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2013, NeuroImage. DOI: 10.1016/j.neuroimage.2012.10.022 PMID: 23085109 PMCID: PMC3580290
A pitfall in the reconstruction of fibre ODFs using spherical deconvolution of diffusion MRI data
G.D. Parker, D. Marshall, P.L. Rosin, N. Drage, S. Richmond, D.K. Jones
Abstract:
Diffusion weighted (DW) MRI facilitates non-invasive quantification of tissue microstructure and, in combination with appropriate signal processing, three-dimensional estimates of fibrous orientation. In recent years, attention has shifted from the diffusion tensor model, which assumes a unimodal Gaussian diffusion displacement profile to recover fibre orientation (with various well-documented limitations), towards more complex high angular resolution diffusion imaging (HARDI) analysis techniques.

Spherical deconvolution (SD) approaches assume that the fibre orientation density function (fODF) within a voxel can be obtained by deconvolving a ‘common’ single fibre response function from the observed set of DW signals. In practice, this common response function is not known a priori and thus an estimated fibre response must be used. Here the establishment of this single-fibre response function is referred to as ‘calibration’. This work examines the vulnerability of two different SD approaches to inappropriate response function calibration: (1) constrained spherical harmonic deconvolution (CSHD)—a technique that exploits spherical harmonic basis sets and (2) damped Richardson–Lucy (dRL) deconvolution—a technique based on the standard Richardson–Lucy deconvolution.

Through simulations, the impact of a discrepancy between the calibrated diffusion profiles and the observed (‘Target’) DW-signals in both single and crossing-fibre configurations was investigated. The results show that CSHD produces spurious fODF peaks (consistent with well known ringing artefacts) as the discrepancy between calibration and target response increases, while dRL demonstrates a lower over-all sensitivity to miscalibration (with a calibration response function for a highly anisotropic fibre being optimal). However, dRL demonstrates a reduced ability to resolve low anisotropy crossing-fibres compared to CSHD. It is concluded that the range and spatial-distribution of expected single-fibre anisotropies within an image must be carefully considered to ensure selection of the appropriate algorithm, parameters and calibration. Failure to choose the calibration response function carefully may severely impact the quality of any resultant tractography.
2022-05-31 22:41:00
#paper: doi.org/10.1016/j.neuroimage.2012.10.022 扩散加权 (DW) MRI 有助于对组织微观结构进行无创量化,并结合适当的信号处理,对纤维方向进行三维估计。近年来,人们的注意力已经从扩散张量模型转移到更复杂的高角分辨率扩散成像 (HARDI) 分析技术,该模型假设单峰高斯扩散位移分布来恢复纤维取向(具有各种有据可查的限制)。 球面反卷积 (SD) 方法假设体素内的纤维取向密度函数 (fODF) 可以通过从观察到的 DW 信号集中对“普通”单纤维响应函数进行反卷积来获得。在实践中,这种常见的响应函数是先验未知的,因此必须使用估计的纤维响应。在这里,这种单纤维响应函数的建立被称为“校准”。这项工作检查了两种不同的 SD 方法对不适当的响应函数校准的脆弱性:(1) 约束球谐反卷积 (CSHD) - 一种利用球谐基组的技术和 (2) 阻尼 Richardson-Lucy (dRL) 反卷积 - 一种技术基于标准的 Richardson-Lucy 反卷积。 通过模拟,研究了在单光纤和交叉光纤配置中校准的扩散剖面与观察到的(“目标”)DW 信号之间的差异的影响。结果表明,随着校准和目标响应之间的差异增加,CSHD 会产生虚假 fODF 峰(与众所周知的振铃伪影一致),而 dRL 对误校准表现出较低的整体敏感性(对于高度各向异性光纤的校准响应函数为最佳)。然而,与 CSHD 相比,dRL 显示出解决低各向异性交叉纤维的能力降低。得出的结论是,必须仔细考虑图像中预期单纤维各向异性的范围和空间分布,以确保选择适当的算法、参数和校准。未能仔细选择校准响应函数可能会严重影响任何最终纤维束成像的质量。
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