Non-gaussian analysis of diffusion-weighted MR imaging in head and neck squamous cell carcinoma: A feasibility study

Abstract

Background and purpose: Water in biological structures often displays non-Gaussian diffusion behavior. The objective of this study was to test the feasibility of non-Gaussian fitting by using the kurtosis model of the signal intensity decay curves obtained from DWI by using an extended range of b-values in studies of phantoms and HNSCC.

Materials and methods: Seventeen patients with HNSCC underwent DWI by using 6 b-factors (0, 50-1500 s/mm(2)) at 1.5T. Monoexponential (yielding ADC(mono)) and non-Gaussian kurtosis (yielding apparent diffusion coefficient D(app) and apparent kurtosis coefficient K(app)) fits were performed on a voxel-by-voxel basis in selected regions of interest (primary tumors, metastatic lymph nodes, and spinal cord). DWI studies were also performed on phantoms containing either water or homogenized asparagus. To determine whether the kurtosis model provided a significantly better fit than did the monoexponential model, an F test was performed. Spearman correlation coefficients were calculated to assess correlations between K(app) and D(app).

Results: The kurtosis model fit the experimental data points significantly better than did the monoexponential model (P < .05). D(app) was approximately twice the value of ADC(mono) (eg, in neck nodal metastases D(app) was 1.54 and ADC(mono) was 0.84). K(app) showed a weak Spearman correlation with D(app) in a homogenized asparagus phantom and for 44% of tumor lesions.

Conclusions: The use of kurtosis modeling to fit DWI data acquired by using an extended b-value range in HNSCC is feasible and yields a significantly better fit of the data than does monoexponential modeling. It also provides an additional parameter, K(app), potentially with added value.

Fig 1.

Logarithm of the MR signal intensity decay averaged over all voxels within the right node of patient 12 as a function of the b-value. The black crosshairs represent the experimental data, and the solid lines are the fitted curves (red is the monoexponential fit, blue is the non-Gaussian kurtosis fit).

Fig 2.

Axial MR images from the oral cavity of a male patient diagnosed with base of tongue cancer (patient 12). A, T2 short tau inversion recovery image. B, Realigned mean b = 0 image. The red and white arrows in A indicate the primary tumor and metastatic nodes, respectively. C–F display voxel-by-voxel calculation outcomes for the right node presented as mask overlays on the realigned mean b = 0 image. G–I display the corresponding histogram distribution plots of the measures from C–E. C and G, Apparent diffusion coefficient obtained from monoexponential fitting (10⁻³ mm²/s). D and H, Apparent diffusion coefficient (10⁻³ mm²/s), (E and I) apparent kurtosis coefficient (dimensionless), and (F) reduced χ² error estimate, all obtained from the diffusional kurtosis analysis.

Fig 3.

T2 short tau inversion recovery images for (A) the right neck metastatic lymph node of patient 12 and (B) primary base of tongue tumor of patient 3 (ROIs are indicated with a white arrow). C and D display the corresponding scatterplots showing the correlation between the D_app (average apparent diffusion coefficient) and the K_app (apparent diffusional kurtosis) obtained from the diffusional kurtosis analysis. The Spearman rank-order correlation coefficient is −0.34 for C, indicating a weak correlation, and −0.90 for D, indicating a strong negative correlation.