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Anomalous diffusion in cerebral glioma assessed using a fractional motion model
Author(s) -
Xu Boyan,
Su Lu,
Wang Zhenxiong,
Fan Yang,
Gong Gaolang,
Zhu Wenzhen,
Gao Peiyi,
Gao JiaHong
Publication year - 2017
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.26581
Subject(s) - glioma , grading (engineering) , magnetic resonance imaging , hurst exponent , diffusion mri , effective diffusion coefficient , medicine , nuclear magnetic resonance , nuclear medicine , receiver operating characteristic , radiology , mathematics , physics , statistics , civil engineering , cancer research , engineering
Purpose To demonstrate the capability of the fractional motion (FM) model for describing anomalous diffusion in cerebral gliomas and to assess the potential feasibility of FM for grading these tumors. Methods Diffusion MRI images were acquired from brain tumor patients using a special Stejskal‐Tanner diffusion sequence with variable diffusion gradient amplitudes and separation times. Patients with histopathologically confirmed gliomas, including astrocytic and oligoastrocytic tumors, were selected. The FM‐related parameters, including the Noah exponent ( α ), the Hurst exponent ( H ), and the memory parameter ( μ = H − 1 / α ), were calculated and compared between low‐ and high‐grade gliomas using a two‐sample t‐test. The grading performance was evaluated using the receiver operating characteristic analysis. Results Twenty‐two patients were included in the present study. The calculated α , H , and μ permitted the separation of tumor lesions from surrounding normal tissues in parameter maps and helped differentiate glioma grades. Moreover, α showed greater sensitivity and specificity in distinguishing low‐ and high‐grade gliomas compared with the apparent diffusion coefficient. Conclusion The FM model could improve the diagnostic accuracy in differentiating low‐ and high‐grade gliomas. This improved diffusion model may facilitate future studies of neuro‐pathological changes in clinical populations. Magn Reson Med 78:1944–1949, 2017. © 2017 International Society for Magnetic Resonance in Medicine.