Towards automatic US-MR fetal brain image registration with learning-based methods

• Published in: NeuroImage (Orlando, Fla.) Vol. 310; p. 121104
• Main Authors: Zeng, Qi, Liu, Weide, Li, Bo, Didier, Ryne, Grant, P. Ellen, Karimi, Davood
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.04.2025; Elsevier Limited; Elsevier
• Subjects: Brain - diagnostic imaging; Brain - embryology; Female; Fetal brain imaging; Fetuses; Humans; Image Processing, Computer-Assisted - methods; Image registration; Learning; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; MRI; Multimodal Imaging - methods; Neuroimaging; Neuroimaging - methods; Pregnancy; Prenatal Diagnosis - methods; Registration; Ultrasonography, Prenatal - methods; Ultrasound; Image registration; Fetal brain imaging; MRI; Ultrasound; Machine learning
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2025.121104

Fetal brain imaging is essential for prenatal care, with ultrasound (US) and magnetic resonance imaging (MRI) providing complementary strengths. While MRI has superior soft tissue contrast, US offers portable and inexpensive screening of neurological abnormalities. Despite the great potential synergy of combined fetal brain US and MR imaging to enhance diagnostic accuracy, little effort has been made to integrate these modalities. An essential step towards this integration is accurate automatic spatial alignment, which is technically very challenging due to the inherent differences in contrast and modality-specific imaging artifacts. In this work, we present a novel atlas-assisted multi-task learning technique to address this problem. Instead of training the registration model solely with intra-subject US-MR image pairs, our approach enables the network to also learn from domain-specific image-to-atlas registration tasks. This leads to an end-to-end multi-task learning framework with superior registration performance. Our proposed method was validated using a dataset of same-day intra-subject 3D US-MR image pairs. The results show that our method outperforms conventional optimization-based methods and recent learning-based techniques for rigid image registration. Specifically, the average target registration error for our method is less than 4 mm, which is significantly better than existing methods. Extensive experiments have also shown that our method has a much wider capture range and is robust to brain abnormalities. Given these advantages over existing techniques, our method is more suitable for deployment in clinical workflows and may contribute to streamlined multimodal imaging pipelines for fetal brain assessment. •Atlas-assisted multi-task learning method for 3D US-MR fetal brain registration.•Achieves average registration error under 4 mm, outperforming existing methods.•Offers a wider tracking range and robustness to brain abnormalities.•Suitable for clinical workflows and multimodal imaging pipelines.