Department of Physics and Astronomy, BioImaging Research Center (BIRC), The University of Georgia, Athens, Georgia 30602, USA.
Magn Reson Med. 2011 May;65(5):1461-9. doi: 10.1002/mrm.22727. Epub 2010 Nov 30.
The susceptibility gradients generated by super-paramagnetic iron oxide (SPIO) nanoparticles make them an ideal contrast agent in magnetic resonance imaging. Traditional quantification methods for SPIO nanoparticle-based contrast agents rely on either mapping T₂* values within a region or by modeling the magnetic field inhomogeneities generated by the contrast agent. In this study, a new model-based SPIO quantification method is introduced. The proposed method models magnetic field inhomogeneities by approximating regions containing SPIOs as ensembles of magnetic dipoles, referred to as the finite perturber method. The proposed method was verified using data acquired from a phantom and in vivo mouse models. The phantom consisted of an agar solution with four embedded vials, each vial containing known but different concentrations of SPIO nanoparticles. Gaussian noise was also added to the phantom data to test performance of the proposed method. The in vivo dataset was acquired using five mice, each of which was subcutaneously implanted in the flanks with 1 × 10(5) labeled and 1 × 10(6) unlabeled C6 glioma cells. For the phantom data set, the proposed algorithm was generate accurate estimations of the concentration of SPIOs. For the in vivo dataset, the method was able to give estimations of the concentration within SPIO-labeled tumors that are reasonably close to the known concentration.
超顺磁性氧化铁 (SPIO) 纳米粒子产生的磁化率梯度使它们成为磁共振成像中理想的对比剂。传统的 SPIO 基于纳米粒子的对比剂定量方法要么依赖于在某个区域内绘制 T₂* 值,要么依赖于对对比剂产生的磁场不均匀性进行建模。在这项研究中,引入了一种新的基于模型的 SPIO 定量方法。该方法通过将包含 SPIOs 的区域近似为磁偶极子的集合来对磁场不均匀性进行建模,这种方法被称为有限扰体方法。该方法使用来自体模和体内小鼠模型的数据进行了验证。体模由含有四个嵌入小瓶的琼脂溶液组成,每个小瓶含有已知但浓度不同的 SPIO 纳米粒子。还向体模数据中添加了高斯噪声,以测试所提出方法的性能。体内数据集是使用五只小鼠获得的,每只小鼠的侧翼皮下植入了 1×10(5)个标记和 1×10(6)个未标记的 C6 神经胶质瘤细胞。对于体模数据集,所提出的算法能够准确估计 SPIOs 的浓度。对于体内数据集,该方法能够给出 SPIO 标记肿瘤内浓度的合理估计,与已知浓度相当接近。