IMAGO7 Foundation, Pisa, Italy; IRCCS Fondazione Stella Maris, Pisa, Italy.
National Institute for Nuclear Physics, Pisa, Italy.
Neuroimage. 2019 Jul 15;195:362-372. doi: 10.1016/j.neuroimage.2019.03.047. Epub 2019 Mar 25.
Fully-quantitative MR imaging methods are useful for longitudinal characterization of disease and assessment of treatment efficacy. However, current quantitative MRI protocols have not been widely adopted in the clinic, mostly due to lengthy scan times. Magnetic Resonance Fingerprinting (MRF) is a new technique that can reconstruct multiple parametric maps from a single fast acquisition in the transient state of the MR signal. Due to the relative novelty of this technique, the repeatability and reproducibility of quantitative measurements obtained using MRF has not been extensively studied. Our study acquired test/retest data from the brains of nine healthy volunteers, each scanned on five MRI systems (two at 3.0 T and three at 1.5 T, all from a single vendor) located at two different centers. The pulse sequence and reconstruction algorithm were the same for all acquisitions. After registration of the MRF-derived M, T and T maps to an anatomical atlas, coefficients-of-variation (CVs) were computed to assess test/retest repeatability and inter-site reproducibility in each voxel, while a General Linear Model (GLM) was used to determine the voxel-wise variability between all confounders, which included test/retest, subject, field strength and site. Our analysis demonstrated an excellent repeatability (CVs of 2-3% for T, 5-8% for T, 3% for normalized-M) and a good reproducibility (CVs of 3-8% for T, 8-14% for T, 5% for normalized-M) in grey and white matter.
全定量磁共振成像方法可用于疾病的纵向特征描述和治疗效果评估。然而,目前的定量 MRI 方案尚未在临床上广泛采用,主要是由于扫描时间长。磁共振指纹成像(MRF)是一种新技术,它可以从磁共振信号的瞬态状态中单次快速采集重建多个参数图。由于这项技术相对较新,因此尚未广泛研究使用 MRF 获得的定量测量的可重复性和再现性。我们的研究从 9 名健康志愿者的大脑中获取了测试/重测数据,每个志愿者在 5 个 MRI 系统(2 个在 3.0T,3 个在 1.5T,均来自单个供应商)上进行扫描,位于两个不同的中心。所有采集的脉冲序列和重建算法都相同。将 MRF 衍生的 M、T 和 T 图配准到解剖图谱后,计算了系数-of-variation(CV),以评估每个体素的测试/重测重复性和跨站点再现性,而广义线性模型(GLM)用于确定所有混杂因素之间的体素间变异性,这些混杂因素包括测试/重测、受试者、磁场强度和站点。我们的分析表明,在灰质和白质中具有出色的重复性(T 的 CV 为 2-3%,T 的 CV 为 5-8%,归一化-M 的 CV 为 3%)和良好的再现性(T 的 CV 为 3-8%,T 的 CV 为 8-14%,归一化-M 的 CV 为 5%)。