用隐式神经表示实现胎儿脑MRI快速高精度重建
Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations
- 基于隐式神经表示统一处理运动校正、异常值剔除和超分辨率
- 在严重运动伪影下重建质量优于当前最佳方法，速度提升50%
- 支持自监督元学习，适配真实与模拟数据，临床适用性强
从多幅受运动干扰的低分辨率2D切片进行高分辨率切片到体数据重建（SVR），是胎儿脑磁共振成像（MRI）等动态受试者图像诊断中的关键步骤。现有方法在严重运动或图像伪影下表现不佳，或需切片预对齐才能达到良好重建效果。本文提出一种新方法，完全基于隐式神经表示，实现运动校正、异常值处理与超分辨率重建一体化。模型可通过在模拟或真实数据上的自监督元学习，注入任务特定先验。在超过480次模拟与临床脑MRI重建实验中（来自不同中心数据），验证了该方法在严重运动和图像伪影下的有效性。结果表明，相比现有最优方法，重建质量显著提升，重建时间最多减少50%。
原文摘要 · Abstract (English)
High-resolution slice-to-volume reconstruction (SVR) from multiple motion-corrupted low-resolution 2D slices constitutes a critical step in image-based diagnostics of moving subjects, such as fetal brain Magnetic Resonance Imaging (MRI). Existing solutions struggle with image artifacts and severe subject motion or require slice pre-alignment to achieve satisfying reconstruction performance. We propose a novel SVR method to enable fast and accurate MRI reconstruction even in cases of severe image and motion corruption. Our approach performs motion correction, outlier handling, and super-resolution reconstruction with all operations being entirely based on implicit neural representations. The model can be initialized with task-specific priors through fully self-supervised meta-learning on either simulated or real-world data. In extensive experiments including over 480 reconstructions of simulated and clinical MRI brain data from different centers, we prove the utility of our method in cases of severe subject motion and image artifacts. Our results demonstrate improvements in reconstruction quality, especially in the presence of severe motion, compared to state-of-the-art methods, and up to 50% reduction in reconstruction time.
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