Grading of Gliomas by Using Monoexponential, Biexponential, and Stretched Exponential Diffusion-weighted MR Imaging and Diffusion Kurtosis MR Imaging

Abstract

).q RSNA, 2015 Purpose:To quantitatively compare the potential of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging models and diffusion kurtosis imaging in the grading of gliomas. Materials andMethods:This study was approved by the local ethics committee, and written informed consent was obtained from all subjects. Both diffusion-weighted imaging and diffusion kurtosis imaging were performed in 69 patients with pathologically proven gliomas by using a 3-T magnetic resonance (MR) imaging unit. An isotropic apparent diffusion coefficient (ADC), true ADC, pseudo-ADC, and perfusion fraction were calculated from diffusionweighted images by using a biexponential model. A water molecular diffusion heterogeneity index and distributed diffusion coefficient were calculated from diffusion-weighted images by using a stretched exponential model. Mean diffusivity, fractional anisotropy, and mean kurtosis were calculated from diffusion kurtosis images. All values were compared between high-grade and low-grade gliomas by using a Mann-Whitney U test. Receiver operating characteristic and Spearman rank correlation analysis were used for statistical evaluations. Results:ADC, true ADC, perfusion fraction, water molecular diffusion heterogeneity index, distributed diffusion coefficient, and mean diffusivity values were significantly lower in high-grade gliomas than in low-grade gliomas (U = 109, 56, 129, 6, 206, and 229, respectively; P , .05). Pseudo-ADC and mean kurtosis values were significantly higher in high-grade gliomas than in low-grade gliomas (U = 98 and 8, respectively; P , .05). Both water molecular diffusion heterogeneity index (area under the receiver operating characteristic curve [AUC] = 0.993) and mean kurtosis (AUC = 0.991) had significantly greater AUC values than ADC (AUC = 0.866), mean diffusivity (AUC = 0.722), and fractional anisotropy (AUC = 0.500) in the differentiation of low-grade and high-grade gliomas (P , .05). Conclusion:Water molecular diffusion heterogeneity index and mean kurtosis values may provide additional information and improve the grading of gliomas compared with conventional diffusion parameters.q RSNA, 2015

Advanced diffusion models in head and neck squamous cell carcinoma patients: Goodness of fit, relationships among diffusion parameters and comparison with dynamic contrast-enhanced perfusion

“…Advanced fitting models for DWI were recently described [7][8][9][10][11][12][13]. For example, a model using intravoxel incoherent motion (IVIM) was proposed; by using the bi-exponential decay function, it divides the fast and slow diffusion components, which reflects the true tissue diffusivity and capillary perfusion, respectively [7,8].…”

“…A stretched exponential model (SEM) diffusion model was described as follows: the SEM model was used to describe the heterogeneity of water diffusion in each voxel. In this model, to obtain a good fit to multiple b-value signal decay data, the two parameters of diffusion heterogeneity and diffusion coefficient are respectively calculated [11,12]. A diffusion kurtosis imaging (DKI) model also uses the diffusion distribution information.…”

“…Image acquisition was performed using a three-dimensional (3D)-T1 fast field echo (T1-FFE) sequence. The MR parameters for the dynamic data acquisition were as follows: TR, 6.1 ms; TE, 1. perform these parameter calculations, we used the following equations [7,[9][10][11][12][13]:…”

Advanced MRI for Pediatric Brain Tumors with Emphasis on Clinical Benefits

“…However, the ADC values do not accurately indicate pure water diffusion in the tissue and tend to be overestimated because they are contaminated by perfusion most apparent in low b-values (e.g., < 100–150 s/mm 2 ). Recent studies reported that ADC ratios, histogram analysis, and diffusion kurtosis based on non-Gaussian water diffusion might better characterize high-grade brain tumors (3141516). Furthermore, a diffusion heterogeneity index calculated from a stretched exponential model or a fractional order calculus model with multi-b-value DWI recently demonstrates improved tumor grading in adult gliomas (16) and pediatric brain tumors (17).…”

Recent Developments in Fast Kurtosis Imaging

“…Several of these DKI metrics have been shown to detect subtle changes in brain tissue structure. To briefly summarize, studies have shown DKI to have potential in diagnostics of a number of diseases such as stroke [4,5], Alzheimer's disease [6], multiple sclerosis [7], gliomas [8,9], and head trauma [10][11][12] (see also the review of this area in Ostergaard et al [13]). DKI is not only of clinical interest but is also a valuable tool in basic neuroscience, and the method has for example been employed in studies of natural alteration of brain microstructure e.g., in the context of development and aging [14,15].…”