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体素内不相干运动磁共振成像的最优模型映射

Optimal Model Mapping for Intravoxel Incoherent Motion MRI.

作者信息

Liao Yen-Peng, Urayama Shin-Ichi, Isa Tadashi, Fukuyama Hidenao

机构信息

Division of Neurobiology and Physiology, Department of Neuroscience, Graduate School of Medicine in Kyoto University, Kyoto, Japan.

Human Brain Research Center, Graduate School of Medicine in Kyoto University, Kyoto, Japan.

出版信息

Front Hum Neurosci. 2021 Feb 22;15:617152. doi: 10.3389/fnhum.2021.617152. eCollection 2021.

Abstract

In general, only one diffusion model would be applied to whole field-of-view voxels in the intravoxel incoherent motion-magnetic resonance imaging (IVIM-MRI) study. However, the choice of the applied diffusion model can significantly influence the estimated diffusion parameters. The quality of the diffusion analysis can influence the reliability of the perfusion analysis. This study proposed an optimal model mapping method to improve the reliability of the perfusion parameter estimation in the IVIM study. Six healthy volunteers (five males and one female; average age of 38.3 ± 7.5 years). Volunteers were examined using a 3.0 Tesla scanner. IVIM-MRI of the brain was applied at 17 b-values ranging from 0 to 2,500 s/mm. The Gaussian model, the Kurtosis model, and the Gamma model were found to be optimal for the CSF, white matter (WM), and gray matter (GM), respectively. In the mean perfusion fraction (f) analysis, the GM/WM ratios were 1.16 (Gaussian model), 1.80 (Kurtosis model), 1.94 (Gamma model), and 1.54 (Optimal model mapping); in the mean pseudo diffusion coefficient (D) analysis, the GM/WM ratios were 1.18 (Gaussian model), 1.19 (Kurtosis model), 1.56 (Gamma model), and 1.24 (Optimal model mapping). With the optimal model mapping method, the estimated f and D were reliable compared with the conventional methods. In addition, the optimal model maps, the associated products of this method, may provide additional information for clinical diagnosis.

摘要

一般来说,在体素内不相干运动磁共振成像(IVIM-MRI)研究中,整个视野体素通常仅应用一种扩散模型。然而,所应用扩散模型的选择会显著影响估计的扩散参数。扩散分析的质量会影响灌注分析的可靠性。本研究提出了一种最优模型映射方法,以提高IVIM研究中灌注参数估计的可靠性。六名健康志愿者(五名男性和一名女性;平均年龄38.3±7.5岁)。志愿者使用3.0特斯拉扫描仪进行检查。在17个从0到2500 s/mm²的b值下对大脑进行IVIM-MRI检查。发现高斯模型、峰度模型和伽马模型分别对脑脊液、白质(WM)和灰质(GM)是最优的。在平均灌注分数(f)分析中,GM/WM比值分别为1.16(高斯模型)、1.80(峰度模型)、1.94(伽马模型)和1.54(最优模型映射);在平均伪扩散系数(D)分析中,GM/WM比值分别为1.18(高斯模型)、1.19(峰度模型)、1.56(伽马模型)和1.24(最优模型映射)。采用最优模型映射方法,与传统方法相比,估计的f和D更可靠。此外,最优模型图作为该方法的相关产物,可能为临床诊断提供额外信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4082/7937866/b70bc86e1e3c/fnhum-15-617152-g0001.jpg

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