IEEE Trans Med Imaging. 2019 Sep;38(9):2070-2080. doi: 10.1109/TMI.2019.2896289. Epub 2019 Jan 31.
Magnetic particle imaging (MPI) is a relatively new medical imaging modality, which detects the nonlinear response of magnetic nanoparticles (MNPs) that are exposed to external magnetic fields. The system matrix (SM) method for MPI image reconstruction requires a time consuming system calibration scan prior to image acquisition, where a single MNP sample is measured at each voxel position in the field-of-view (FOV). The scanned sample has the maximum size of a voxel so that the calibration measurements have relatively poor signal-to-noise ratio (SNR). In this paper, we present the coded calibration scene (CCS) framework, where we place multiple MNP samples inside the FOV in a random or pseudo-random fashion. Taking advantage of the sparsity of the SM, we reconstruct the SM by solving a convex optimization problem with alternating direction method of multipliers using CCS measurements. We analyze the effects of filling rate, number of measurements, and SNR on the SM reconstruction using simulations and demonstrate different implementations of CCS for practical realization. We also compare the imaging performance of the proposed framework with that of a standard compressed sensing SM reconstruction that utilizes a subset of calibration measurements from a single MNP sample. The results show that CCS significantly reduces calibration time while increasing both the SM reconstruction and image reconstruction performances.
磁性粒子成像（MPI）是一种相对较新的医学成像方式，它可以检测到暴露在外部磁场中的磁性纳米粒子（MNPs）的非线性响应。MPI 图像重建的系统矩阵（SM）方法需要在图像采集前进行耗时的系统校准扫描，其中在视场（FOV）中的每个体素位置测量单个 MNP 样本。扫描样本的最大尺寸为体素尺寸，因此校准测量的信噪比（SNR）相对较差。在本文中，我们提出了编码校准场景（CCS）框架，其中我们以随机或伪随机方式在 FOV 内放置多个 MNP 样本。利用 SM 的稀疏性，我们通过使用 CCS 测量值使用交替方向乘子法求解凸优化问题来重建 SM。我们通过仿真分析了填充率、测量次数和 SNR 对 SM 重建的影响，并展示了 CCS 的不同实现方式以实现实际应用。我们还将提出的框架与利用单个 MNP 样本的校准测量子集的标准压缩感知 SM 重建的成像性能进行了比较。结果表明，CCS 可以显著减少校准时间，同时提高 SM 重建和图像重建性能。
