FetDTIAlign: A deep learning framework for affine and deformable registration of fetal brain dMRI

• Published in: NeuroImage (Orlando, Fla.) Vol. 311; p. 121190
• Main Authors: Li, Bo, Zeng, Qi, Warfield, Simon K., Karimi, Davood
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2025; Elsevier Limited; Elsevier
• Subjects: Brain - diagnostic imaging; Brain - embryology; Deep Learning; Diffusion Magnetic Resonance Imaging - methods; Diffusion MRI; Female; Fetal brain; Fetus - diagnostic imaging; Fetuses; Humans; Image Processing, Computer-Assisted - methods; Neuroimaging; Pregnancy; Registration; Spatial normalization; Substantia alba; White matter tracts; White matter tracts; Diffusion MRI; Spatial normalization; Fetal brain; Registration
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2025.121190

Diffusion MRI (dMRI) offers unique insights into the microstructure of fetal brain tissue in utero. Longitudinal and cross-sectional studies of fetal dMRI have the potential to reveal subtle but crucial changes associated with normal and abnormal neurodevelopment. However, these studies depend on precise spatial alignment of data across scans and subjects, which is particularly challenging in fetal imaging due to the low data quality, rapid brain development, and limited anatomical landmarks for accurate registration. Existing registration methods, primarily developed for superior-quality adult data, are not well-suited for addressing these complexities. To bridge this gap, we introduce FetDTIAlign, a deep learning approach tailored to fetal brain dMRI, enabling accurate affine and deformable registration. FetDTIAlign integrates a novel dual-encoder architecture and iterative feature-based inference, effectively minimizing the impact of noise and low resolution to achieve accurate alignment. Additionally, it strategically employs different network configurations and domain-specific image features at each registration stage, addressing the unique challenges of affine and deformable registration, enhancing both robustness and accuracy. We validated FetDTIAlign on a dataset covering gestational ages centered between 23 and 36 weeks, encompassing 60 white matter tracts. For all age groups, FetDTIAlign consistently showed superior anatomical correspondence and the best visual alignment in both affine and deformable registration, outperforming two classical optimization-based methods and a deep learning-based pipeline. Further validation on external data from the Developing Human Connectome Project demonstrated the generalizability of our method to data collected with different acquisition protocols. Our results show the feasibility of using deep learning for fetal brain dMRI registration, providing a more accurate and reliable alternative to classical techniques. By enabling precise cross-subject and tract-specific analyses, FetDTIAlign paves the way for new discoveries in early brain development. The code is available at https://gitlab.com/blibli/fetdtialign. •The first machine learning method for aligning diffusion MRI of the fetal brain.•Superior accuracy and robustness to standard and state-of-the-art techniques.•Supports cross-sectional and longitudinal studies of fetal brain development.