Wang Lu, Huang Yan, Zhao Yishen, Tian Jie, Zhang Lu, Du Yang
School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, 100069, China.
Mol Imaging Biol. 2023 Aug;25(4):788-797. doi: 10.1007/s11307-023-01812-x. Epub 2023 Mar 27.
Magnetic particle imaging (MPI) is a technique for imaging magnetic particle concentration distribution. It has the advantages of high sensitivity, no signal attenuation with depth, and no ionizing radiation. Although MPI has been widely used in the biomedical field, accurate image analysis has been challenging due to its anisotropic point spread function (PSF). The purpose of this study is to propose an MPI image restoring and segmentation method to facilitate a more precise quantitative evaluation of the magnetic particle imaging in vivo.
We proposed a DeRSF method that combined deblurring and region scalable fitting (RSF) to determine the imaging tracer distribution. Then a uniform erosion and scaling criterion was established based on simulation experiments to correct the segmentation results, which was further validated on phantom imaging. Finally, we imaged the MPI tracer at gradient concentrations to establish the calibration curve between the MPI signal and iron mass for iron quantification in phantom and in vivo imaging.
The phantom imaging experiments showed that our method achieved improved segmentation performance. The mean value of the dice coefficients for segmentation was up to 0.86, demonstrating that our method can accurately map and quantify the distribution of the tracer. Moreover, the iron quantification on both phantom and in vivo mouse imaging was realized with the minimal error of 5.50%, by our established calibration curve.
Our proposed DeRSF method was successfully used for improved MPI quantitative analysis. More importantly, this method also showed accurate quantitative results on images with different shapes and tracer concentrations in both phantom and in vivo data, which laid the foundation for the biomedical study of MPI.
磁粒子成像（MPI）是一种用于成像磁粒子浓度分布的技术。它具有高灵敏度、信号不随深度衰减以及无电离辐射等优点。尽管MPI已在生物医学领域广泛应用，但由于其各向异性点扩散函数（PSF），准确的图像分析一直具有挑战性。本研究的目的是提出一种MPI图像恢复和分割方法，以促进对体内磁粒子成像进行更精确的定量评估。
我们提出了一种结合去模糊和区域可扩展拟合（RSF）的DeRSF方法来确定成像示踪剂分布。然后基于模拟实验建立了统一的腐蚀和缩放准则来校正分割结果，并在体模成像上进一步验证。最后，我们对不同浓度梯度的MPI示踪剂进行成像，以建立MPI信号与铁质量之间的校准曲线，用于体模和体内成像中的铁定量分析。
体模成像实验表明，我们的方法实现了更好的分割性能。分割的骰子系数平均值高达0.86，表明我们的方法能够准确地映射和量化示踪剂的分布。此外，通过我们建立的校准曲线，在体模和小鼠体内成像上实现了铁定量分析，最小误差为5.50%。
我们提出的DeRSF方法成功用于改进MPI定量分析。更重要的是，该方法在体模和体内数据中不同形状和示踪剂浓度的图像上也显示出准确的定量结果，为MPI的生物医学研究奠定了基础。
