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. 2024 Dec;34(4):859-869.
doi: 10.1007/s00062-024-01432-0. Epub 2024 Jun 26.

Evaluation of Neonatal Cerebral Circulation Under Hypoxic Ischemic Risk Factors Based on Quantitative Analysis of Cerebral Veins with Magnetic Resonance Susceptibility Weighted Imaging

Qi Xie  1 , Yan-Hui Liao  2   3 , Wen-Juan He  2 , Peng-Peng Han  4 , Jun Wu  4
Affiliations

Evaluation of Neonatal Cerebral Circulation Under Hypoxic Ischemic Risk Factors Based on Quantitative Analysis of Cerebral Veins with Magnetic Resonance Susceptibility Weighted Imaging

Qi Xie et al. Clin Neuroradiol. 2024 Dec.

Abstract

Purpose: To observe the regulation of cerebral circulation in vivo based on image segmentation algorithms for deep learning in medical imaging to automatically detect and quantify the neonatal deep medullary veins (DMVs) on susceptibility weighted imaging (SWI) images. To evaluate early cerebral circulation self-rescue for neonates undergoing risk of cerebral hypoxia-ischaemia in vivo.

Methods: SWI images and clinical data of 317 neonates with or without risk of cerebral hypoxia-ischaemia were analyzed. Quantitative parameters showing the number, width, and curvature of DMVs were obtained using an image segmentation algorithm.

Results: The number of DMVs was greater in males than in females (p < 0.01), and in term than in preterm infants (p = 0.001). The width of DMVs was greater in term than in preterm infants (p < 0.01), in low-risk than in high-risk group (p < 0.01), and in neonates without intracranial extracerebral haemorrhage (ICECH) than with ICECH (p < 0.05). The curvature of DMVs was greater in term than in preterm infants (P < 0.05). The width of both bilateral thalamic veins and anterior caudate nucleus veins were positively correlated with the number of DMVs; the width of bilateral thalamic veins was positively correlated with the width of DMVs.

Conclusion: The DMVs quantification based on image segmentation algorithm may provide more detailed and stable quantitative information in neonate. SWI vein quantification may be an observable indicator for in vivo assessment of cerebral circulation self-regulation in neonatal hypoxic-ischemic brain injury.

Keywords: DMVs; Image Segmentation Algorithm; MRI; Neonatal Hypoxic-Ischemia; SWI.

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Conflict of interest statement

Declarations Conflict of interest Q. Xie, Y.-H. Liao, W.-j. He, P.-p. Han and J. Wu declare that they have no competing interests. Ethical standards All procedures performed in studies involving human participants or on human tissue were in accordance with the declaration of Helsinki (as revised in 2013). Informed consent was obtained from participants’ parents before MR scans.

Figures

Fig. 1
Fig. 1
Positioning Map of reconstructed axial images used for quantitative analysis
Fig. 2
Fig. 2
Automatic identification and quantitative analysis of DMVs. a Original MinIP image. b Divided DMVs. c Overlay and quantization of the segmented DMVs with the Original MinIP image
Fig. 3
Fig. 3
a Common routes of the TSV and ACV. 1. Anterior septal vein (ASV); 2. Anterior caudate vein (ACV); 3. Thalamostriate vein (TSV); 4. Transverse vein of caudate nucleus; 5 external straight vein; 6. Medial lateral ventricular vein (MLVV); 7. Internal cerebral vein (ICV); 8. Intraoccipital vein; 9. The deep medullary vein (DMV) flowing into ASV; 10. DMV flowing into transverse vein of caudate nucleus; 11. DMV flowing into MLVV. b The right TSV and the ACV are not shown, and an abnormal vein compensatory widening (thin white arrow) is seen locally. c The left TSV and ACV are thin, and an abnormally widened vein (thick white arrow) is seen locally
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