用隐式神经表示加速胎儿脑时空图谱构建，省去繁琐重建步骤。
INFANiTE: Implicit Neural representation for high-resolution Fetal brain spatio-temporal Atlas learNing from clinical Thick-slicE MRI
- 用隐式神经表征直接处理厚层MRI，跳过传统重建和配准流程。
- 在稀疏数据下仍保持高一致性与生物合理性，端到端耗时从天级降至小时级。
- 适合大规模胎儿脑发育研究，尤其适合临床队列数据快速分析。
胎儿脑时空图谱对刻画正常神经发育与识别先天异常至关重要。现有图谱构建方法需数日完成切片到体积分割（SVR），再经多轮非刚性配准，难以应用于大规模队列。本文提出INFANiTE框架，基于隐式神经表示（INR）直接从临床厚层MRI构建高分辨率胎儿脑时空图谱，完全跳过耗时的SVR与迭代配准步骤，显著加速图谱生成。大量实验表明，即便在数据稀疏条件下，INFANiTE在个体一致性、参考图像保真度、内在质量与生物学合理性方面均优于现有基线。相比传统3D体积管道（如SyGN），其端到端处理时间由数日缩短至数小时，极大推动了群体水平胎儿脑分析的发展。
原文摘要 · Abstract (English)
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. Code: https://github.com/hu2274898/INFANiTE
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。