双流调控重建与分割网络：面向超低场儿科神经影像的层级伪影先验建模
A Dual-Stream Regulated Reconstruction and Segmentation Network with Hierarchical Artifact-Prior Modeling for Ultra-Low-Field Pediatric Neuroimaging
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中文总结 AI 辅助
针对超低场儿科MRI的低信噪比和伪影问题，提出双流调控重建与分割网络，结合层级伪影先验建模，在统一推理中完成质量评估、增强和分割，并通过配准缓解标注稀缺。
中文摘要 AI 辅助
在$0.064\\,\mathrm{T}$超低场儿科MRI中，多结构自动质量评估、增强和分割受限于低信噪比、弱解剖边界和频繁伪影。我们为LISA 2026挑战赛提出一个统一框架，在单一推理流程中同时执行这三项任务。一个基于3D U-Net构建的双耦合流网络，首先重建增强的超低场（uLF）体积，然后结合原始和增强图像进行皮层下结构分割。为提高边界稳定性，我们添加了一个辅助类别，覆盖目标结构之外的脑组织，该类别源自全脑掩膜。一个以伪影图为条件的头部网络，从重建残差和冻结的分割特征预测七项伪影评分。我们利用从图谱到目标的微分同胚配准进行标签传播，并正则化解剖重建，以解决密集标注稀缺的问题。我们报告了所有三项任务的验证结果。
英文摘要
Automated quality assessment, enhancement, and segmentation of multiple structures in $0.064\,\mathrm{T}$ ultra-low-field pediatric MRI are limited by a low signal-to-noise ratio, weak anatomical boundaries, and frequent artifacts. We present a unified framework for the LISA 2026 Challenge that performs all three tasks together within one inference pipeline. A network with two coupled streams, built on a 3D U-Net, first reconstructs an enhanced uLF volume and then combines the original and enhanced images for subcortical segmentation. To improve boundary stability, we add an auxiliary class covering brain tissue outside the target structures, derived from whole brain masks. A head conditioned on an artifact graph predicts the seven artifact ratings from reconstruction residuals and frozen segmentation features. We address the scarcity of dense annotations using diffeomorphic registration from atlas to target for label propagation and to regularize anatomical reconstruction. We report validation results across all three tasks.
发表机构
- Weill Cornell Medicine（威尔康奈尔医学院）
机构由 AI 辅助整理，请以论文原文为准。
