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评估通过单束信号求和来近似交叉束扩散加权 MRI 信号的有效性。

Assessing the validity of the approximation of diffusion-weighted-MRI signals from crossing fascicles by sums of signals from single fascicles.

机构信息

ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium.

Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

出版信息

Magn Reson Med. 2018 Apr;79(4):2332-2345. doi: 10.1002/mrm.26832. Epub 2017 Jul 16.

Abstract

PURPOSE

To assess the validity of the superposition approximation for crossing fascicles, i.e., the assumption that the total diffusion-weighted MRI signal is the sum of the signals arising from each fascicle independently, even when the fascicles intermingle in a voxel.

METHODS

Monte Carlo simulations were used to study the impact of the approximation on the diffusion-weighted MRI signal and to assess whether this approximate model allows microstructural features of interwoven fascicles to be accurately estimated, despite signal differences.

RESULTS

Small normalized signal differences were observed, typically 10-3-10-2. The use of the approximation had little impact on the estimation of the crossing angle, the axonal density index, and the radius index in clinically realistic scenarios wherein the acquisition noise was the predominant source of errors. In the absence of noise, large systematic errors due to the superposition approximation only persisted for the radius index, mainly driven by a low sensitivity of diffusion-weighted MRI signals to small radii in general.

CONCLUSION

The use of the superposition approximation rather than a model of interwoven fascicles does not adversely impact the estimation of microstructural features of interwoven fascicles in most current clinical settings. Magn Reson Med 79:2332-2345, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

摘要

目的

评估交叉束的叠加近似的有效性,即假设总扩散加权 MRI 信号是每个束独立产生的信号的总和,即使束在体素中混合。

方法

使用蒙特卡罗模拟研究了该近似对扩散加权 MRI 信号的影响,并评估了尽管存在信号差异,该近似模型是否允许准确估计交织束的微观结构特征。

结果

观察到小的归一化信号差异,通常为 10-3-10-2。在临床现实情况下,当采集噪声是主要误差源时,该近似对交叉角、轴突密度指数和半径指数的估计几乎没有影响。在没有噪声的情况下,由于叠加近似,仅半径指数会持续出现大的系统误差,主要是由于扩散加权 MRI 信号对一般小半径的低灵敏度所致。

结论

在大多数当前临床环境中,使用叠加近似而不是交织束模型不会对交织束的微观结构特征的估计产生不利影响。磁共振医学 79:2332-2345, 2018。© 2017 国际磁共振学会。

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Robust and fast nonlinear optimization of diffusion MRI microstructure models.
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2
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3
Degeneracy in model parameter estimation for multi-compartmental diffusion in neuronal tissue.
NMR Biomed. 2016 Jan;29(1):33-47. doi: 10.1002/nbm.3450. Epub 2015 Nov 29.
4
Improved fidelity of brain microstructure mapping from single-shell diffusion MRI.
Med Image Anal. 2015 Dec;26(1):268-86. doi: 10.1016/j.media.2015.10.004. Epub 2015 Oct 22.
5
Sparse and Adaptive Diffusion Dictionary (SADD) for recovering intra-voxel white matter structure.
Med Image Anal. 2015 Dec;26(1):243-55. doi: 10.1016/j.media.2015.10.002. Epub 2015 Oct 22.
6
Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization.
PLoS One. 2015 Oct 15;10(10):e0138910. doi: 10.1371/journal.pone.0138910. eCollection 2015.
8
Microstructural parameter estimation in vivo using diffusion MRI and structured prior information.
Magn Reson Med. 2016 Apr;75(4):1787-96. doi: 10.1002/mrm.25723. Epub 2015 May 20.
9
Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI.
Neuroimage. 2015 Jul 15;115:245-55. doi: 10.1016/j.neuroimage.2015.04.049. Epub 2015 May 2.
10
Mesoscopic structure of neuronal tracts from time-dependent diffusion.
Neuroimage. 2015 Jul 1;114:18-37. doi: 10.1016/j.neuroimage.2015.03.061. Epub 2015 Mar 30.

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