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Updated: Feb 11, 2026
Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury
Published on: August 14, 2019
Utility of Readout-Segmented Echo-Planar Imaging-Based Diffusion Kurtosis Imaging for Differentiating Malignant from
Gao Ma1, Xiao-Quan Xu1, Hao Hu1
1Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Objective:
To compare the diagnostic performance of readout-segmented echo-planar imaging (RS-EPI)-based diffusion kurtosis imaging (DKI) and that of diffusion-weighted imaging (DWI) for differentiating malignant from benign masses in head and neck region.
Materials And Methods:
Between December 2014 and April 2016, we retrospectively enrolled 72 consecutive patients with head and neck masses who had undergone RS-EPI-based DKI scan (b value of 0, 500, 1000, and 1500 s/mm2) for pretreatment evaluation. Imaging data were post-processed by using monoexponential and diffusion kurtosis (DK) model for quantitation of apparent diffusion coefficient (ADC), apparent diffusion for Gaussian distribution (Dapp), and apparent kurtosis coefficient (Kapp). Unpaired t test and Mann-Whitney U test were used to compare differences of quantitative parameters between malignant and benign groups. Receiver operating characteristic curve analyses were performed to determine and compare the diagnostic ability of quantitative parameters in predicting malignancy.
Results:
Malignant group demonstrated significantly lower ADC (0.754 ± 0.167 vs. 1.222 ± 0.420, p < 0.001) and Dapp (1.029 ± 0.226 vs. 1.640 ± 0.445, p < 0.001) while higher Kapp (1.344 ± 0.309 vs. 0.715 ± 0.249, p < 0.001) than benign group. Using a combination of Dapp and Kapp as diagnostic index, significantly better differentiating performance was achieved than using ADC alone (area under curve: 0.956 vs. 0.876, p = 0.042).
Conclusion:
Compared to DWI, DKI could provide additional data related to tumor heterogeneity with significantly better differentiating performance. Its derived quantitative metrics could serve as a promising imaging biomarker for differentiating malignant from benign masses in head and neck region.
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