Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark.
Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark.
Neuroimage. 2018 Nov 15;182:329-342. doi: 10.1016/j.neuroimage.2017.08.039. Epub 2017 Aug 14.
Biophysical modelling of diffusion MRI is necessary to provide specific microstructural tissue properties. However, estimating model parameters from data with limited diffusion gradient strength, such as clinical scanners, has proven unreliable due to a shallow optimization landscape. On the other hand, estimation of diffusion kurtosis (DKI) parameters is more robust, and its parameters may be connected to microstructural parameters, given an appropriate biophysical model. However, it was previously shown that this procedure still does not provide sufficient information to uniquely determine all model parameters. In particular, a parameter degeneracy related to the relative magnitude of intra-axonal and extra-axonal diffusivities remains. Here we develop a model of diffusion in white matter including axonal dispersion and demonstrate stable estimation of all model parameters from DKI in fixed pig spinal cord. By employing the recently developed fast axisymmetric DKI, we use stimulated echo acquisition mode to collect data over a two orders of magnitude diffusion time range with very narrow diffusion gradient pulses, enabling finely resolved measurements of diffusion time dependence of both net diffusion and kurtosis metrics, as well as model intra- and extra-axonal diffusivities, and axonal dispersion. Our results demonstrate substantial time dependence of all parameters except volume fractions, and the additional time dimension provides support for intra-axonal diffusivity to be larger than extra-axonal diffusivity in spinal cord white matter, although not unambiguously. We compare our findings for the time-dependent compartmental diffusivities to predictions from effective medium theory with reasonable agreement.
扩散 MRI 的生物物理建模对于提供特定的微观结构组织特性是必要的。然而，由于优化景观较浅，从具有有限扩散梯度强度的数据（如临床扫描仪）中估计模型参数已被证明是不可靠的。另一方面，扩散峰度（DKI）参数的估计更为稳健，并且在适当的生物物理模型下，其参数可能与微观结构参数相关。然而，以前已经表明，该过程仍然不能提供足够的信息来唯一确定所有模型参数。特别是，与轴内和轴外扩散率的相对大小有关的参数简并仍然存在。在这里，我们开发了一种包括轴突弥散在内的白质扩散模型，并证明了从固定猪脊髓的 DKI 中稳定地估计所有模型参数。通过采用最近开发的快速轴对称 DKI，我们使用激发回波采集模式在两个数量级的扩散时间范围内收集数据，扩散梯度脉冲非常窄，从而能够对净扩散和峰度度量以及模型的轴内和轴外扩散率以及轴突弥散的扩散时间依赖性进行精细分辨的测量。我们的结果表明，除了体积分数之外，所有参数都具有很大的时间依赖性，并且额外的时间维度支持脊髓白质中轴内扩散率大于轴外扩散率，尽管并不明确。我们将我们对时变隔室扩散率的发现与具有合理一致性的有效介质理论的预测进行了比较。
