Centre for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB) and Leeds Institute for Cardiac and Metabolic Medicine (LICAMM), School of Computing & School of Medicine, University of Leeds, Leeds, United Kingdom.
CISTIB, Electronic and Electrical Engineering Department, The University of Sheffield, Sheffield, United Kingdom.
Magn Reson Med. 2019 Jul;82(1):395-410. doi: 10.1002/mrm.27714. Epub 2019 Mar 13.
Biophysical tissue models are increasingly used in the interpretation of diffusion MRI (dMRI) data, with the potential to provide specific biomarkers of brain microstructural changes. However, it has been shown recently that, in the general Standard Model, parameter estimation from dMRI data is ill-conditioned even when very high b-values are applied. We analyze this issue for the Neurite Orientation Dispersion and Density Imaging with Diffusivity Assessment (NODDIDA) model and demonstrate that its extension from single diffusion encoding (SDE) to double diffusion encoding (DDE) resolves the ill-posedness for intermediate diffusion weightings, producing an increase in accuracy and precision of the parameter estimation.
We analyze theoretically the cumulant expansion up to fourth order in b of SDE and DDE signals. Additionally, we perform in silico experiments to compare SDE and DDE capabilities under similar noise conditions.
We prove analytically that DDE provides invariant information non-accessible from SDE, which makes the NODDIDA parameter estimation injective. The in silico experiments show that DDE reduces the bias and mean square error of the estimation along the whole feasible region of 5D model parameter space.
DDE adds additional information for estimating the model parameters, unexplored by SDE. We show, as an example, that this is sufficient to solve the previously reported degeneracies in the NODDIDA model parameter estimation.
生物物理组织模型越来越多地用于解释扩散磁共振成像（dMRI）数据，具有提供大脑微观结构变化的特定生物标志物的潜力。然而，最近已经表明，在一般标准模型中，即使应用非常高的 b 值，从 dMRI 数据进行参数估计也是病态的。我们分析了 Neurite Orientation Dispersion and Density Imaging with Diffusivity Assessment（NODDIDA）模型的这个问题，并证明其从单扩散编码（SDE）扩展到双扩散编码（DDE）解决了中间扩散权重的不适定性，从而提高了参数估计的准确性和精度。
我们从理论上分析了 SDE 和 DDE 信号在 b 上高达四阶的累积展开。此外，我们还进行了计算机模拟实验，以在类似噪声条件下比较 SDE 和 DDE 的能力。
我们从理论上证明了 DDE 提供了从 SDE 不可访问的不变信息，这使得 NODDIDA 参数估计具有单值性。计算机模拟实验表明，DDE 降低了整个 5D 模型参数空间可行区域的估计偏差和均方误差。
DDE 为估计模型参数添加了 SDE 未探索的附加信息。我们举例说明了这足以解决以前报告的 NODDIDA 模型参数估计中的退化问题。
