Ultra-low-dose multiphase CT angiography derived from CT perfusion data in patients with middle cerebral artery stenosis

  • Xiaoling Wu
  • Yuelong Yang
  • Menghuang Wen
  • Lijuan Wang
  • Yunjun Yang
  • Yuhu Zhang
  • Zihua Mo
  • Kun Nie
  • Biao HuangEmail author
Diagnostic Neuroradiology



Computed tomography (CT) perfusion (CTP) source images contain both brain perfusion and cerebrovascular information, and may allow a dynamic assessment of collaterals. The purpose of the study was to compare the image quality and the collaterals identified on multiphase CT angiography (CTA) derived from CTP datasets (hereafter called CTPA) reconstructed with iterative model reconstruction (IMR) algorithm in patients with middle cerebral artery (MCA) steno-occlusion with those of routine CTA.


Consecutive patients with a unilateral MCA steno-occlusion underwent non-contrast CT (NCCT), CTP, and CTA. CTPA images were reconstructed from CTP datasets. The vascular attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of routine CTA and CTPA were measured and analyzed by Student’s t test. Subjective image quality and collaterals were scored and compared using the Wilcoxon signed-rank test.


Fifty-eight patients (mean age 61.7 years, 78% males, median National Institutes of Health Stroke Scale score = 12) were included. The effective radiation dose of CTP was 1.28 mSv. The vascular attenuation, SNR, CNR, and the image quality of CTPA were considerably higher than that of CTA (all, p < 0.001). Collaterals were rated higher on CTPA compared with CTA (1.79 ± 0.64 vs. 1.22 ± 0.84, p < 0.001). Fifty-three percent of patients with poor collaterals assessed on single-phase CTA had good collaterals on CTPA.


CTPA derived from CTP datasets reconstructed with IMR algorithm offers image quality comparable to routine CTA and provides time-resolved evaluation of collaterals in patients with MCA ischemic disease.


Perfusion imaging Computed tomography angiography Collateral circulation Iterative model reconstruction Radiation dosage 


Funding information

This work was supported by the Key R&D Program of Guangdong Province (grant number 2018B030339001), the National Natural Science Foundation of China (grant number 81671275), and the Fundamental Research Funds for the Central Universities (grant number 2018MS27).

Compliance with ethical standards

We declare that all human studies have been approved by the local ethics committee and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Conflict of interest

The authors declare that they have no conflict of interest.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Mettler FA Jr, Bhargavan M, Faulkner K, Gilley DB, Gray JE, Ibbott GS, Lipoti JA, Mahesh M, McCrohan JL, Stabin MG, Thomadsen BR, Yoshizumi TT (2009) Radiologic and nuclear medicine studies in the United States and worldwide: frequency, radiation dose, and comparison with other radiation sources--1950-2007. Radiology 253(2):520–531. CrossRefPubMedGoogle Scholar
  2. 2.
    Mehta D, Thompson R, Morton T, Dhanantwari A, Shefer E (2013) Iterative model reconstruction: simultaneously lowered computed tomography radiation dose and improved image quality. Med Phys Int J 2:147–155Google Scholar
  3. 3.
    Kim H, Park CM, Song YS, Lee SM, Goo JM (2014) Influence of radiation dose and iterative reconstruction algorithms for measurement accuracy and reproducibility of pulmonary nodule volumetry: a phantom study. Eur J Radiol 83(5):848–857. CrossRefPubMedGoogle Scholar
  4. 4.
    Khawaja RDA, Singh S, Blake M, Harisinghani M, Choy G, Karosmangulu A, Padole A, Do S, Brown K, Thompson R, Morton T, Raihani N, Koehler T, Kalra MK (2015) Ultra-low dose abdominal MDCT: using a knowledge-based Iterative Model Reconstruction technique for substantial dose reduction in a prospective clinical study. Eur J Radiol 84(1):2–10. CrossRefPubMedGoogle Scholar
  5. 5.
    Patino M, Fuentes JM, Hayano K, Kambadakone AR, Uyeda JW, Sahani DV (2015) A quantitative comparison of noise reduction across five commercial (hybrid and model-based) iterative reconstruction techniques: an anthropomorphic phantom study. AJR Am J Roentgenol 204(2):W176–W183. CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Yuki H, Utsunomiya D, Funama Y, Tokuyasu S, Namimoto T, Hirai T, Itatani R, Katahira K, Oshima S, Yamashita Y (2014) Value of knowledge-based iterative model reconstruction in low-kV 256-slice coronary CT angiography. J Cardiovasc Comput Tomogr 8(2):115–123. CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Menon BK, d'Esterre CD, Qazi EM, Almekhlafi M, Hahn L, Demchuk AM, Goyal M (2015) Multiphase CT angiography: a new tool for the imaging triage of patients with acute ischemic stroke. Radiology 275(2):510–520. CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Cai W, Hu C, Hu S, Wang X, Gong J, Zhang W, Shi D, Cheng B (2018) Feasibility study of iterative model reconstruction combined with low tube voltage, low iodine load, and low iodine delivery rate in craniocervical CT angiography. Clin Radiol 73(2):217.e211–217.e216. CrossRefGoogle Scholar
  9. 9.
    Wang X, Zhu C, Li J, Degnan AJ, Jiang T, Lu J (2018) Knowledge-based iterative model reconstruction: comparative image quality with low tube voltage cerebral CT angiography. Medicine (Baltimore) 97(30):e11514. CrossRefGoogle Scholar
  10. 10.
    Smit EJ, Vonken EJ, van Seeters T, Dankbaar JW, van der Schaaf IC, Kappelle LJ, van Ginneken B, Velthuis BK, Prokop M (2013) Timing-invariant imaging of collateral vessels in acute ischemic stroke. Stroke 44(8):2194–2199. CrossRefPubMedGoogle Scholar
  11. 11.
    Bivard A, Levi C, Spratt N, Parsons M (2013) Perfusion CT in acute stroke: a comprehensive analysis of infarct and penumbra. Radiology 267(2):543–550. CrossRefPubMedGoogle Scholar
  12. 12.
    Copen WA, Lev MH, Rapalino O (2016) Brain perfusion: computed tomography and magnetic resonance techniques. Handb Clin Neurol 135:117–135. CrossRefPubMedGoogle Scholar
  13. 13.
    Heit JJ, Wintermark M (2016) Perfusion computed tomography for the evaluation of acute ischemic stroke: strengths and pitfalls. Stroke 47(4):1153–1158. CrossRefPubMedGoogle Scholar
  14. 14.
    Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K, Biller J, Brown M, Demaerschalk BM, Hoh B, Jauch EC, Kidwell CS, Leslie-Mazwi TM, Ovbiagele B, Scott PA, Sheth KN, Southerland AM, Summers DV, Tirschwell DL (2018) 2018 Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 49(3):e46–e110. CrossRefPubMedGoogle Scholar
  15. 15.
    Geyer LL, Schoepf UJ, Meinel FG, Nance JW Jr, Bastarrika G, Leipsic JA, Paul NS, Rengo M, Laghi A, De Cecco CN (2015) State of the art: iterative ct reconstruction techniques. Radiology 276(2):339–357. CrossRefPubMedGoogle Scholar
  16. 16.
    Bang M, Choi SH, Park J, Kang BS, Kwon WJ, Lee TH, Nam JG (2016) Radiation dose reduction in paranasal sinus CT: with feasibility of iterative reconstruction technique. Otolaryngol Head Neck Surg 155(6):982–987. CrossRefPubMedGoogle Scholar
  17. 17.
    Yan C, Xu J, Liang C, Wei Q, Wu Y, Xiong W, Zheng H, Xu Y (2018) Radiation dose reduction by using CT with iterative model reconstruction in patients with pulmonary invasive fungal infection. Radiology 288(1):285–292. CrossRefPubMedGoogle Scholar
  18. 18.
    Andre F, Fortner P, Vembar M, Mueller D, Stiller W, Buss SJ, Kauczor HU, Katus HA, Korosoglou G (2017) Improved image quality with simultaneously reduced radiation exposure: knowledge-based iterative model reconstruction algorithms for coronary CT angiography in a clinical setting. J Cardiovasc Comput Tomogr 11(3):213–220. CrossRefPubMedGoogle Scholar
  19. 19.
    Inoue T, Nakaura T, Yoshida M, Yokoyama K, Hirata K, Kidoh M, Oda S, Utsunomiya D, Harada K, Yamashita Y (2017) Diagnosis of small posterior fossa stroke on brain CT: effect of iterative reconstruction designed for brain CT on detection performance. Eur Radiol 27(9):3710–3715. CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Qian WL, Zhou DJ, Jiang Y, Feng C, Chen Q, Wang H, Zhang JB, Xu JM (2018) Ultra-low radiation dose CT angiography of the lower extremity using the iterative model reconstruction (IMR) algorithm. Clin Radiol 73(11):985.e913–985.e919. CrossRefGoogle Scholar
  21. 21.
    Iyama Y, Nakaura T, Yokoyama K, Kidoh M, Harada K, Tokuyasu S, Yamashita Y (2017) Impact of knowledge-based iterative model reconstruction in abdominal dynamic CT with low tube voltage and low contrast dose. AJR Am J Roentgenol 206(4):687–693CrossRefGoogle Scholar
  22. 22.
    Wintermark M, Albers GW, Alexandrov AV, Alger JR, Bammer R, Baron JC, Davis S, Demaerschalk BM, Derdeyn CP, Donnan GA, Eastwood JD, Fiebach JB, Fisher M, Furie KL, Goldmakher GV, Hacke W, Kidwell CS, Kloska SP, Kohrmann M, Koroshetz W, Lee TY, Lees KR, Lev MH, Liebeskind DS, Ostergaard L, Powers WJ, Provenzale J, Schellinger P, Silbergleit R, Sorensen AG, Wardlaw J, Wu O, Warach S (2008) Acute stroke imaging research roadmap. AJNR Am J Neuroradiol 29(5):e23–e30CrossRefGoogle Scholar
  23. 23.
    Murphy A, So A, Lee TY, Symons S, Jakubovic R, Zhang L, Aviv RI (2014) Low dose CT perfusion in acute ischemic stroke. Neuroradiology 56(12):1055–1062. CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Lin CJ, Wu TH, Lin CH, Hung SC, Chiu CF, Liu MJ, Teng MM, Chang FC, Guo WY, Chang CY (2013) Can iterative reconstruction improve imaging quality for lower radiation CT perfusion? Initial experience. AJNR Am J Neuroradiol 34:1516–1521. CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Diego L, Atul P, Kalra MK, Sarabjeet S (2015) Tube potential and CT radiation dose optimization. AJR Am J Roentgenol 204(1):4–10CrossRefGoogle Scholar
  26. 26.
    Frolich AM, Psychogios MN, Klotz E, Schramm R, Knauth M, Schramm P (2012) Antegrade flow across incomplete vessel occlusions can be distinguished from retrograde collateral flow using 4-dimensional computed tomographic angiography. Stroke 43(11):2974–2979. CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    van den Wijngaard IR, Boiten J, Holswilder G, Algra A, Dippel DW, Velthuis BK, Wermer MJ, van Walderveen MA (2015) Impact of collateral status evaluated by dynamic computed tomographic angiography on clinical outcome in patients with ischemic stroke. Stroke 46(12):3398–3404. CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    van den Wijngaard IR, Holswilder G, Wermer MJ, Boiten J, Algra A, Dippel DW, Dankbaar JW, Velthuis BK, Boers AM, Majoie CB, van Walderveen MA (2016) Assessment of collateral status by dynamic CT angiography in acute MCA stroke: timing of acquisition and relationship with final infarct volume. AJNR Am J Neuroradiol 37(7):1231–1236. CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Menon BK, Smith EE, Modi J, Patel SK, Bhatia R, Watson TW, Hill MD, Demchuk AM, Goyal M (2011) Regional leptomeningeal score on CT angiography predicts clinical and imaging outcomes in patients with acute anterior circulation occlusions. AJNR Am J Neuroradiol 32(9):1640–1645. CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Menon BK, O’Brien B, Bivard A, Spratt NJ, Demchuk AM, Miteff F, Lu X, Levi C, Parsons MW (2013) Assessment of leptomeningeal collaterals using dynamic CT angiography in patients with acute ischemic stroke. J Cereb Blood Flow Metab 33(3):365–371. CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Radiology, Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhouPeople’s Republic of China
  2. 2.School of MedicineSouth China University of TechnologyGuangzhouChina
  3. 3.Department of Neurology, Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhouChina

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