Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
Neuroimage. 2019 Apr 1;189:202-213. doi: 10.1016/j.neuroimage.2019.01.034. Epub 2019 Jan 14.
Current chemical exchange saturation transfer (CEST) neuroimaging protocols typically acquire CEST-weighted images, and, as such, do not essentially provide quantitative proton-specific exchange rates (or brain pH) and concentrations. We developed a dictionary-free MR fingerprinting (MRF) technique to allow CEST parameter quantification with a reduced data set. This was accomplished by subgrouping proton exchange models (SPEM), taking amide proton transfer (APT) as an example, into two-pool (water and semisolid macromolecules) and three-pool (water, semisolid macromolecules, and amide protons) models. A variable radiofrequency saturation scheme was used to generate unique signal evolutions for different tissues, reflecting their CEST parameters. The proposed MRF-SPEM method was validated using Bloch-McConnell equation-based digital phantoms with known ground-truth, which showed that MRF-SPEM can achieve a high degree of accuracy and precision for absolute CEST parameter quantification and CEST phantoms. For in-vivo studies at 3 T, using the same model as in the simulations, synthetic Z-spectra were generated using rates and concentrations estimated from the MRF-SPEM reconstruction and compared with experimentally measured Z-spectra as the standard for optimization. The MRF-SPEM technique can provide rapid and quantitative human brain CEST mapping.
目前的化学交换饱和传递(CEST)神经影像学方案通常采集 CEST 加权图像,因此不能从本质上提供定量的质子特异性交换率(或脑 pH 值)和浓度。我们开发了一种无字典的磁共振指纹图谱(MRF)技术,通过减少数据集来实现 CEST 参数的定量。这是通过分组质子交换模型(SPEM)来实现的,以酰胺质子转移(APT)为例,将其分为两池(水和半固态大分子)和三池(水、半固态大分子和酰胺质子)模型。采用可变射频饱和方案为不同组织生成独特的信号演化,反映其 CEST 参数。该方法使用基于 Bloch-McConnell 方程的数字体模进行了验证,这些体模具有已知的真实值,结果表明,MRF-SPEM 可以实现绝对 CEST 参数定量和 CEST 体模的高精度和高精确度。在 3T 的体内研究中,使用与模拟中相同的模型,使用从 MRF-SPEM 重建中估计的速率和浓度生成合成 Z 谱,并将其与实验测量的 Z 谱进行比较,作为优化的标准。MRF-SPEM 技术可以提供快速和定量的人脑 CEST 映射。