SPHERIOUSLY? The challenges of estimating spherical pore size non-invasively in the human brain from diffusion MRI
SPHERIOUSLY? The challenges of estimating spherical pore size non-invasively in the human brain from diffusion MRI
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球形？
DOI：
10.1101/2020.11.06.371740
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Derek K. Jones
中科院分区：
文献类型：
--
作者：
M. Afzali;M. Nilsson;M. Palombo;Derek K. Jones
最近提出的索马和神经突密度成像（SANDI）三室模型，解开圆柱形和球形的几何形状，分别归因于神经突和索马室，在脑组织中。该方法还可以使微结构参数，如表观尺寸（半径）的索马的估计。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”编码和在频域中分析信号），但尚未应用于SANDI。在这项工作中，使用：（i）超强梯度;（ii）线性、平面和球形b-张量编码的组合;以及（iii）在频域中分析信号，识别了对索马大小的鲁棒估计的三个主要挑战：首先，幅度重建数据中的Rician噪声基底以非均匀的方式使索马性质的估计产生偏差。它可能会导致高估或低估的索马的大小和密度。这可以通过在估计例程中考虑噪声本底来部分地改善。其次，即使当使用最强的扩散编码梯度强度可用于人类MRI，有一个经验的球形信号分数和孔大小，可以检测和估计鲁棒性的下限。对于这里使用的实验设置，信号分数的下限约为10%。我们采用了两种不同的方法来建立球半径估计的下限在白色的问题。第一个，检查经验数据中DW信号和扩散加权之间的幂律关系，得到7 μm的下限，而第二个，纯蒙特卡罗模拟，得到3 μm的下限，在这个低半径域中，信号衰减几乎没有差异。第三，如果对圆柱形结构中的横向细胞内扩散率敏感，例如，轴突和细胞投射，然后尝试使用一个实验参数解开两个扩散时间依赖性（即，编码波形的频率内容的变化）使得球形孔尺寸估计特别具有挑战性。我们的结论是，由于上述挑战，当相应的信号分数较低时，球形孔径估计可能会有偏差，这在临床/研究中使用它们作为生物标志物时必须考虑到这一点。
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. The approach could also enable estimation of microstructure parameters such as the apparent size (radius) of the soma. 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 soma size were identified: First, the Rician noise floor in magnitude-reconstructed data biases estimates of soma properties in a non-uniform fashion. It may cause overestimation or underestimation of the soma 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 pore-size that can be detected and estimated robustly. For the experimental setup used here, the lower bound on the 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 7 μm, while the second, pure Monte Carlo simulations, yielded a lower limit of 3 μm 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 pore-size estimates particularly challenging. We conclude that due to the aforementioned challenges spherical pore size estimates may be biased when the corresponding signal fraction is low, which must be considered when using them as biomarkers in clinical/research studies.
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影响因子：
16.6
作者：
Bansal V;Mitjans M;Burik CAP;Linnér RK;Okbay A;Rietveld CA;Begemann M;Bonn S;Ripke S;de Vlaming R;Nivard MG;Ehrenreich H;Koellinger PD
通讯作者：
Koellinger PD
影响因子：
3
作者：
Afzali M;Pieciak T;Newman S;Garyfallidis E;Özarslan E;Cheng H;Jones DK
通讯作者：
Jones DK
影响因子：
2.5
作者：
McKinnon ET;Jensen JH;Glenn GR;Helpern JA
通讯作者：
Helpern JA
Deep-Channel 使用深度神经网络从膜片钳数据中检测单分子事件
DOI：
10.1101/767418
发表时间：
2019
期刊：
--
影响因子：
--
作者：
Celik N
通讯作者：
Celik N
人类深部大脑 MRI 图谱。
影响因子：
3.4
作者：
Lemaire,Jean-Jacques;DeSalles,Antonio;Coll,Guillaume;ElOuadih,Youssef;Chaix,Rémi;Coste,Jérôme;Durif,Franck;Makris,Nikos;Kikinis,Ron
通讯作者：
Kikinis,Ron