不同磁共振扩散加权成像模型在大鼠脑内的重复性

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Abstract Objective: To evaluate the repeatability of different magnetic resonance diffusion weighted imaging (DWI) mathematical modelsin the brains of rats, such as the apparent diffusion coefficient (ADC) model, intravoxel incoherent motion (IVIM) model, diffusion kurtosis imaging (DKI) model and stretched exponential model. Methods: There were 14 male Sprague-Dawley rats experienced DWI scanning in 3.0T scanner twice, in 24-hours intervals. The data was analyzed with different diffusion mathematical models. The intra-subject coefficient of variation (intra-subject CV) and Bland-Altman analysis were used to evaluate the repeatability of the experiment. Results: In the ADC model, DKI model and stretched exponential model, the Cronbach’s alpha of these diffusion related parameters (ADC, Dapp and DDC) were less than 5%, intra-subject CV were less than 0.7, and LA were less than 0.15×10-3 mm2/s. Moreover, ADC had the smallest of LA, which was 0.086×10-3 mm2/s. Cronbach’s alpha of all the coefficients of the IVIM model were less than 0.5, intra-subject CV were more than 40%, and LA were greater than 0.4×10-3 mm2/s. Among different regions of Interest, minimum intra-subject CV and LA of ADC were in striatum, respectively: 2.00% and 0.034×10-3 mm2/s. And Corpus callosum had the highest intra-subject CV and LA of ADC, respectively 3.98% and 0.085×10-3 mm2/s. Conclusion: The repetitive analysis results of different diffusion mathematical models, which from the DWI of rat’s brain in a intervals of 24 hours, are significantly different. The repeatability of ADC model is the best, and IVIM model is the worst. In addition, the repeatability of ADC value in different brain ROIs of rats are also significantly different, which shows that striatum has a best repeatability, and corpus callosum is relatively poor.

Key words： magnetic resonance imaging diffusion weighted imaging multiple model analysis repeatability study

Received: 11 September 2018

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	LU Yi
	CHEN Tao
	PENG Wenwen
	SONG Jiao
	WANG Xue
	YAN Zhihan

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https://xb.wmu.edu.cn/EN/ OR https://xb.wmu.edu.cn/EN/Y2019/V49/I7/491

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