Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA.
School of Health Sciences, Purdue University, West Lafayette, Indiana.
NMR Biomed. 2021 May;34(5):e4275. doi: 10.1002/nbm.4275. Epub 2020 Feb 20.
The purpose of this work is to develop and validate a new atlas-based metabolite quantification pipeline for edited magnetic resonance spectroscopic imaging (MEGA-MRSI) that enables group comparisons of brain structure-specific GABA levels. By using brain structure masks segmented from high-resolution MPRAGE images and coregistering these to MEGA-LASER 3D MRSI data, an automated regional quantification of neurochemical levels is demonstrated for the example of the thalamus. Thalamic gamma-aminobutyric acid + coedited macromolecules (GABA+) levels from 21 healthy subjects scanned at 3 T were cross-validated both against a single-voxel MEGA-PRESS acquisition in the same subjects and same scan sessions, as well as alternative MRSI processing techniques (ROI approach, four-voxel approach) using Pearson correlation analysis. In addition, reproducibility was compared across the MRSI processing techniques in test-retest data from 14 subjects. The atlas-based approach showed a significant correlation with SV MEGA-PRESS (correlation coefficient r [GABA+] = 0.63, P < 0.0001). However, the actual values for GABA+, NAA, tCr, GABA+/tCr and tNAA/tCr obtained from the atlas-based approach showed an offset to SV MEGA-PRESS levels, likely due to the fact that on average the thalamus mask used for the atlas-based approach only occupied 30% of the SVS volume, ie, somewhat different anatomies were sampled. Furthermore, the new atlas-based approach showed highly reproducible GABA+/tCr values with a low median coefficient of variance of 6.3%. In conclusion, the atlas-based metabolite quantification approach enables a more brain structure-specific comparison of GABA+ and other neurochemical levels across populations, even when using an MRSI technique with only cm-level resolution. This approach was successfully cross-validated against the typically used SVS technique as well as other different MRSI analysis methods, indicating the robustness of this quantification approach.
这项工作的目的是开发和验证一种新的基于图谱的代谢物定量分析方法，用于编辑磁共振波谱成像（MEGA-MRSI），以实现脑结构特异性 GABA 水平的组间比较。通过使用从高分辨率 MPRAGE 图像分割的脑结构掩模，并将这些掩模配准到 MEGA-LASER 3D MRSI 数据，我们展示了一种自动的神经化学水平区域定量方法，以丘脑为例。21 名健康受试者在 3T 下进行扫描，使用该方法对其丘脑的伽马氨基丁酸+共编辑大分子（GABA+）水平进行定量分析，并与同一受试者同一扫描序列中单体 MEGA-PRESS 采集进行交叉验证，同时还与其他 MRSI 处理技术（ROI 方法、四体素方法）进行了比较，使用 Pearson 相关分析。此外，还在 14 名受试者的测试-重测数据中比较了不同 MRSI 处理技术的可重复性。基于图谱的方法与 SV MEGA-PRESS 具有显著相关性（GABA+的相关系数 r = 0.63，P < 0.0001）。然而，基于图谱的方法获得的 GABA+、NAA、tCr、GABA+/tCr 和 tNAA/tCr 的实际值与 SV MEGA-PRESS 水平存在偏移，这可能是由于用于基于图谱的方法的丘脑掩模平均仅占据 SV 体素的 30%，即采样的解剖结构略有不同。此外，新的基于图谱的方法显示 GABA+/tCr 值具有高度可重复性，中值变异系数低至 6.3%。总之，基于图谱的代谢物定量方法能够更具脑结构特异性地比较群体间的 GABA+和其他神经化学水平，即使使用仅具有厘米级分辨率的 MRSI 技术也是如此。该方法成功地与通常使用的 SVS 技术以及其他不同的 MRSI 分析方法进行了交叉验证，表明了这种定量方法的稳健性。
