用神经隐式表示联合估计磁共振畸变场与重建图像
Physics-Informed Implicit Neural Representations for Joint B0 Estimation and Echo Planar Imaging
- 用隐式神经表示融合物理模型，同步优化畸变场与图像
- 在3名受试者180张脑部图像上提升重建质量与场图精度
- 特别适合高磁场区域的复杂畸变校正，对个体差异鲁棒
回波平面成像（EPI）因快速采集被广泛应用，但受B0不均匀性影响，沿相位编码方向产生严重几何畸变。现有方法采用两步流程：先重建blip-up/down EPI图像，再估计B0场，易导致误差累积，尤其在高B0区域，畸变方向一致更难分离。本文提出新方法，将隐式神经表示（INRs）与物理约束校正模型结合，从旋转视角EPI数据中联合估计B0不均匀性并重建无畸变图像。INRs提供灵活连续的空间表征，无需预设网格场图，能动态适应个体差异，增强不同成像条件下的鲁棒性。在三名受试者的180个脑部切片上实验表明，该方法在重建质量与场估计精度上均优于传统方法。
原文摘要 · Abstract (English)
Echo Planar Imaging (EPI) is widely used for its rapid acquisition but suffers from severe geometric distortions due to B0 inhomogeneities, particularly along the phase encoding direction. Existing methods follow a two-step process: reconstructing blip-up/down EPI images, then estimating B0, which can introduce error accumulation and reduce correction accuracy. This is especially problematic in high B0 regions, where distortions align along the same axis, making them harder to disentangle. In this work, we propose a novel approach that integrates Implicit Neural Representations (INRs) with a physics-informed correction model to jointly estimate B0 inhomogeneities and reconstruct distortion-free images from rotated-view EPI acquisitions. INRs offer a flexible, continuous representation that inherently captures complex spatial variations without requiring predefined grid-based field maps. By leveraging this property, our method dynamically adapts to subject-specific B0 variations and improves robustness across different imaging conditions. Experimental results on 180 slices of brain images from three subjects demonstrate that our approach outperforms traditional methods in terms of reconstruction quality and field estimation accuracy.
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