For newborns and neonates, ultrasound (US) is the most common imaging modality used for examinations due to its accessibility and ease of use. However, precise volume measurements remain limited in 2D, while MRI in newborns is typically avoided because of immobilization issues which may require sedation. The objective of this study is to assess and validate the lateral ventricular and total brain volumes obtained with an automatic segmentation method using cerebral trans-fontanelle 3D US. Infants aged between 2 and 8.5 months old were recruited, with both MRI and 3D US acquired on the same day was used to validate ventricular and brain volume measurements in comparison to MRI. Lateral ventricles were segmented on both the US (manually and with a proposed automatic fusion-based approach) and MRI, while brain volumes were estimated with an automatic segmentation method. Volumetric 3D US measurements were then evaluated with respect to age distribution. For the comparison between MRI and 3D US, strong inter-class correlations (ICC) were found for the ventricle volumes (manual: 5.9%  ±  2.5% difference (ICC  =  0.99); automatic: 6.0%  ±  2.6% difference (ICC  =  0.98)), as well as the total brain size, with a 3.0%  ±  1.3% difference (ICC  =  0.98). There was no statistically significant difference based on t-test and f-test for the lateral ventricles volume (t-test: p  =  0.542) and (f-test: p  =  0.738) and for the total brain volume (t-test: p  =  0.412) and (f-test: p  =  0.685) between MRI and 3D US. This study demonstrates that 3D US can be used to automatically assess lateral ventricular and total brain volumes with no significant difference to the MRI acquisitions. The highest correlations were obtained for infants under 8 months when the fontanelle is open.

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Ultrasound (US) can be used to assess brain development in newborns, as MRI is challenging due to immobilization issues, and may require sedation. Dilatation of the lateral ventricles in the brain is a risk factor for poorer neurodevelopment outcomes in infants. Hence, 3D US has the ability to assess the volume of the lateral ventricles similar to clinically standard MRI, but manual segmentation is time consuming. The objective of this study is to develop an approach quantifying the ratio of lateral ventricular dilatation with respect to total brain volume using 3D US, which can assess the severity of macrocephaly. Automatic segmentation of the lateral ventricles is achieved with a multi-atlas deformable registration approach using locally linear correlation metrics for US-MRI fusion, followed by a refinement step using deformable mesh models. Total brain volume is estimated using a 3D ellipsoid modeling approach. Validation was performed on a cohort of 12 infants, ranging from 2 to 8.5 months old, where 3D US and MRI were used to compare brain volumes and segmented lateral ventricles. Automatically extracted volumes from 3D US show a high correlation and no statistically significant difference when compared to ground truth measurements. Differences in volume ratios was 6.0 ± 4.8% compared to MRI, while lateral ventricular segmentation yielded a mean Dice coefficient of 70.8 ± 3.6% and a mean absolute distance (MAD) of 0.88 ± 0.2mm, demonstrating the clinical benefit of this tool in paediatric ultrasound.

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Macrocephaly has been associated with neurodevelopmental disorders; however, it has been mainly studied in the context of pathological or high-risk populations and little is known about its impact, as an isolated trait, on brain development in general population. Electroencephalographic (EEG) power spectral density (PSD) and signal complexity have shown to be sensitive to neurodevelopment and its alterations. We aimed to investigate the impact of macrocephaly as isolated trait on EEG signal as measured by power spectral density (PSD) and Multiscale Entropy (MSE) during the first year of life. We recorded high density EEG resting state activity of 74 healthy full-term infants, 50 control (26 girls) and 24 macrocephalic (12 girls) aged between 3 and 11 months. We used linear regression models to assess group and age effects on EEG power spectral density (PSD) and signal complexity. Sex and brain volume measures, obtained via a 3D transfontanellar ultrasound, were also included into the models to evaluate their contribution. Our results showed lower PSD of the low alpha (8-10Hz) frequency band and lower complexity in the macrocephalic group compared to the control group. In addition, we found an increase in low alpha (8.5-10Hz) PSD and in the complexity index with age. These findings suggest that macrocephaly as an isolated trait has a significant impact on brain activity during the first year of life.