Synopsis

Keywords: Diffusion/other diffusion imaging techniques, Cancer

Cellular-interstitial water exchange time has been suggested to be associated with a number of important cellular properties such as membrane permeability, tumor aggressiveness and treatment response. In this study we investigated the reliability of measuring water exchange times based on diffusivity and diffusional kurtosis at long diffusion times. We used two well-established diffusion phantoms and found that diffusion and kurtosis show stable values over a wide range of diffusion times. In head and neck cancer patients, we found that the Kärger model is a valid model for measuring water exchange time in metastatic lymph node voxels.

KEYWORDS

Kärger model; Diffusion Phantom; Kurtosis; STEAM-EPI

INTRODUCTION

Diffusion MRI (dMRI) has become the modality of choice to assess the cellular properties of tumors, as the diffusion of water molecules is highly sensitive to tissue microstructure¹. However, the diffusivity derived from dMRI acquisition is not a constant for a given biological tissue, but a function of measurement conditions. Specifically, it is important to consider the dependency of dMRI derived parameters on diffusion time² when a higher-order term of diffusion signal, such as diffusional kurtosis³, is included. Our work investigates how reliably diffusivity and diffusional kurtosis can be measured for long diffusion times (100 - 800 ms). To carry out these experiments, we developed an in-house Stimulated Echo (STEAM-EPI) sequence that can achieve long diffusion time while keeping echo time and b-value constant. Based on well-established diffusion and kurtosis phantoms, we found that time-dependent dMRI measurements can provide stable diffusion and kurtosis values over a wide range of diffusion times. Moreover, estimation of cellular-interstitial water exchange time can be achieved using Kärger model (KM) for the metastatic lymph nodes in head and neck cancer patients.

METHODS

We tested the time-dependent dMRI on two diffusion phantoms. The first phantom is a diffusion phantom provided by National Institute of Standards and Technology (NIST)⁴, composed of thirteen 30 ml vials with different polyvinylpyrrolidone (PVP) concentrations: 0%, 10%, 20%, 30%, 40% and 50%, bathed in ice water. The second phantom is a kurtosis phantom⁵, composed alcohols and surfactants, creating nanoscopic vesicles, measured at room temperature. Five tonsil biopsy-proven oropharyngeal squamous cell carcinoma (OPSCC) patients with metastatic lymph nodes were recruited. All data were acquired with our in-house STEAM-EPI sequence (Fig. 1) on a 3T MAGNETOM Prisma MRI system using a 20-channel head/neck coil array. The STEAM-EPI imaging parameters included: TR/TE=5000/60 ms, resolution=1.5x1.5x4.0 mm³, FOV=190 mm, partial Fourier 6/8, and GRAPPA with R=2. The STEAM-EPI diffusion parameters included $$$\delta$$$ = 15 ms with five diffusion times, [∆ = 100, 200, 300, 500, 700 ms], one b=0 and 4 b-shells [b = 200, 1000, 2000, 3000 s/mm²] with 3 diffusion directions along x, y, and z axes. Each set of images were registered, denoised, de-Gibbsed and corrected for Rician bias. Following post-processing, diffusion and kurtosis maps were generated via a weighted linear least square fit method (Fig. 2). Multi-slice regions of interest (ROI) were drawn for each patient. For each voxel in the ROIs, diffusion and kurtosis were also calculated via a model-based method assuming that for the range of diffusion times $$$D(t)$$$ remains constant (Fig. 3): $$D=(1-v_e ) D_e+v_e D_i=const$$ In this regime, $$$D$$$ is sensitive towards exchanging volume fractions, $$$v_e$$$, whose water exchange time could be determined by modelling $$$K(t)$$$ using the Kärger model (KM)⁶: $$K(t)=K_∞+K_0\frac{2τ_{ex}}{t} [1-\frac{τ_{ex}}{t} (1-e^{-t⁄τ_{ex}} )]$$ where $$$K_0+K_∞$$$ is the maximum of $$$K$$$ in the case of impermeable barriers and $$$ K_∞$$$ accounts for a partial volume effect. Furthermore, the time dependence of the cumulants $$$D$$$ and $$$K(t)$$$ can be used to estimate diffusion weighted signals: $$S_e (t,b)=S_{e0}(t)exp(-bD+\frac{1}{6}b^2 D^2 K(t))$$ The estimated signal $$$S_e (t,b)$$$ can be linearly scaled by adjusting $$$S_{e0}(t)$$$ to match the measured signal $$$S_m (t,b)$$$ for each diffusion time. Then, estimation of four KM parameters is conducted by minimizing the sum of squared differences between the estimated and measured signals for each voxel: $$\left\{K_0, K_∞, τ_{ex},D\right\}=arg ⁡min ∑_{t,b}(S_e (t,b) - S_m (t,b))^2 $$.

RESULTS AND DISCUSSION

Overall diffusivity variation measured by the NIST phantom, across the diffusion times, was 5.4±3.0%. In the Kurtosis phantom, low concentration samples showed characteristic patterns of low permeability, while high concentration samples showed some permeability characterized by strong diffusivity and kurtosis dependency, over different diffusion time. Figure 2 shows b0 images from a patient with a metastatic cervical node with good SNR and estimated diffusivity and kurtosis maps for each diffusion time. Figure 3 shows a representative case with a lymph node that has a cluster of voxels suitable for KM (red) characterized by constant diffusivity over the diffusion times. For the two slices shown in Figure 3, 47% and 35% of the whole lesion voxels were found suitable for KM analysis, respectively. Figure 4 shows representative parameter maps of three patients using KM analysis. The KM analysis over all five cases, suggested median $$$K_0$$$ between 0.3 and 0.65 and median cellular-interstitial exchange time between 58.5 and 70.6 ms (Figure 5).

CONCLUSION

Time-dependent dMRI measurements over a wide range of diffusion times can provide stable diffusion and kurtosis values. Moreover, estimation of cellular-interstitial water exchange time was achieved using Kärger model for the metastatic lymph nodes in patients with head and neck cancer. Future studies will evaluate the prognostic value of these imaging markers.

Acknowledgements

NIH grants UH3CA228699, R01CA160620, R01CA219964 and R01EB028774.