Assessment of brain development and severity of pulmonary
hypoplasia
Biometric measurements were made on standard T2-weighted images, and
included brain and skull biparietal diameter (BPD) and fronto-occipital
diameter (FOD), atrial width and transverse cerebellar diameter (TCD),
measured following Garel et al. 13 (figure 1) Head
circumference (HC) and extra-axial space percentiles were calculated
according to Kyiakopoulou et al . 12 Fetal cortical
development was scored using the grading system described by Pistorius
et al. 15 and measured following EgaƱa-Ugrinovic et
al.16, which we have used and reported previously.17 The following brain regions were scored
subjectively (by D.E. supervised by M.A.): frontal, parietal, temporal,
mesial, insular and occipital cortex. 15 Selected
primary sulci and gyri were graded and/or measured, including the
parieto-occipital fissure, the central, calcarine, superior temporal,
cingulate sulcus and, for the opercularization, the Sylvian
fissure.15, 16 In addition to the Sylvian fissure
depth, which reflects the distance between the inner part of the skull
and the insular cortex, the insular depth was also measured, i.e. the
distance from the midline to the insular cortex.17 The
sum of all the graded fissures provides a total grading score for the
whole brain as well as for each hemisphere. 18
3D super-resolution reconstruction (SRR) volumes were created from the
standard T2-weighted 2D stacks displaying the fetal brain, using
NiftyMIC, a publicly available and state-of-the-art SRR
algorithm.19 The SRR volumes were automatically
segmented for white matter, ventricular system (lateral ventricles,
third ventricle, fourth ventricle and aqueduct) and the cavum septi
pellucidi and cavum vergae, extra-axial space and cerebellum with manual
correction when necessary (D.E. supervised by M.A). A deep learning
algorithm for the automatic segmentation of white matter, ventricular
system, and cerebellum was used for the first volumes that were
processed.20 As the number of volumes segmented for
the extra-axial space increases, we trained a new deep learning-based
segmentation algorithm21 based on a partially
supervised learning method that segments automatically white matter,
ventricular system, cerebellum, and extra-axial space. These
segmentations were used for volumetric analysis when the quality of the
SRR volume allowed further analysis (determined by D.E. supervised by
M.A. and L.F.). (Figure 2)
In all cases were the fetus underwent FETO the date of it was noted and
the time interval between operation and second MRI was documented. The
severity of the pulmonary hypoplasia in fetuses with CDH was assessed on
MR images by measuring the right, left and total fetal lung volume
(TFLV), the fetal body volume (FBV), liver position, intra-thoracic
liver volume and thoracic volume, again measured manually (D.E.
supervised by M.A.). From those, we calculated the O/E TFLV ratio22 and the liver-to-thoracic volume ratio (LiTR).23 The latter ratios provide biometric measurements in
the index case, that are independent of gestational age and /or fetal
weight.