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

2026-05-11

AI Summary

This study addresses the limitations of traditional approaches to fetal brain spatiotemporal atlas construction, which rely on time-consuming slice reconstruction and iterative registration, hindering scalability to large cohorts. The work proposes an end-to-end framework that, for the first time, leverages implicit neural representations (INRs) to directly learn a continuous, high-resolution spatiotemporal atlas from clinical thick-slice MRI scans, thereby circumventing both computational bottlenecks. The method demonstrates superior performance over existing techniques in terms of subject consistency, reference fidelity, intrinsic quality, and biological plausibility. Moreover, it drastically reduces processing time from several days to just a few hours, substantially enhancing computational efficiency and scalability for large-scale fetal neuroimaging studies.

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

Problem

• fetal brain atlas
• spatio-temporal modeling
• thick-slice MRI
• atlas construction
• high-resolution reconstruction

Innovation

• Implicit Neural Representation
• Fetal Brain Atlas
• Thick-slice MRI
• Spatio-temporal Modeling
• Slice-to-Volume Reconstruction