynamic CT. Radiology 188:405–411

  5. Materne R, Van Beers BE, Smith AM et al (2000) Non-invasive quantification of liver perfusion with dynamic computed tomography and a dual-input one-compartmental model. Clin Sci (Lond) 99:517–525

    Article  CAS  Google Scholar 

  6. Lee TY, Purdie TG, Stewart E (2003) CT imaging of angiogenesis. Q J Nucl Med 47:171–87

  7. Miles KA, Lee TY, Goh V et al (2012) Current status and guidelines for the assessment of tumour vascular support with dynamic contrast-enhanced computed tomography. Eur Radiol 22:1430–1441

    Article  CAS  Google Scholar 

  8. 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 110:39–44

    Article  Google Scholar 

  9. Topcuoglu OM, Karçaaltincaba M, Akata D, Özmen MN (2016) Reproducibility and variability of very low dose hepatic perfusion CT in metastatic liver disease. Diagn Interv Radiol 22:495–500

    Article  Google Scholar 

  10. Enjilela E, Lee TY, Hsieh J et al (2017) Ultra-low-dose sparse-view quantitative CT liver perfusion imaging. Tomography 3:175–179

    Article  Google Scholar 

  11. **ao Y, Liu P, Liang Y et al (2019) STIR-net: deep spatial-temporal image restoration net for radiation reduction in CT perfusion. Front Neurol 10:647

    Article  Google Scholar 

  12. Brix G, Lechel U, Nekolla E, Griebel J, Becker C (2015) Radiation protection issues in dynamic contrast-enhanced (perfusion) computed tomography. Eur J Radiol 84:2347–2358

    Article  Google Scholar 

  13. Klotz E, Haberland U, Glatting G et al (2015) Technical prerequisites and imaging protocols for CT perfusion imaging in oncology. Eur J Radiol 84:2359–2367

    Article  Google Scholar 

  14. Goh V, Dattani M, Farwell J et al (2011) Radiation dose from volumetric helical perfusion CT of the thorax, abdomen or pelvis. Eur Radiol 21:974–981

    Article  Google Scholar 

  15. Ng CS, Chandler AG, Wei W et al (2013) Effect of sampling frequency on perfusion values in perfusion CT of lung tumors. AJR Am J Roentgenol 200:W155–W162

    Article  Google Scholar 

  16. Ng CS, Hobbs BP, Wei W et al (2015) Effect on perfusion values of sampling interval of computed tomographic perfusion acquisitions in neuroendocrine liver metastases and normal liver. J Comput Assist Tomogr 39:373–382

    PubMed  PubMed Central  Google Scholar 

  17. European Association For The Study Of The Liver (2012) EASL-EORTC clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol 56:908–943

  18. Hatzidakis A, Perisinakis K, Kalarakis G et al (2018) Perfusion-CT analysis for assessment of hepatocellular carcinoma lesions: diagnostic value of different perfusion maps. Acta Radiol 284185118791200

  19. Deak PD, Smal Y, Kalender WA (2010) Multisection CT protocols: sex- and age-specific conversion factors used to determine effective dose from dose-length product. Radiology 257:158–166

    Article  Google Scholar 

  20. van Ommen F, Kauw F, Bennink E, Dankbaar JW, Viergever MA, de Jong HWAM (2019) Effect of prolonged acquisition intervals for CT-perfusion analysis methods in patients with ischemic stroke. Med Phys 46:3156–3164

    Article  Google Scholar 

  21. Karwacki GM, Vögele S, Blackham KA (2019) Dose reduction in perfusion CT in stroke patients by lowering scan frequency does not affect automatically calculated infarct core volumes. J Neuroradiol 46:351–358

    Article  Google Scholar 

  22. Kambadakone AR, Sharma A, Catalano OA, Hahn PF, Sahani DV (2011) Protocol modifications for CT perfusion (CTp) examinations of abdomen-pelvic tumors: impact on radiation dose and data processing time. Eur Radiol 21:1293–1300

    Article  Google Scholar 

  23. Goh V, Halligan S, Hugill JA, Gartner L, Bartram CI (2005) Quantitative colorectal cancer perfusion measurement using dynamic contrast-enhanced multidetector-row computed tomography: effect of acquisition time and implications for protocols. J Comput Assist Tomogr 29:59–63

    Article  Google Scholar 

  24. 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 269:758–767

    Article  Google Scholar 

  25. Bayle M, Clerc-Urmes I, Ayav A et al (2019) Computed tomographic perfusion with 160-mm coverage: comparative analysis of hepatocellular carcinoma treated by two transarterial chemoembolization courses relative to magnetic resonance imaging findings. Abdom Radiol (NY) 44:85–94

    Article  Google Scholar 

  26. Kurucay M, Kloth C, Kaufmann S et al (2017) Multiparametric imaging for detection and characterization of hepatocellular carcinoma using gadoxetic acid-enhanced MRI and perfusion-CT: which parameters work best? Cancer Imaging 17:18

    Article  Google Scholar 

  27. Gawlitza J, Haubenreisser H, Meyer M et al (2016) Comparison of organ-specific-radiation dose levels between 70 kVp perfusion CT and standard tri-phasic liver CT in patients with hepatocellular carcinoma using a Monte-Carlo-simulation-based analysis platform. Eur J Radiol Open 3:95–99

    Article  CAS  Google Scholar 

  28. 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 51:430–434

    Article  Google Scholar 

  29. Fischer MA, Kartalis N, Grigoriadis A et al (2015) Perfusion computed tomography for detection of hepatocellular carcinoma in patients with liver cirrhosis. Eur Radiol 25:3123–3132

    Article  Google Scholar 

  30. Cros M, Geleijns J, Joemai RMS, Salvado M (2016) Perfusion CT of the brain and liver and of lung tumors: use of Monte Carlo simulation for patient dose estimation for examinations with a cone-beam 320-MDCT scanner. AJR Am J Roentgenol 206:129–135

    Article  Google Scholar 

  31. Brehmer K, Brismar TB, Morsbach F et al (2018) Triple arterial phase CT of the liver with radiation dose equivalent to that of single arterial phase CT: initial experience. Radiology 289:111–118

    Article  Google Scholar 

  32. Bevilacqua A, Malavasi S, Vilgrain V (2019) Liver CT perfusion: which is the relevant delay that reduces radiation dose and maintains diagnostic accuracy? Eur Radiol 29:6550–6558

    Article  Google Scholar 

  33. Thaiss WM, Haberland U, Kaufmann S et al (2019) Dose optimization of perfusion-derived response assessment in hepatocellular carcinoma treated with transarterial chemoembolization: comparison of volume perfusion CT and iodine concentration. Acad Radiol 26:1154–1163

    Article  Google Scholar 

  34. Kanda T, Yoshikawa T, Ohno Y et al (2012) CT hepatic perfusion measurement: comparison of three analytic methods. Eur J Radiol 81:2075–2079

    Article  Google Scholar 

  35. Hatzidakis A, Perisinakis K, Kalarakis G et al (2019) Perfusion-CT analysis for assessment of hepatocellular carcinoma lesions: diagnostic value of different perfusion maps. Acta Radiol 60:561–568

    Article  Google Scholar 

  36. Ippolito D, Capraro C, Casiraghi A, Cestari C, Sironi S (2012) Quantitative assessment of tumour associated neovascularisation in patients with liver cirrhosis and hepatocellular carcinoma: role of dynamic-CT perfusion imaging. Eur Radiol 22:803–811

    Article  Google Scholar 

  37. Fournier LS, Cuenod CA, de Bazelaire C et al (2004) Early modifications of hepatic perfusion measured by functional CT in a rat model of hepatocellular carcinoma using a blood pool contrast agent. Eur Radiol 14:2125–2133

    Article  Google Scholar 

  38. Sahani DV, Holalkere NS, Mueller PR, Zhu AX (2007) Advanced hepatocellular carcinoma: CT perfusion of liver and tumor tissue - initial experience. Radiology 243:736–743

    Article  Google Scholar 

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Correspondence to Georgios Kalarakis.

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The scientific guarantor of this publication is Adam Hatzidakis.

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Kalarakis, G., Perisinakis, K., Akoumianakis, E. et al. CT liver perfusion in patients with hepatocellular carcinoma: can we modify acquisition protocol to reduce patient exposure?. Eur Radiol 31, 1410–1419 (2021). https://doi.org/10.1007/s00330-020-07206-9

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  • DOI: https://doi.org/10.1007/s00330-020-07206-9

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