在磁粒子成像中的系统矩阵任意超分辨率的连续隐性神经表示
Zhaoji Miao1, Liwen Zhang2,3, Jie Tian1,4,2,5
1School of Computer Science and Engineering, Southeast University, Nanjing 211189, People's Republic of China.
Physics in medicine and biology
|February 6, 2025
概括
这项研究引入了一种新的磁粒子成像 (MPI) 系统矩阵校准方法,使用连续隐式神经表示. 它可以使用单一模型实现任意规模的超级分辨率,大大降低成本并提高效率.
科学领域:
- 医疗成像医学成像
- 计算成像技术的成像
- 纳米技术 纳米技术
背景情况:
- 磁粒子成像 (MPI) 是一种使用磁场检测磁纳米粒子的新技术.
- 基于系统矩阵 (SM) 的重建对于MPI图像质量至关重要.
- 目前的SM校准是耗时的,需要经常重新校准,这阻碍了实际应用.
研究的目的:
- 为了加速磁粒子成像 (MPI) 中的系统矩阵 (SM) 校准过程.
- 为SM回收开发一种任意规模的超级分辨率方法,克服现有的低采样方法的局限性.
- 为了减少与SM校准相关的存储和培训时间成本.
主要方法:
- 提出了一种任意规模的SM超分辨率方法,利用连续隐式神经表示 (INR).
- 通过调整采样密度,模拟SM作为连续空间函数,以实现灵活的超分辨率.
- 实现了交叉频率编码器,用于共享频率信息和上下文分析,并与CNN相结合,以优化INR中的网格采样.
主要成果:
- 拟议的基于INR的方法在OpenMPI数据集上的现有技术相比显示出更高的性能.
- 实现了准确而高效的SM回收,即使在高低样本率下.
- 在任何尺度上使用单个训练模型启用校准.
结论:
- 这种新方法显著降低了MPI SM校准的计算成本和存储需求.
- 通过使用统一模型实现任意规模的超级分辨率,为MPI系统提供了更高的灵活性和效率.
- 通过更实用的校准程序,为MPI技术的更广泛采用铺平了道路.
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