Evaluation of water diffusion microenvironment in healthy brain white matter and gliomas using non-Gaussian diffusion MR imaging: a reproducibility study
|更新时间:2026-01-09
|
Evaluation of water diffusion microenvironment in healthy brain white matter and gliomas using non-Gaussian diffusion MR imaging: a reproducibility study
Journal of Army Medical UniversityVol. 38, Issue 22, Pages: 2399-2346(2016)
Ren Yan,Feng Xiaoyuan,Pang Haopeng,Qiu Tianming,Zhang Jiawen,Liu Hanqiu,Yao Chengjun,Chen Hong,Zhang Yong,Qu Jianxun,Zhuang Dongxiao,Wu Jinsong,Yao Zhenwei.Evaluation of water diffusion microenvironment in healthy brain white matter and gliomas using non-Gaussian diffusion MR imaging: a reproducibility study[J].J Amry Med Univ (J Third Mil Med Univ),2016,38(22):2399-2346.
Ren Yan,Feng Xiaoyuan,Pang Haopeng,Qiu Tianming,Zhang Jiawen,Liu Hanqiu,Yao Chengjun,Chen Hong,Zhang Yong,Qu Jianxun,Zhuang Dongxiao,Wu Jinsong,Yao Zhenwei.Evaluation of water diffusion microenvironment in healthy brain white matter and gliomas using non-Gaussian diffusion MR imaging: a reproducibility study[J].J Amry Med Univ (J Third Mil Med Univ),2016,38(22):2399-2346.DOI:
Evaluation of water diffusion microenvironment in healthy brain white matter and gliomas using non-Gaussian diffusion MR imaging: a reproducibility study
Objective To explore the reproducibility of non-Gaussian diffusion MR imaging using bi- and stretched-exponential models with 22 b-value (0~5 000 s/mm2) in evaluation of water diffusion microenvironment in the healthy brain white matter and gliomas. Methods Twelve healthy volunteers and 65 patients with pathologically confirmed glioma were recruited for this study. For the healthy volunteers
2 consecutive enhanced diffusion weighted imaging (eDWI) scans were performed and post-processed using bi- and stretched-exponetial models,respectively. The white matter located at the basal ganglion and semioval centrum was selected for the placement of region of interest (ROI)
with each section containing 4 different ROIs. For the glioma group
eDWI scan was done once. The representative section showing the largest solid tumor was identified and the tumor ROIs were delineated manually twice by two neuroradiologists separately. Mean values of slow- and fast-diffusion coefficients (Dslow and Dfast)
perfusion fraction (PF)
distributed diffusion coefficient (DDC) and intravoxel heterogeneity index α were calculated. The repeatability of these parameters obtained from 2 scans in healthy controls was analyzed by paired t test
coefficient of variability (CV) and Bland-Altman (B-A) plots. Analyses of intraclass correlative coefficient (ICC) and CV were used for testing the agreement of 2 scans of measurement in the same patient with glioma. Results For the 12 healthy volunteers
no significant differences in all diffusion parameters were shown between the 2 scans (P0.05). The CV values of all parameters were below 5%. B-A plots illustrated a majority (≥11/12) of data points within 95% normalized difference for all parameters of eight ROIs in the 2 sections. For 65 cases of gliomas
satifactory agreements were acquired for all the parameters with the ICC value of more than 0.75 (P0.001). Dfast and PF had higher CV values within a range of 5.0%~11.5%
followed by DDC and Dslow. Their CV values were positively associated with the increasing gliomas grade. The α index had the smallest CV value of less than 2%. Conclusion Our study shows a good reproducibility of non-Gaussian derived diffusion parameters for the evaluation of water diffusion microenvironment in the healthy brain white matter
whereas the- index α has the best reproducibility among the non=Gaussian derived diffusion parameters in the application of gliomas.
Value of MRI radiomics based on intratumoral and peritumoral heterogeneity in predicting spatial patterns of locally recurrent high-grade gliomas
Hypoxic transcriptional phenotype and cellular ultrastructural changes of tumor-associated macrophages in gliomas
Inhibiting and invasive self-renewal effect of PTPRZ 1 monoclonal antibody on glioma stem cells
Prostate cancer T-stage intelligent diagnosis based on MRI images and deep learning
Silent information regulator 3 modulates lysosome function to inhibit apoptosis of U87 cell by mTOR pathway
Related Author
WANG Hanwei
ZENG Linlan
ZHAO Mimi
LI Xuan
XIE Huan
LI Xiaoguang
YI Liang
WU Yi
Related Institution
Department of Radiology, Chongqing Clinical Research Centre of Imaging and Nuclear Medicine, Faculty of Biomedical Engineering and Imaging Medicine, Army Medical University (Third Military Medical University)
Department of Neurosurgery, Army Medical Center of PLA/Daping Hospital of Third Military Medical University, Chongqing, Faculty of Biomedical Engineering and Imaging Medicine, Army Medical University (Third Military Medical University)
Department of Digital Medicine, Faculty of Biomedical Engineering and Imaging Medicine, Army Medical University (Third Military Medical University)