Maarten Naeyaert^{1}, Tim Vanderhasselt^{1}, Marcel Warntjes^{2}, and Hubert Raeymaekers^{1}

Synthetic MRI using a multi-delay multi-echo sequence was applied to a pre-term neonatal and

31 preterm born neonates (born 205 ± 19 days gestational age, 14 females) were scanned near term equivalent age. 13 full term born neonates and infants were scanned at various times after birth (mean age 29 +/- 32 days, 7 females). All scans were performed on a 3T Philips Achieva, using a multi-delay multi-echo sequence with four saturation delays, two echo times and a TR of 5000ms. FOV was 180 x 130 mm with an acquisition resolution of 0.7x0.9 mm; 30 slices were acquired with a 3.0 mm thickness and 0,3mm gap, the timing was 6min10s. Additionally, a 3D T1 weighted and a 3D T2 weighted TSE sequence image were acquired.

The data was analyzed and segmented using an adapted version of SyMRI 8 where detected CSF partial volume in neonates was suppressed (SyntheticMR, Linköping, Sweden). The R1 and R2 relaxation rates and proton-density parameter maps are estimated, and the brain is segmented into different tissues based on a look-up table in a three-dimensional space ^{1}. After segmentation, the intra-cranial volume (ICV), Brain Parenchymal Volume (BPV) and Fraction (BPF) are also calculated, as well as the GM, WM, and CSF fractions.

For the preterm neonates, Kidokoro’s score was calculated using the 3D scans by a trained neonatal neuroradiologist. During scoring the severity of brain development impairment is scored by manually measuring landmarks and counting lesions ².

To investigate whether the gestational age at birth (GAB) affected brain measures, a linear regression was performed using SPSS 23 (IBM, Armonk, NY, USA) for the absolute BPV, GM, CSF, ICV and for the relative fractions BPF, GMF and CSF. Both sex and GAB were added stepwise as factors in the model.

Additionally, the preterm neonates with a normal Kidokoro score (<4) and the term born children (total n=37) were analyzed to investigate the development of the brain, by performing a linear regression on the same volumetric parameters as above, with corrected age and sex as factors in the regression. The preterm children were only included if the preterm analysis found no connection between GAB and the parameter under investigation.

An example of the fully automatic brain segmentation for children scanned at different ages is provided in figure 1. The results of the linear regressions on the preterm population are presented in table 1. Excluding children with an abnormal Kidokoro’s score did not change the significance of the regression results. The regression results for all neonates are presented in table 2. For the absolute volumes, the preterm population was included, as no statistical difference was found in the previous tests, with the corrected age (days) = GAB–280+age at time of scanning.

1. West J, Warntjes JB, Lundberg P. Novel whole brain segmentation and volume estimation using quantitative MRI. Eur Radiol. 2012 May;22(5):998-1007. doi: 10.1007/s00330-011-2336-7

2. Kidokoro H, Neil J, Inder T. A New MRI Assessment Tool to Define Brain Abnormalities in Very Preterm Infants at Term, AJNR Am J Neuroradiol. 2013 Nov-Dec; 34(11): 2208–2214. doi: 10.3174/ajnr.A3521

3. McAllister A, Leach J, West H, Jones B, Zhang B, Serai S. Quantitative Synthetic MRI in Children: Normative Intracranial Tissue Segmentation Values during Development. AJNR Am J Neuroradiol. 2017 Oct 5. doi: 10.3174/ajnr.A5398

Figure 1: An example of the fully automatic brain segmentation. The child on the left was premature, born at 25 weeks and scanned at a time equivalent to 39 weeks gestation, in the center an a-term child scanned after 1 week and on the right an a-term child scanned at 14 weeks of age is shown. Displayed are a synthesized T2W image (TE/TR = 100/15000 ms) (top row) and the CSF segmentation (bottom row). The red line indicates the outline of the ICV. Other segmented tissues are white matter (WM), grey matter (GM), myelin and ‘other tissue’.

Table 1: The results of the linear regressions performed
on the quantities, for the entire preterm population. For the absolute volumes,
sex was found to be a factor for both GM and BPV. For the fractions, gestational
age at birth was a relevant factor for BPF, GMF and CSF fraction. There were no
regressions which included both sex and gestational age as relevant factors.
All volumes are in ml, while fractions are in percentages.

Table 2: The results of the linear regressions for the
neonatal population. For the absolute volumes, the preterm born patients were
also included, and both sex and corrected age were found to be a factor for GM and BPV, while the CSF volume and ICV are only dependent on the
corrected age. For the fractions, only the term-born patients were tested, and
no factors were found to be significant for the regression analysis. All
volumes are in ml, while fractions are in percentages.