SPHERIOUSLY? The challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI.
SPHERIOUSLY? The challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI.
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DOI：
10.1016/j.neuroimage.2021.118183
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发表时间：
2021-08-15
期刊：
影响因子：
5.7
通讯作者：
Jones DK
中科院分区：
文献类型：
--
作者：
Afzali M;Nilsson M;Palombo M;Jones DK
最近提出了Soma and Neurite Density Imaging （SANDI）三室模型，用于解开脑组织中分别归因于神经突和体细胞室的圆柱形和球形几何形状。最近在弥散加权MRI信号编码和分析方面取得了一些进展（包括使用多个所谓的“b张量”编码和在频域分析信号），但这些进展尚未应用于SANDI。在这项工作中，使用：(i)超强梯度；（ii）线性、平面和球面b张量编码的组合；（iii）在频域分析信号，确定了球体尺寸鲁棒估计的三个主要挑战：首先，震级重建数据中的噪底会以不均匀的方式对球体特性进行估计。它可能导致高估或低估球形隔室的大小和密度。这可以通过考虑估计例程中的本底噪声来部分改善。其次，即使使用人类MRI可用的最强扩散编码梯度强度，球形信号分数和半径的经验下界也可以被检测和稳健估计。对于这里使用的实验装置，球体信号分数的下界约为10%。我们采用了两种不同的方法来建立白质球面半径估计的下界。第一种方法是在经验数据中检验dw信号与扩散权重之间的幂律关系，得出了的下界，而第二种方法是纯蒙特卡罗模拟，得出了的下界，并且在这个低半径域中，信号衰减几乎没有微分。第三，如果对圆柱形结构（如轴突和细胞投影）中的横向细胞内扩散系数有敏感性，那么尝试使用一个实验参数（即编码波形的频率含量的变化）解开两个扩散时间依赖性使得球面半径估计特别具有挑战性。我们得出的结论是，由于上述挑战，当相应的球体信号分数较低时，球面半径估计可能会有偏差，这必须加以考虑。
The Soma and Neurite Density Imaging (SANDI) three-compartment model was recently proposed to disentangle cylindrical and spherical geometries, attributed to neurite and soma compartments, respectively, in brain tissue. There are some recent advances in diffusion-weighted MRI signal encoding and analysis (including the use of multiple so-called ’b-tensor’ encodings and analysing the signal in the frequency-domain) that have not yet been applied in the context of SANDI. In this work, using: (i) ultra-strong gradients; (ii) a combination of linear, planar, and spherical b-tensor encodings; and (iii) analysing the signal in the frequency domain, three main challenges to robust estimation of sphere size were identified: First, the Rician noise floor in magnitude-reconstructed data biases estimates of sphere properties in a non-uniform fashion. It may cause overestimation or underestimation of the spherical compartment size and density. This can be partly ameliorated by accounting for the noise floor in the estimation routine. Second, even when using the strongest diffusion-encoding gradient strengths available for human MRI, there is an empirical lower bound on the spherical signal fraction and radius that can be detected and estimated robustly. For the experimental setup used here, the lower bound on the sphere signal fraction was approximately 10%. We employed two different ways of establishing the lower bound for spherical radius estimates in white matter. The first, examining power-law relationships between the DW-signal and diffusion weighting in empirical data, yielded a lower bound of , while the second, pure Monte Carlo simulations, yielded a lower limit of and in this low radii domain, there is little differentiation in signal attenuation. Third, if there is sensitivity to the transverse intra-cellular diffusivity in cylindrical structures, e.g., axons and cellular projections, then trying to disentangle two diffusion-time-dependencies using one experimental parameter (i.e., change in frequency-content of the encoding waveform) makes spherical radii estimates particularly challenging. We conclude that due to the aforementioned challenges spherical radii estimates may be biased when the corresponding sphere signal fraction is low, which must be considered.
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影响因子：
4.5
作者：
Danos, P;Baumann, B;Bogerts, B
通讯作者：
Bogerts, B
DOI：
10.1006/jmra.1995.1055
发表时间：
1995-03-01
期刊：
JOURNAL OF MAGNETIC RESONANCE SERIES A
影响因子：
--
作者：
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通讯作者：
CALLAGHAN, PT
影响因子：
5.7
作者：
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通讯作者：
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DOI：
10.1073/pnas.1010356107
发表时间：
2010-09-07
影响因子：
11.1
作者：
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通讯作者：
Kaas, Jon H.
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影响因子：
5.7
作者：
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通讯作者：
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