Institute of Geophysics, Sonneggstrasse 5, ETH-Zürich, CH-8092 Zürich, Switzerland.
nanoPET Pharma GmbH, Luisencarreé, Robert-Koch-Platz 4, D-10115 Berlin, Germany.
Molecules. 2017 Dec 12;22(12):2204. doi: 10.3390/molecules22122204.
Magnetic resonance imaging (MRI) and magnetic particle imaging (MPI) are powerful methods in the early diagnosis of diseases. Both imaging techniques utilize magnetic nanoparticles that have high magnetic susceptibility, strong saturation magnetization, and no coercivity. FeraSpin R and its fractionated products have been studied for their imaging performances; however, a detailed magnetic characterization in their immobilized state is still lacking. This is particularly important for applications in MPI that require fixation of magnetic nanoparticles with the target cells or tissues. We examine the magnetic properties of immobilized FeraSpin R, its size fractions, and Resovist, and use the findings to demonstrate which magnetic properties best predict performance. All samples show some degree of oxidation to hematite, and magnetic interaction between the particles, which impact negatively on image performance of the materials. MRI and MPI performance show a linear dependency on the slope of the magnetization curve, i.e., initial susceptibility, and average blocking temperature. The best performance of particles in immobilized state for MPI is found for particle sizes close to the boundary between superparamagnetic (SP) and magnetically ordered, in which only Néel relaxation is important. Initial susceptibility and bifurcation temperature are the best indicators to predict MRI and MPI performance.
磁共振成像(MRI)和磁共振粒子成像(MPI)是疾病早期诊断的有力方法。这两种成像技术都利用具有高磁敏感性、强饱和磁化强度和无矫顽力的磁性纳米粒子。FeraSpin R 及其分级产物已被研究用于其成像性能;然而,其固定状态下的详细磁性特征仍然缺乏。对于需要将磁性纳米粒子固定到靶细胞或组织中的 MPI 应用,这一点尤为重要。我们检查了固定化 FeraSpin R、其粒度分级和 Resovist 的磁性特性,并利用这些发现来证明哪些磁性特性最能预测性能。所有样品都显示出一定程度的向赤铁矿的氧化,以及颗粒之间的磁相互作用,这对材料的图像性能产生负面影响。MRI 和 MPI 性能与磁化曲线的斜率(即初始磁化率和平均阻塞温度)呈线性关系。对于 MPI 而言,在固定状态下颗粒的最佳性能出现在接近超顺磁性(SP)和磁性有序之间边界的粒径范围内,其中只有奈尔弛豫是重要的。初始磁化率和分叉温度是预测 MRI 和 MPI 性能的最佳指标。
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