Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom.
Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom.
Neuroimage. 2023 Apr 1;269:119930. doi: 10.1016/j.neuroimage.2023.119930. Epub 2023 Feb 5.
Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general.
时间弥散比(TDR)是一种新提出的弥散磁共振成像(dMRI)技术(Dell'Acqua 等人,在 ISMRM 2019 年会上发表),它提供了受限扩散区域与无受限扩散区域或长度尺度太小而无法进行特征描述的区域之间的对比度。因此,它有可能提供有关孔径的信息,特别是大轴突直径或其他细胞结构的存在。TDR 采用了两种相同的、大 b 值但扩散梯度波形不同的 dMRI 采集的信号。TDR 的优势在于它采用了标准的采集序列,不对基础组织结构做出任何假设,也不需要任何模型拟合,从而避免了与模型退化相关的问题。这项工作首次在模拟中引入并优化了 TDR 方法,涵盖了一系列不同的组织和扫描仪限制,并在临床前演示中进行了验证。我们同时考虑了包含圆柱体和球体结构的基质,这些结构代表了组织中的细胞体。我们的结果表明,对比短梯度持续时间、短扩散时间和高梯度强度的采集与长梯度持续时间、长扩散时间和低梯度强度的采集,可以最大限度地提高 TDR 对比度,适用于广泛的孔径配置。此外,在存在瑞利噪声的情况下,与最初提出的从采集的扩散梯度的子集(50%或更少)而不是整个壳计算 TDR 相比,计算 TDR 进一步提高了对比度。在工作的最后一部分,实验结果在大鼠脊髓上得到了验证。与模拟结果一致,实验数据表明,优化后的 TDR 与非优化的 TDR 相比,对比度得到了提高。此外,我们发现 TDR 与轴突直径的组织学测量之间存在很强的相关性。总之,我们发现 TDR 具有很大的潜力,是一种非常有前途的替代方法(或潜在的补充方法),可以提供有关孔径和一般受限扩散的信息,而不需要基于模型的方法。