Daianu Madelaine, Jahanshad Neda, Villalon-Reina Julio E, Prasad Gautam, Jacobs Russell E, Barnes Samuel, Zlokovic Berislav V, Montagne Axel, Thompson Paul M
Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles.
Biological Imaging Center, California Institute of Technology, Los Angeles.
Proc SPIE Int Soc Opt Eng. 2015 Mar 20;9413. doi: 10.1117/12.2081491.
Diffusion weighted imaging (DWI) is widely used to study microstructural characteristics of the brain. High angular resolution diffusion imaging (HARDI) samples diffusivity at a large number of spherical angles, to better resolve neural fibers that mix or cross. Here, we implemented a framework for advanced mathematical analysis of mouse 5-shell HARDI (=1000, 3000, 4000, 8000, 12000 s/mm), also known as hybrid diffusion imaging (HYDI). Using -ball imaging (QBI) at ultra-high field strength (7 Tesla), we computed diffusion and fiber orientation distribution functions (dODF, fODF) to better detect crossing fibers. We also computed a quantitative anisotropy (QA) index, and deterministic tractography, from the peak orientation of the fODFs. We found that the signal to noise ratio (SNR) of the QA was significantly higher in single and multi-shell reconstructed data at the lower -values (=1000, 3000, 4000 s/mm) than at higher -values (=8000, 12000 s/mm); the =1000 s/mm shell increased the SNR of the QA in all multi-shell reconstructions, but when used alone or in <5-shell reconstruction, it led to higher angular error for the major fibers, compared to 5-shell HYDI. Multi-shell data reconstructed major fibers with less error than single-shell data, and was most successful at reducing the angular error when the lowest shell was excluded (=1000 s/mm). Overall, high-resolution connectivity mapping with 7T HYDI offers great potential for understanding unresolved changes in mouse models of brain disease.
扩散加权成像(DWI)被广泛用于研究大脑的微观结构特征。高角分辨率扩散成像(HARDI)在大量球角上对扩散率进行采样,以更好地分辨混合或交叉的神经纤维。在此,我们实现了一个用于对小鼠5壳层HARDI(=1000、3000、4000、8000、12000 s/mm²)进行高级数学分析的框架,也称为混合扩散成像(HYDI)。使用超高场强(7特斯拉)下的 -球成像(QBI),我们计算了扩散和纤维取向分布函数(dODF,fODF),以更好地检测交叉纤维。我们还从fODF的峰值方向计算了定量各向异性(QA)指数和确定性纤维束成像。我们发现,在较低 -值(=1000、3000、4000 s/mm²)的单壳层和多壳层重建数据中,QA的信噪比(SNR)显著高于较高 -值(=8000、1200 s/mm²);1000 s/mm²的壳层在所有多壳层重建中提高了QA的SNR,但单独使用或在<5壳层重建中使用时,与5壳层HYDI相比,它会导致主要纤维的角度误差更高。多壳层数据重建主要纤维时的误差比单壳层数据小,并且在排除最低壳层(=1000 s/mm²)时,在减少角度误差方面最为成功。总体而言,使用7T HYDI进行高分辨率连接图谱绘制为理解脑疾病小鼠模型中未解决的变化提供了巨大潜力。