Abstract：Diffusion weighted imaging has been widely used in quantitative study of human brain white matter. Apparent fiber density (AFD) is a quantitative metric based on the fiber orientation distribution (FOD), which can reflect the fiber density of white matter and has been applied in the research of healthy development and neurodegenerative diseases. However, the value of AFD measurement strongly depends on experimental set-up, and the test-retest repeatability of AFD under different experimental conditions has not been systematically assessed. In this paper, the test-retest repeatability of AFD was examined at different diffusion weighted values (b-values) using the scan-rescan data of 42 subjects from two separate datasets. One dataset (n=35) is from Human Connectome Project (HCP), the other (n=7) is form Connectome Diffusion Microstructure Dataset (CDMD). The AFD value of fixel level was estimated using fixel-based analysis (FBA), and tract ROIs were obtained using TractSeg to estimate the fiber bundle averaged AFD. Results showed that at the fixel level, as the b-value increased, the Pearson’s correlation coefficient (r) and intraclass correlation coefficient (ICC) increased from 0.796 9 and 0.898 5 to 0.882 8 and 0.941 4 respectively in the test-retest experiment with HCP, and the absolute deviation decreased from 0.124 1 to 0.107 3. Similar trend was also found in the dataset from CDMD. At the tract-average level, the test-retest repeatability can be achieved at all b-values in both datasets. In summary, this paper examined the repeatability of AFD estimation for b-values in the range of 1 000~5 000 s·mm-2, and our results might provide useful insights into the experimental design in future neurobiological investigations using AFD.
骆义超, 严经国, 陈元园, 范秋筠. 基于扩散加权成像的表观纤维密度重测信度研究[J]. 中国生物医学工程学报, 2023, 42(4): 420-430.
Luo Yichao, Yan Jingguo, Chen Yuanyuan, Fan Qiuyun. The Test-Retest Assessment of Apparent Fiber Density Measurements Using Diffusion Weighted Imaging in English. Chinese Journal of Biomedical Engineering, 2023, 42(4): 420-430.
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