Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey. National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey.
Phys Med Biol. 2019 Aug 21;64(16):165018. doi: 10.1088/1361-6560/ab3525.
Magnetic particle imaging (MPI) is a fast emerging biomedical imaging modality that exploits the nonlinear response of superparamagnetic iron oxide (SPIO) nanoparticles to image their spatial distribution. Previously, various scanning trajectories were analyzed for the system function reconstruction (SFR) approach, providing important insight regarding their image quality performances. While Cartesian trajectories remain the most popular choice for x-space-based reconstruction, recent work suggests that non-Cartesian trajectories such as the Lissajous trajectory may prove beneficial for improving image quality. In this work, we propose a generalized reconstruction scheme for x-space MPI that can be used in conjunction with any scanning trajectory. The proposed technique automatically tunes the reconstruction parameters from the scanning trajectory, and does not induce any additional blurring. To demonstrate the proposed technique, we utilize five different trajectories with varying density levels. Comparison to alternative reconstruction methods show significant improvement in image quality achieved by the proposed technique. Among the tested trajectories, the Lissajous and bidirectional Cartesian trajectories prove more favorable for x-space MPI, and the resolution of the images from these two trajectories can further be improved via deblurring. The proposed fully automated gridding reconstruction can be utilized with these trajectories to improve the image quality in x-space MPI.
磁共振粒子成像（MPI）是一种快速发展的生物医学成像模式，利用超顺磁氧化铁（SPIO）纳米粒子的非线性响应来对其空间分布进行成像。以前，已经对各种扫描轨迹进行了分析，以用于系统函数重建（SFR）方法，为其图像质量性能提供了重要的见解。虽然笛卡尔轨迹仍然是基于 x 空间重建的最受欢迎的选择，但最近的工作表明，像 Lissajous 轨迹这样的非笛卡尔轨迹可能有助于提高图像质量。在这项工作中，我们提出了一种用于 x 空间 MPI 的广义重建方案，该方案可以与任何扫描轨迹一起使用。所提出的技术可以自动从扫描轨迹调整重建参数，并且不会引起任何额外的模糊。为了证明所提出的技术，我们利用具有不同密度级别的五种不同轨迹进行演示。与替代重建方法的比较表明，所提出的技术在图像质量方面取得了显著的改善。在所测试的轨迹中，Lissajous 和双向笛卡尔轨迹对 x 空间 MPI 更为有利，并且这两个轨迹的图像分辨率可以通过去模糊进一步提高。所提出的全自动网格重建可以与这些轨迹一起用于提高 x 空间 MPI 的图像质量。
