Kerkelä Leevi, Henriques Rafael Neto, Hall Matt G, Clark Chris A, Shemesh Noam
UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.
Champalimaud Neuroscience Programme, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.
Magn Reson Med. 2020 May;83(5):1698-1710. doi: 10.1002/mrm.28048. Epub 2019 Oct 25.
Double diffusion encoding (DDE) MRI enables the estimation of microscopic diffusion anisotropy, yielding valuable information on tissue microstructure. A recent study proposed that the acquisition of rotationally invariant DDE metrics, typically obtained using a spherical "5-design," could be greatly simplified by assuming Gaussian diffusion, facilitating reduced acquisition times that are more compatible with clinical settings. Here, we aim to validate the new minimal acquisition scheme against the standard DDE 5-design, and to quantify the proposed method's noise robustness to facilitate future clinical use.
DDE MRI experiments were performed on both ex vivo and in vivo rat brains at 9.4 T using the 5-design and the proposed minimal design and taking into account the difference in the number of acquisitions. The ensuing microscopic fractional anisotropy (μFA) maps were compared over a range of b-values up to 5000 s/mm . Noise robustness was studied using analytical calculations and numerical simulations.
The minimal protocol quantified μFA at an accuracy comparable to the estimates obtained by means of the more theoretically robust DDE 5-design. μFA's sensitivity to noise was found to strongly depend on compartment anisotropy and tensor magnitude in a nonlinear manner. When μFA < 0.75 or when mean diffusivity is particularly low, very high signal-to-noise ratio is required for precise quantification of µFA.
Our work supports using DDE for quantifying microscopic diffusion anisotropy in clinical settings but raises hitherto overlooked precision issues when measuring μFA with DDE and typical clinical signal-to-noise ratio.
双扩散编码（DDE）磁共振成像（MRI）能够估计微观扩散各向异性，提供有关组织微观结构的有价值信息。最近的一项研究提出，通过假设高斯扩散，可以大大简化通常使用球形“5设计”获得的旋转不变DDE指标的采集，从而缩短采集时间，使其更符合临床环境。在这里，我们旨在针对标准DDE 5设计验证新的最小采集方案，并量化所提出方法的噪声鲁棒性，以促进其未来的临床应用。
在9.4T磁场下，对离体和活体大鼠脑进行DDE MRI实验，分别采用5设计和提出的最小设计，并考虑采集次数的差异。在高达5000 s/mm²的一系列b值范围内，比较随后得到的微观分数各向异性（μFA）图。使用解析计算和数值模拟研究噪声鲁棒性。
最小方案量化μFA的准确性与通过理论上更稳健的DDE 5设计获得的估计值相当。发现μFA对噪声的敏感性强烈地以非线性方式依赖于区室各向异性和张量大小。当μFA < 0.75或平均扩散率特别低时，精确量化μFA需要非常高的信噪比。
我们的工作支持在临床环境中使用DDE来量化微观扩散各向异性，但在用DDE和典型临床信噪比测量μFA时，提出了迄今被忽视的精度问题。
