INFANiTE: Implicit Neural representation for high-resolution Fetal brain spatio-temporal Atlas learNing from clinical Thick-slicE MRI
2026-05-11 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a new method called INFANiTE to create detailed 3D maps of fetal brains from MRI scans much faster than older methods. Their approach skips slow steps usually needed to reconstruct and align brain images, which often take days, reducing the process to hours. They tested INFANiTE extensively and found it produces more accurate and biologically meaningful brain atlases, even with limited data. This improvement helps researchers study fetal brain development in larger groups more practically.
fetal brain atlasMRIslice-to-volume reconstructionregistrationimplicit neural representationspatio-temporal atlas3D volume reconstructionneurodevelopmentnon-rigid registration
Authors
Xiaotian Hu, Mingxuan Liu, Hongjia Yang, Juncheng Zhu, Yijin Li, Yifei Chen, Haoxiang Li, Tongxi Song, Zihan Li, Yingqi Hao, Ziyu Li, Yujin Zhang, Gang Ning, Yi Liao, Haibo Qu, Qiyuan Tian
Abstract
Spatio-temporal fetal brain atlases are important for characterizing normative neurodevelopment and identifying congenital anomalies. However, existing atlas construction pipelines necessitate days for slice-to-volume reconstruction (SVR) to generate high-resolution 3D brain volumes and several additional days for iterative volume registration, thereby rendering atlas construction from large-scale cohorts prohibitively impractical. We address these limitations with INFANiTE, an Implicit Neural Representation (INR) framework for high-resolution Fetal brain spatio-temporal Atlas learNing from clinical Thick-slicE MRI scans, bypassing both the costly SVR and the iterative non-rigid registration steps entirely, thereby substantially accelerating atlas construction. Extensive experiments demonstrate that INFANiTE outperforms existing baselines in subject consistency, reference fidelity, intrinsic quality and biological plausibility, even under challenging sparse-data settings. Additionally, INFANiTE reduces the end-to-end processing time (i.e., from raw scans to the final atlas) from days to hours compared to the traditional 3D volume-based pipeline (e.g., SyGN), facilitating large-scale population-level fetal brain analysis. Our code is publicly available at: https://anonymous.4open.science/r/INFANiTE-5D74