Univ Rennes, Inserm, LTSI-UMR 1099, Rennes, France.
NMR Biomed. 2024 Nov;37(11):e5182. doi: 10.1002/nbm.5182. Epub 2024 Jul 12.
Currently, brain iron content represents a new neuromarker for understanding the physiopathological mechanisms leading to Parkinson's disease (PD). In vivo quantification of biological iron is possible by reconstructing magnetic susceptibility maps obtained using quantitative susceptibility mapping (QSM). Applying QSM is challenging, as up to now, no standardization of acquisition protocols and phase image processing has emerged from referenced studies. Our objectives were to compare the accuracy and the sensitivity of 10 QSM pipelines built from algorithms from the literature, applied on phantoms data and on brain data. Two phantoms, with known magnetic susceptibility ranges, were created from several solutions of gadolinium chelate. Twenty healthy volunteers from two age groups were included. Phantoms and brain data were acquired at 1.5 and 3 T, respectively. Susceptibility-weighted images were obtained using a 3D multigradient-recalled-echo sequence. For brain data, 3D anatomical T1- and T2-weighted images were also acquired to segment the deep gray nuclei of interest. Concerning in vitro data, the linear dependence of magnetic susceptibility versus gadolinium concentration and deviations from the theoretically expected values were calculated. For brain data, the accuracy and sensitivity of the QSM pipelines were evaluated in comparison with results from the literature and regarding the expected magnetic susceptibility increase with age, respectively. A nonparametric Mann-Whitney U-test was used to compare the magnetic susceptibility quantification in deep gray nuclei between the two age groups. Our methodology enabled quantifying magnetic susceptibility in human brain and the results were consistent with those from the literature. Statistically significant differences were obtained between the two age groups in all cerebral regions of interest. Our results show the importance of optimizing QSM pipelines according to the application and the targeted magnetic susceptibility range, to achieve accurate quantification. We were able to define the optimal QSM pipeline for future applications on patients with PD.
目前，脑铁含量是理解导致帕金森病（PD）的生理病理机制的一个新的神经标志物。通过重建使用定量磁化率映射（QSM）获得的磁化率图，可以对生物铁进行体内定量。由于迄今为止，参考研究中尚未出现采集协议和相位图像处理的标准化，因此应用 QSM 具有挑战性。我们的目标是比较来自文献算法构建的 10 个 QSM 管道的准确性和灵敏度，这些算法应用于体模数据和大脑数据。两个具有已知磁化率范围的体模是由几种钆螯合物溶液创建的。纳入了来自两个年龄组的 20 名健康志愿者。分别在 1.5 和 3 T 下采集体模和大脑数据。使用 3D 多梯度回波序列获得磁化率加权图像。对于大脑数据，还采集了 3D 解剖 T1 和 T2 加权图像，以分割感兴趣的深部灰质核。关于体外数据，计算了磁化率与钆浓度的线性关系和与理论预期值的偏差。对于大脑数据，分别根据文献结果和与年龄相关的预期磁化率增加，评估了 QSM 管道的准确性和灵敏度。使用非参数曼-惠特尼 U 检验比较了两个年龄组之间深部灰质核的磁化率定量。我们的方法使我们能够量化人脑的磁化率，并且结果与文献中的结果一致。在所有感兴趣的大脑区域，两个年龄组之间都获得了统计学上显著的差异。我们的结果表明，根据应用和目标磁化率范围优化 QSM 管道对于实现准确定量非常重要。我们能够为未来在 PD 患者中应用定义最佳的 QSM 管道。
