Laboratory for Structural, Physiologic, and Functional Imaging, Department of Radiology, University of Pennsylvania Medical Center, Philadelphia, PA, USA.
Laboratory for Structural, Physiologic, and Functional Imaging, Department of Radiology, University of Pennsylvania Medical Center, Philadelphia, PA, USA.
Neuroimage. 2018 Jul 1;174:420-431. doi: 10.1016/j.neuroimage.2018.03.043. Epub 2018 Mar 23.
Quantitative BOLD (qBOLD), a non-invasive MRI method for assessment of hemodynamic and metabolic properties of the brain in the baseline state, provides spatial maps of deoxygenated blood volume fraction (DBV) and hemoglobin oxygen saturation (HbO) by means of an analytical model for the temporal evolution of free-induction-decay signals in the extravascular compartment. However, mutual coupling between DBV and HbO in the signal model results in considerable estimation uncertainty precluding achievement of a unique set of solutions. To address this problem, we developed an interleaved qBOLD method (iqBOLD) that combines extravascular R' and intravascular R mapping techniques so as to obtain prior knowledge for the two unknown parameters. To achieve these goals, asymmetric spin echo and velocity-selective spin-labeling (VSSL) modules were interleaved in a single pulse sequence. Prior to VSSL, arterial blood and CSF signals were suppressed to produce reliable estimates for cerebral venous blood volume fraction (CBV) as well as venous blood R (to yield HbO). Parameter maps derived from the VSSL module were employed to initialize DBV and HbO in the qBOLD processing. Numerical simulations and in vivo experiments at 3 T were performed to evaluate the performance of iqBOLD in comparison to the parent qBOLD method. Data obtained in eight healthy subjects yielded plausible values averaging 60.1 ± 3.3% for HbO and 3.1 ± 0.5 and 2.0 ± 0.4% for DBV in gray and white matter, respectively. Furthermore, the results show that prior estimates of CBV and HbO from the VSSL component enhance the solution stability in the qBOLD processing, and thus suggest the feasibility of iqBOLD as a promising alternative to the conventional technique for quantifying neurometabolic parameters.
定量血氧水平依赖（qBOLD）是一种非侵入性的 MRI 方法，用于评估基线状态下大脑的血液动力学和代谢特性，通过分析模型提供去氧血红蛋白体积分数（DBV）和血红蛋白氧饱和度（HbO）的空间图，该模型用于描述血管外隔室中自由感应衰减信号的时间演化。然而，信号模型中 DBV 和 HbO 之间的相互耦合导致估计不确定性相当大，从而无法获得唯一的解。为了解决这个问题，我们开发了一种交叠 qBOLD 方法（iqBOLD），该方法结合了血管外 R'和血管内 R 映射技术，以便为两个未知参数提供先验知识。为了实现这些目标，在单个脉冲序列中交错使用不对称自旋回波和速度选择自旋标记（VSSL）模块。在 VSSL 之前，动脉血液和 CSF 信号被抑制，以产生可靠的脑静脉血体积分数（CBV）以及静脉血 R（产生 HbO）估计值。从 VSSL 模块导出的参数图用于初始化 qBOLD 处理中的 DBV 和 HbO。在 3T 进行了数值模拟和体内实验，以评估 iqBOLD 与原始 qBOLD 方法相比的性能。在 8 名健康受试者中获得的数据产生了合理的数值，HbO 的平均值为 60.1±3.3%，灰质和白质中的 DBV 分别为 3.1±0.5%和 2.0±0.4%。此外，结果表明，VSSL 组件中 CBV 和 HbO 的先验估计增强了 qBOLD 处理中的解稳定性，因此表明 iqBOLD 作为量化神经代谢参数的传统技术的有前途替代方法是可行的。
