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Age-dependent Intracranial Artery Morphology in Healthy Children

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Abstract

Purpose

Evaluation of intracranial artery morphology plays an important role in diagnosing a variety of neurovascular diseases. In addition to clinical symptoms, diagnosis currently relies on qualitative rather than quantitative evaluation of vascular imaging sequences, such as magnetic resonance angiography (MRA). However, there is a paucity of literature on normal arterial morphology in the pediatric population across brain development. We aimed to quantitatively assess normal, age-related changes in artery morphology in children.

Methods

We performed retrospective analysis of pediatric MRA data obtained from a tertiary referral center. An MRA dataset from 98 children (49 boys/49 girls) aged 0.6–20 years (median = 11.5 years) with normal intracranial vasculature was retrospectively collected between 2011 and 2018. All arteries were automatically segmented to determine the vessel radius. Using an atlas-based approach, the average radius and density of arteries were measured in the three main cerebral vascular territories and the radius of five major arteries was determined at corresponding locations.

Results

The radii of the major arteries as well as the average artery radius and density in the different vascular territories in the brain remained constant throughout childhood and adolescence (|r| < 0.369 in all cases).

Conclusion

This study presents the first automated evaluation of intracranial vessel morphology on MRA across childhood. Our results can serve as a framework for quantitative evaluation of cerebral vessel morphology in the setting of pediatric neurovascular diseases.

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References

  1. Lee BC, Park TS, Kaufman BA. MR angiography in pediatric neurological disorders. Pediatr Radiol. 1995;25:409–19.

    Article  CAS  Google Scholar 

  2. Ruggieri PM, Masaryk TJ, Ross JS, Modic MT. Intracranial magnetic resonance angiography. Cardiovasc Intervent Radiol. 1992;15:71–81.

    Article  CAS  Google Scholar 

  3. Mouches P, Forkert ND. A statistical atlas of cerebral arteries generated using multi-center MRA datasets from healthy subjects. Sci Data. 2019;6:29.

    Article  Google Scholar 

  4. Forkert ND, Li MD, Lober RM, Yeom KW. Gray Matter Growth Is Accompanied by Increasing Blood Flow and Decreasing Apparent Diffusion Coefficient during Childhood. AJNR Am J Neuroradiol. 2016;37:1738–44.

    Article  CAS  Google Scholar 

  5. Cuoco JA, Busch CM, Klein BJ, Benko MJ, Stein R, Nicholson AD, Marvin EA. ACTA2 Cerebral Arteriopathy: Not Just a Puff of Smoke. Cerebrovasc Dis. 2018;46:161–71.

    Article  Google Scholar 

  6. Shim KW, Park EK, Kim JS, Kim DS. Cognitive Outcome of Pediatric Moyamoya Disease. J Korean Neurosurg Soc. 2015;57:440–4.

    Article  CAS  Google Scholar 

  7. Rätsep MT, Paolozza A, Hickman AF, Maser B, Kay VR, Mohammad S, Pudwell J, Smith GN, Brien D, Stroman PW, Adams MA, Reynolds JN, Croy BA, Forkert ND. Brain Structural and Vascular Anatomy Is Altered in Offspring of Pre-Eclamptic Pregnancies: A Pilot Study. AJNR Am J Neuroradiol. 2016;37:939–45.

    Article  Google Scholar 

  8. Kholmovski EG, Alexander AL, Parker DL. Correction of slab boundary artifact using histogram matching. J Magn Reson Imaging. 2002;15:610–7.

    Article  Google Scholar 

  9. Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging. 1998;17:87–97.

    Article  CAS  Google Scholar 

  10. Forkert ND, Schmidt-Richberg A, Fiehler J, Illies T, Möller D, Säring D, Handels H, Ehrhardt J. 3D cerebrovascular segmentation combining fuzzy vessel enhancement and level-sets with anisotropic energy weights. Magn Reson Imaging. 2013;31:262–71.

    Article  Google Scholar 

  11. Lee TC, Kashyap RL, Chu CN. Building skeleton models via 3‑D medial surface/axis thinning algorithms. Graph Model Image Proc. 1994;56:462–78.

    Article  Google Scholar 

  12. Danielsson PE. Euclidean distance mapping. Comput Graph Image Proc. 1980;14:227–48.

    Article  Google Scholar 

  13. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, Woods R, Paus T, Simpson G, Pike B, Holmes C, Collins L, Thompson P, MacDonald D, Iacoboni M, Schormann T, Amunts K, Palomero-Gallagher N, Geyer S, Parsons L, Narr K, Kabani N, Le Goualher G, Boomsma D, Cannon T, Kawashima R, Mazoyer B. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci. 2001;356:1293–322.

    Article  CAS  Google Scholar 

  14. Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL; Brain Development Cooperative Group. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage. 2011;54:313–27.

    Article  Google Scholar 

  15. Modat M, McClelland J, Ourselin S. Lung registration using the NiftyReg package. In: Medical image analysis for the clinic: a grand challenge. Workshop from the 13th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2010),; Beijing. 2010. pp. 33–42.

    Google Scholar 

  16. Mutsaerts HJ, van Dalen JW, Heijtel DF, Groot PF, Majoie CB, Petersen ET, Richard E, Nederveen AJ. Cerebral Perfusion Measurements in Elderly with Hypertension Using Arterial Spin Labeling. PLoS One. 2015;10:e0133717.

    Article  CAS  Google Scholar 

  17. Zurada A, Gielecki J, Tubbs RS, Loukas M, Maksymowicz W, Cohen-Gadol AA, Michalak M, Chlebiej M, Zurada-Zielińska A. Three-dimensional morphometrical analysis of the M1 segment of the middle cerebral artery: potential clinical and neurosurgical implications. Clin Anat. 2011;24:34–46.

    Article  Google Scholar 

  18. Türe U, Yaşargil MG, Al-Mefty O, Yaşargil DC. Arteries of the insula. J Neurosurg. 2000;92:676–87.

    Article  Google Scholar 

  19. Müller HR, Brunhölzl C, Radü EW, Buser M. Sex and side differences of cerebral arterial caliber. Neuroradiology. 1991;33:212–6.

    Article  CAS  Google Scholar 

  20. Wong WS, Tsuruda JS, Liberman RL, Chirino A, Vogt JF, Gangitano E. Color Doppler imaging of intracranial vessels in the neonate. AJR Am J Roentgenol. 1989;152:1065–70.

    Article  CAS  Google Scholar 

  21. O’Brien NF, Maa T, Moore-Clingenpeel M, Rosenberg N, Yeates KO. Relationships between cerebral flow velocities and neurodevelopmental outcomes in children with moderate to severe traumatic brain injury. Childs Nerv Syst. 2018;34:663–72.

    Article  Google Scholar 

  22. Lovett ME, Maa T, Chung MG, O’Brien NF. Cerebral blood flow velocity and autoregulation in paediatric patients following a global hypoxic-ischaemic insult. Resuscitation. 2018;126:191–6.

    Article  Google Scholar 

  23. Wintermark M, Lepori D, Cotting J, Roulet E, van Melle G, Meuli R, Maeder P, Regli L, Verdun FR, Deonna T, Schnyder P, Gudinchet F. Brain perfusion in children: evolution with age assessed by quantitative perfusion computed tomography. Pediatrics. 2004;113:1642–52.

    Article  Google Scholar 

  24. Hirata Y, Hamano SI, Ikemoto S, Oba A, Matsuura R. Quantitative evaluation of regional cerebral blood flow changes during childhood using 123I-N-isopropyl-iodoamphetamine single-photon emission computed tomography. Brain Dev. 2018;40:841–9.

    Article  Google Scholar 

  25. Wu C, Honarmand AR, Schnell S, Kuhn R, Schoeneman SE, Ansari SA, Carr J, Markl M, Shaibani A. Age-Related Changes of Normal Cerebral and Cardiac Blood Flow in Children and Adults Aged 7 Months to 61 Years. J Am Heart Assoc. 2016;5:e002657.

    PubMed  PubMed Central  Google Scholar 

  26. Bode H, Wais U. Age dependence of flow velocities in basal cerebral arteries. Arch Dis Child. 1988;63:606–11.

    Article  CAS  Google Scholar 

  27. Harrington AR, Kuzawa CW, Boyer DM. Carotid foramen size in the human skull tracks developmental changes in cerebral blood flow and brain metabolism. Am J Phys Anthropol. 2019;169:161–9.

    Article  Google Scholar 

  28. Raybaud C. Normal and abnormal embryology and development of the intracranial vascular system. Neurosurg Clin N Am. 2010;21:399–426.

    Article  Google Scholar 

  29. Malamateniou C, Counsell SJ, Allsop JM, Fitzpatrick JA, Srinivasan L, Cowan FM, Hajnal JV, Rutherford MA. The effect of preterm birth on neonatal cerebral vasculature studied with magnetic resonance angiography at 3 Tesla. Neuroimage. 2006;32:1050–9.

    Article  Google Scholar 

  30. Degani S. Evaluation of fetal cerebrovascular circulation and brain development: the role of ultrasound and Doppler. Semin Perinatol. 2009;33:259–69.

    Article  Google Scholar 

  31. Bray S. Age-associated patterns in gray matter volume, cerebral perfusion and BOLD oscillations in children and adolescents. Hum Brain Mapp. 2017;38:2398–407.

    Article  Google Scholar 

  32. Sass C, Herbeth B, Chapet O, Siest G, Visvikis S, Zannad F. Intima-media thickness and diameter of carotid and femoral arteries in children, adolescents and adults from the Stanislas cohort: effect of age, sex, anthropometry and blood pressure. J Hypertens. 1998;16:1593–602.

    Article  CAS  Google Scholar 

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Correspondence to Nils D. Forkert.

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Conflict of interest

J.L. Quon, P. Mouches, L.H. Kim, R. Jabarkheel, Y. Zhang, G.K. Steinberg, G.A. Grant, M.S.B. Edwards, K.W. Yeom and N.D. Forkert declare that they have no competing interests.

Ethical standards

For this article no studies with human participants or animals were performed by any of the authors. All studies performed were in accordance with the ethical standards indicated in each case.

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Quon, J.L., Mouches, P., Kim, L.H. et al. Age-dependent Intracranial Artery Morphology in Healthy Children. Clin Neuroradiol 32, 49–56 (2022). https://doi.org/10.1007/s00062-021-01071-9

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  • DOI: https://doi.org/10.1007/s00062-021-01071-9

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