用位置先验提升磁粒子成像系统矩阵超分辨效率
Position-Prior-Guided Network for System Matrix Super-Resolution in Magnetic Particle Imaging
- 将物理位置对称性先验融入深度学习超分辨框架
- 2D与3D实验均显著缩短系统矩阵标定时间
- 适合医学影像重建与快速标定研究者
磁粒子成像（MPI）是一种新型医学成像技术，其重建依赖于系统矩阵（SM）。然而，SM校准通常耗时且需在系统参数变化时重复测量。现有方法虽采用基于深度学习的超分辨率（SR）技术加速校准，但未充分利用与SM相关的物理先验知识，如位置对称性。为此，本文将位置先验集成至现有校准框架中。基于理论分析，通过2D与3D SM超分辨实验，实证验证了引入位置先验的有效性，显著提升了校准效率。
原文摘要 · Abstract (English)
Magnetic Particle Imaging (MPI) is a novel medical imaging modality. One of the established methods for MPI reconstruction is based on the System Matrix (SM). However, the calibration of the SM is often time-consuming and requires repeated measurements whenever the system parameters change. Current methodologies utilize deep learning-based super-resolution (SR) techniques to expedite SM calibration; nevertheless, these strategies do not fully exploit physical prior knowledge associated with the SM, such as symmetric positional priors. Consequently, we integrated positional priors into existing frameworks for SM calibration. Underpinned by theoretical justification, we empirically validated the efficacy of incorporating positional priors through experiments involving both 2D and 3D SM SR methods.
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