Arterial spin-labeling magnetic resonance imaging of brain maturation in early childhood: Mathematical model fitting to assess age-dependent change of cerebral blood flow

Alex Mun-Ching Wong; Ho-Ling Liu; Ming-Lun Tsai; Erin Simon Schwartz; Chih-Hua Yeh; Huei-Shyong Wang; Tai-Wei Wu; Chien-Yuan Lin

doi:10.1016/j.mri.2019.03.016

Arterial spin-labeling magnetic resonance imaging of brain maturation in early childhood: Mathematical model fitting to assess age-dependent change of cerebral blood flow

Magn Reson Imaging. 2019 Jun:59:114-120. doi: 10.1016/j.mri.2019.03.016. Epub 2019 Mar 21.

Authors

Alex Mun-Ching Wong¹, Ho-Ling Liu², Ming-Lun Tsai³, Erin Simon Schwartz⁴, Chih-Hua Yeh³, Huei-Shyong Wang⁵, Tai-Wei Wu⁶, Chien-Yuan Lin⁷

Affiliations

¹ Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Keelung/Linkou, and Chang Gung University, Taiwan. Electronic address: alexmcwchop@yahoo.com.
² Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: hlaliu@mdanderson.org.
³ Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Keelung/Linkou, and Chang Gung University, Taiwan.
⁴ Department of Radiology, The Children's Hospital of Philadelphia, Perelman School of Medicine at University of Pennsylvania, Philadelphia, PA, USA. Electronic address: schwartzES@chop.edu.
⁵ Division of Pediatric Neurology, Department of Pediatrics, Chang Gung Children's Hospital and Chang Gung University, Linkou, Taiwan. Electronic address: wanghs444@adm.cgmh.org.tw.
⁶ Feta and Neonatal Institute, Division of Neonatology, Children's Hospital Los Angeles, Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Electronic address: twu@chla.usc.edu.
⁷ GE Healthcare, Taiwan.

PMID: 30905764
DOI: 10.1016/j.mri.2019.03.016

Abstract

Purpose: To determine the trajectory of age-dependent cerebral blood flow (CBF) change in infants and young children by fitting mathematical models to the imaging data.

Methods: In this retrospective study, we reviewed the arterial spin-labeling imaging studies of 49 typically developing infants and young children at postmenstrual age (PMA) ranging from 38 to 194 weeks. All patients had normal structural MR imaging. Coregistration and gray matter segmentation were performed to extract whole-brain CBF values. Regional CBF values were obtained using manual region-of-interest placement. Curve estimation regression procedures with the corrected Akaike information criterion (AICc) were performed to determine the mathematical model best fitting the relationship between the CBF (whole-brain and regional measurements) and PMA of the patients.

Results: Whole-brain CBF trajectory was best fitted by a cubic model (AICc = 215.95; R² = 0.566; P < .001). Whole-brain CBF at 1, 6, 12, and 24 months was estimated to be 36, 52, 58, and 55 mL/100 g/min, respectively. Regional CBF trajectory was also best fitted by a cubic model in the frontal (AICc = 233.63; R² = 0.442; P < .001), parietal (AICc = 229.18; R² = 0.614; P < .001), basal ganglion (AICc = 239.39; R² = 0.178; P = .043), temporal (AICc = 236.01; R² = 0.441; P < .001), and occipital (AICc = 236.46; R² = 0.475; P < .001) regions.

Conclusions: In early childhood, the trajectory of CBF change was nonlinear and best fitted by the cubic model for the whole brain and all brain regions.

Keywords: Akaike information criterion; Arterial spin-labeling imaging; Cerebral blood flow; Mathematical model fitting.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Arteries / diagnostic imaging
Brain / diagnostic imaging*
Brain / physiology
Cerebrovascular Circulation*
Child, Preschool
Female
Gray Matter
Humans
Image Processing, Computer-Assisted / methods*
Infant
Infant, Newborn
Magnetic Resonance Imaging*
Male
Models, Theoretical
Perfusion
Regression Analysis
Retrospective Studies
Software
Spin Labels*

Substances

Spin Labels