INFANiTE: Implicit Neural representation for high-resolution Fetal brain spatio-temporal Atlas learNing from clinical Thick-slicE MRI
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, and Qiyuan Tian

TL;DR
INFANiTE introduces a novel implicit neural representation framework that significantly accelerates high-resolution fetal brain atlas construction from clinical MRI scans, outperforming traditional methods in quality and efficiency.
Contribution
The paper presents INFANiTE, a new INR-based method that eliminates the need for costly reconstruction and registration steps, enabling rapid and high-quality fetal brain atlas learning from thick-slice MRI.
Findings
Outperforms existing baselines in consistency and quality.
Reduces processing time from days to hours.
Effective even with sparse data settings.
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,…
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