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本文引用的文献

1
ODF RECONSTRUCTION IN Q-BALL IMAGING WITH SOLID ANGLE CONSIDERATION.考虑立体角的Q球成像中的ODF重建
Proc IEEE Int Symp Biomed Imaging. 2009 Jun-Jul;2009:1398-1401. doi: 10.1109/ISBI.2009.5193327.
2
Multiple Q-shell ODF reconstruction in Q-ball imaging.Q球成像中的多Q壳ODF重建
Med Image Comput Comput Assist Interv. 2009;12(Pt 2):423-31. doi: 10.1007/978-3-642-04271-3_52.
3
Estimating orientation distribution functions with probability density constraints and spatial regularity.基于概率密度约束和空间正则性估计方向分布函数
Med Image Comput Comput Assist Interv. 2009;12(Pt 1):877-85. doi: 10.1007/978-3-642-04268-3_108.
4
On approximation of orientation distributions by means of spherical ridgelets.基于球脊波的方向分布逼近。
IEEE Trans Image Process. 2010 Feb;19(2):461-77. doi: 10.1109/TIP.2009.2035886. Epub 2009 Nov 3.
5
Diffusion propagator imaging: using Laplace's equation and multiple shell acquisitions to reconstruct the diffusion propagator.扩散传播子成像:利用拉普拉斯方程和多壳采集来重建扩散传播子。
Inf Process Med Imaging. 2009;21:1-13. doi: 10.1007/978-3-642-02498-6_1.
6
Efficient and robust computation of PDF features from diffusion MR signal.从扩散磁共振信号高效且稳健地计算概率密度函数特征。
Med Image Anal. 2009 Oct;13(5):715-29. doi: 10.1016/j.media.2009.06.004. Epub 2009 Jul 12.
7
Estimation of fiber orientation probability density functions in high angular resolution diffusion imaging.高角分辨率扩散成像中纤维取向概率密度函数的估计
Neuroimage. 2009 Aug 15;47(2):638-50. doi: 10.1016/j.neuroimage.2009.04.049. Epub 2009 Apr 22.
8
Mathematical description of q-space in spherical coordinates: exact q-ball imaging.球坐标下q空间的数学描述:精确q球成像
Magn Reson Med. 2009 Jun;61(6):1350-67. doi: 10.1002/mrm.21917.
9
Directional functions for orientation distribution estimation.用于取向分布估计的定向函数。
Med Image Anal. 2009 Jun;13(3):432-44. doi: 10.1016/j.media.2009.01.004. Epub 2009 Feb 5.
10
Mathematical methods for diffusion MRI processing.扩散磁共振成像处理的数学方法。
Neuroimage. 2009 Mar;45(1 Suppl):S111-22. doi: 10.1016/j.neuroimage.2008.10.054. Epub 2008 Nov 13.

在恒定立体角内进行单壳和多壳 q-球成像中的方位分布函数重建。

Reconstruction of the orientation distribution function in single- and multiple-shell q-ball imaging within constant solid angle.

机构信息

Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA.

出版信息

Magn Reson Med. 2010 Aug;64(2):554-66. doi: 10.1002/mrm.22365.

DOI:10.1002/mrm.22365
PMID:20535807
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2911516/
Abstract

q-Ball imaging is a high-angular-resolution diffusion imaging technique that has been proven very successful in resolving multiple intravoxel fiber orientations in MR images. The standard computation of the orientation distribution function (the probability of diffusion in a given direction) from q-ball data uses linear radial projection, neglecting the change in the volume element along each direction. This results in spherical distributions that are different from the true orientation distribution functions. For instance, they are neither normalized nor as sharp as expected and generally require postprocessing, such as artificial sharpening. In this paper, a new technique is proposed that, by considering the solid angle factor, uses the mathematically correct definition of the orientation distribution function and results in a dimensionless and normalized orientation distribution function expression. Our model is flexible enough so that orientation distribution functions can be estimated either from single q-shell datasets or by exploiting the greater information available from multiple q-shell acquisitions. We show that the latter can be achieved by using a more accurate multiexponential model for the diffusion signal. The improved performance of the proposed method is demonstrated on artificial examples and high-angular-resolution diffusion imaging data acquired on a 7-T magnet.

摘要

q- 球成像是一种高角度分辨率的扩散成像技术,已被证明在磁共振成像中解析多个体素内纤维方向非常成功。从 q- 球数据计算方向分布函数(给定方向扩散的概率)的标准方法使用线性径向投影,忽略了每个方向上体积元的变化。这导致与真实方向分布函数不同的球形分布。例如,它们既没有归一化,也不像预期的那样尖锐,通常需要后处理,例如人为锐化。在本文中,提出了一种新技术,该技术通过考虑立体角因子,使用方向分布函数的数学正确定义,并得到无量纲和归一化的方向分布函数表达式。我们的模型足够灵活,因此可以从单个 q-壳数据集或利用来自多个 q-壳采集的更多信息来估计方向分布函数。我们表明,通过使用更准确的扩散信号多指数模型可以实现后者。所提出的方法的改进性能在人工示例和在 7-T 磁铁上采集的高角度分辨率扩散成像数据上得到了证明。