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CT and MR perfusion can discriminate severe cerebral hypoperfusion from perfusion absence: evaluation of different commercial software packages by using digital phantoms

  • Diagnostic Neuroradiology
  • Published:
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Abstract

Introduction

Computed tomography perfusion (CTP) and magnetic resonance perfusion (MRP) are expected to be usable for ancillary tests of brain death by detection of complete absence of cerebral perfusion; however, the detection limit of hypoperfusion has not been determined. Hence, we examined whether commercial software can visualize very low cerebral blood flow (CBF) and cerebral blood volume (CBV) by creating and using digital phantoms.

Methods

Digital phantoms simulating 0–4% of normal CBF (60 mL/100 g/min) and CBV (4 mL/100 g/min) were analyzed by ten software packages of CT and MRI manufacturers. Region-of-interest measurements were performed to determine whether there was a significant difference between areas of 0% and areas of 1–4% of normal flow.

Results

The CTP software detected hypoperfusion down to 2–3% in CBF and 2% in CBV, while the MRP software detected that of 1–3% in CBF and 1–4% in CBV, although the lower limits varied among software packages.

Conclusion

CTP and MRP can detect the difference between profound hypoperfusion of <5% from that of 0% in digital phantoms, suggesting their potential efficacy for assessing brain death.

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References

  1. Wijdicks EF (2002) Brain death worldwide: accepted fact but no global consensus in diagnostic criteria. Neurology 58(1):20–25

    PubMed  Google Scholar 

  2. Young GB, Lee D (2004) A critique of ancillary tests for brain death. Neurocrit Care 1(4):499–508

    Article  PubMed  Google Scholar 

  3. Busl KM, Greer DM (2009) Pitfalls in the diagnosis of brain death. Neurocrit Care 11(2):276–287

    Article  PubMed  Google Scholar 

  4. Heran MK, Heran NS, Shemie SD (2008) A review of ancillary tests in evaluating brain death. Can J Neurol Sci 35(4):409–419

    PubMed  Google Scholar 

  5. Dupas B, Gayet-Delacroix M, Villers D, Antonioli D, Veccherini MF, Soulillou JP (1998) Diagnosis of brain death using two-phase spiral CT. AJNR Am J Neuroradiol 19(4):641–647

    PubMed  CAS  Google Scholar 

  6. Frampas E, Videcoq M, de Kerviler E, Ricolfi F, Kuoch V, Mourey F, Tenaillon A, Dupas B (2009) CT angiography for brain death diagnosis. AJNR Am J Neuroradiol 30(8):1566–1570

    Article  PubMed  CAS  Google Scholar 

  7. Karantanas AH, Hadjigeorgiou GM, Paterakis K, Sfiras D, Komnos A (2002) Contribution of MRI and MR angiography in early diagnosis of brain death. Eur Radiol 12(11):2710–2716

    PubMed  CAS  Google Scholar 

  8. Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: mathematical approach and statistical analysis. Magn Reson Med 36(5):715–725

    Article  PubMed  CAS  Google Scholar 

  9. Meier P, Zierler KL (1954) On the theory of the indicator-dilution method for measurement of blood flow and volume. J Appl Physiol 6(12):731–744

    PubMed  CAS  Google Scholar 

  10. Stewart GN (1893) Researches on the circulation time in organs and on the influences which affect it: Parts I–III. J Physiol 15(1–2):1–89

    PubMed  CAS  Google Scholar 

  11. Wu O, Ostergaard L, Weisskoff RM, Benner T, Rosen BR, Sorensen AG (2003) Tracer arrival timing-insensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magn Reson Med 50(1):164–174

    Article  PubMed  Google Scholar 

  12. Latchaw RE, Yonas H, Hunter GJ, Yuh WT, Ueda T, Sorensen AG, Sunshine JL, Biller J, Wechsler L, Higashida R, Hademenos G (2003) Guidelines and recommendations for perfusion imaging in cerebral ischemia: a scientific statement for healthcare professionals by the writing group on perfusion imaging, from the Council on Cardiovascular Radiology of the American Heart Association. Stroke 34(4):1084–1104

    Article  PubMed  Google Scholar 

  13. Jones TH, Morawetz RB, Crowell RM, Marcoux FW, FitzGibbon SJ, DeGirolami U, Ojemann RG (1981) Thresholds of focal cerebral ischemia in awake monkeys. J Neurosurg 54(6):773–782

    Article  PubMed  CAS  Google Scholar 

  14. Pistoia F, Johnson DW, Darby JM, Horton JA, Applegate LJ, Yonas H (1991) The role of xenon CT measurements of cerebral blood flow in the clinical determination of brain death. AJNR Am J Neuroradiol 12(1):97–103

    PubMed  CAS  Google Scholar 

  15. Darby JM, Yonas H, Gur D, Latchaw RE (1987) Xenon-enhanced computed tomography in brain death. Arch Neurol 44(5):551–554

    Article  PubMed  CAS  Google Scholar 

  16. Donohoe KJ, Frey KA, Gerbaudo VH, Mariani G, Nagel JS, Shulkin B (2003) Procedure guideline for brain death scintigraphy. J Nucl Med 44(5):846–851

    PubMed  Google Scholar 

  17. Munari M, Zucchetta P, Carollo C, Gallo F, De Nardin M, Marzola MC, Ferretti S, Facco E (2005) Confirmatory tests in the diagnosis of brain death: comparison between SPECT and contrast angiography. Crit Care Med 33(9):2068–2073

    Article  PubMed  Google Scholar 

  18. Wijdicks EF (2010) The case against confirmatory tests for determining brain death in adults. Neurology 75(1):77–83

    Article  PubMed  Google Scholar 

  19. Ishii K, Onuma T, Kinoshita T, Shiina G, Kameyama M, Shimosegawa Y (1996) Brain death: MR and MR angiography. AJNR Am J Neuroradiol 17(4):731–735

    PubMed  CAS  Google Scholar 

  20. Escudero D, Otero J, Marques L, Parra D, Gonzalo JA, Albaiceta GM, Cofino L, Blanco A, Vega P, Murias E, Meilan A, Roger RL, Taboada F (2009) Diagnosing brain death by CT perfusion and multislice CT angiography. Neurocrit Care 11(2):261–271

    Article  PubMed  Google Scholar 

  21. Salomon EJ, Barfett J, Willems PW, Geibprasert S, Bacigaluppi S, Krings T (2009) Dynamic CT angiography and CT perfusion employing a 320-detector row CT: protocol and current clinical applications. Klin Neuroradiol 19(3):187–196

    Article  PubMed  Google Scholar 

  22. Murayama K, Katada K, Nakane M, Toyama H, Anno H, Hayakawa M, Ruiz DS, Murphy KJ (2009) Whole-brain perfusion CT performed with a prototype 256-detector row CT system: initial experience. Radiology 250(1):202–211

    Article  PubMed  Google Scholar 

  23. Ulzheimer S, Flohr T (2009) Multislice CT: current technology and future developments. In: Reiser MF, Becker CR, Nikolaou K, Glazer G (eds) Multislice CT. Medical radiology. Springer, Berlin Heidelberg, pp 3–23

    Chapter  Google Scholar 

  24. Stocchetti N, Zanier ER, Nicolini R, Faegersten E, Canavesi K, Conte V, Gattinoni L (2005) Oxygen and carbon dioxide in the cerebral circulation during progression to brain death. Anesthesiology 103(5):957–961

    Article  PubMed  CAS  Google Scholar 

  25. Bohatyrewicz R, Sawicki M, Walecka A, Walecki J, Rowinski O, Bohatyrewicz A, Kanski A, Czajkowski Z, Krzysztalowski A, Solek-Pastuszka J, Zukowski M, Marzec-Lewenstein E, Wojtaszek M (2010) Computed tomographic angiography and perfusion in the diagnosis of brain death. Transplant Proc 42(10):3941–3946

    Article  PubMed  CAS  Google Scholar 

  26. Nabavi DG, Cenic A, Craen RA, Gelb AW, Bennett JD, Kozak R, Lee TY (1999) CT assessment of cerebral perfusion: experimental validation and initial clinical experience. Radiology 213(1):141–149

    PubMed  CAS  Google Scholar 

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Acknowledgments

This work was partly supported by a Grant-in-Aid for Strategic Medical Science Research Center from the Ministry of Education, Culture, Sports, Science, and Technology of Japan, and by a Grant-in-Aid for Scientific Research 2009 from the Ministry of Health, Labor, and Welfare of Japan.

Conflict of interest

We declare that we have no conflict of interest.

Author information

Authors and Affiliations

  1. Advanced Medical Research Center, Iwate Medical University, 19-1 Uchimaru, Morioka, 020-8505, Japan

    Ikuko Uwano, Kohsuke Kudo & Makoto Sasaki

  2. Departments of Neurology and Radiology, University of Melbourne, Royal Melbourne Hospital, Victoria, 3050, Australia

    Soren Christensen

  3. Department of Neuroradiology, Center for Functionally Integrative Neuroscience, Århus University Hospital, DK, 8000, Århus C, Denmark

    Leif Østergaard

  4. Department of Neurosurgery, Iwate Medical University, 19-1 Uchimaru, Morioka, 020-8505, Japan

    Kuniaki Ogasawara & Akira Ogawa

Authors
  1. Ikuko Uwano
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  2. Kohsuke Kudo
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  3. Makoto Sasaki
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  5. Leif Østergaard
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  7. Akira Ogawa
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Corresponding author

Correspondence to Kohsuke Kudo.

Appendix

Appendix

The arterial curve expressed by an AIF, C a (t), was generated using a γ variate function as follows:

$$ {C_a}(t) = \left\{ {\begin{array}{*{20}{c}} 0 \\ {{C_0}{{\left( {t - {t_0}} \right)}^r}{e^{{ - \left( {t - {t_0}} \right)/b}}}} \\ \end{array} \begin{array}{*{20}{c}}, \\, \\ \end{array} } \right.\begin{array}{*{20}{c}} {{ }} \\ {{ }} \\ \end{array} \begin{array}{*{20}{c}} {t \leqslant {t_0}} \\ {t > {t_0}} \\ \end{array} $$
(A1)

with C 0 = 1.0, t 0 = 12.0 s, r = 3.0 and b = 1.5 s. The VOF (C v (t)) was created by convolution of AIF and the residue function, R(t), as follows:

$$ {C_v}(t) = {C_a}(t) \otimes R(t) $$
(A2)

where ⊗ denotes the convolution operator. The exponential R(t) described below was used:

$$ R(t) = {e^{{ - \tfrac{t}{\text{MTT}}}}} $$
(A3)

where MTT is the mean transit time of the contrast, and the VOF was set to 6 s. The concentration-time curve of the tissue, C(t), was also generated by convolution of AIF and R(t), as follows:

$$ C(t) = \frac{1}{H} \cdot {\text{CBF}} \cdot \left( {{C_a}(t) \otimes R(t)} \right) $$
(A4)

where H was a correction factor for the hematocrit difference between a large vessel (hl) and a small vessel (hs).

$$ H = \frac{{1 - hl}}{{1 - hs}} $$
(A5)

Values for hl and hs were set to 0.45 and 0.25, respectively. A box-shaped R(t) was used for C(t) generation as follows:

$$ R(t) = \left\{ {\begin{array}{*{20}{c}} {1,} & {t \leqslant {\text{MTT}}} \\ {0,} & {t > {\text{MTT}}} \\ \end{array} } \right. $$
(A6)

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Uwano, I., Kudo, K., Sasaki, M. et al. CT and MR perfusion can discriminate severe cerebral hypoperfusion from perfusion absence: evaluation of different commercial software packages by using digital phantoms. Neuroradiology 54, 467–474 (2012). https://doi.org/10.1007/s00234-011-0905-8

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