用于联合 B0 估计与回波平面成像的物理信息隐式神经表示
Physics-Informed Implicit Neural Representations for Joint B0 Estimation and Echo Planar Imaging
AI总结：
本文提出融合 INRs 与物理信息模型的旋转视角 EPI 联合框架，实现 B0 场估计与无畸变图像重建，并在 180 层脑图像上验证其优势。
摘要：
回波平面成像（Echo Planar Imaging，EPI）因采集快速而被广泛使用，但会受到 B0 不均匀性导致的严重几何畸变影响，尤其是沿相位编码方向。现有方法遵循两步流程：先重建 blip-up/down EPI 图像，再估计 B0，这会引入误差累积并降低校正精度。该问题在高 B0 区域尤其突出，因为畸变沿同一轴对齐，使其更难被分离。在本研究中，我们提出一种新方法，将隐式神经表示（Implicit Neural Representations，INRs）与物理信息校正模型相结合，从旋转视角 EPI 采集中联合估计 B0 不均匀性并重建无畸变图像。INRs 提供灵活、连续的表示，能够内在地捕捉复杂空间变化，而无需预定义的基于网格的场图。通过利用这一特性，我们的方法可动态适应受试者特异性 B0 变化，并提升不同成像条件下的鲁棒性。在来自三名受试者的 180 层脑部图像上的实验结果表明，我们的方法在重建质量和场估计精度方面优于传统方法。
英文摘要：
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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