Measurement of cellular-interstitial water exchange time in tumors based on diffusion-time-dependent diffusional kurtosis imaging

Abstract

Purpose: To assess the feasibility of using diffusion-time-dependent diffusional kurtosis imaging (tDKI) to measure cellular-interstitial water exchange time (τ_ex ) in tumors, both in animals and in humans.

Methods: Preclinical tDKI studies at 7 T were performed with the GL261 glioma model and the 4T1 mammary tumor model injected into the mouse brain. Clinical studies were performed at 3 T with women who had biopsy-proven invasive ductal carcinoma. tDKI measurement was conducted using a diffusion-weighted STEAM pulse sequence with multiple diffusion times (20-800 ms) at a fixed echo time, while keeping the b-values the same (0-3000 s/mm² ) by adjusting the diffusion gradient strength. The tDKI data at each diffusion time t were used for a weighted linear least-squares fit method to estimate the diffusion-time-dependent diffusivity, D(t), and diffusional kurtosis, K(t).

Results: Both preclinical and clinical studies showed that, when diffusion time t ≥ 200 ms, D(t) did not have a noticeable change while K(t) decreased monotonically with increasing diffusion time in tumors and t ≥ 100 ms for the cortical ribbon of the mouse brain. The estimated τ_ex averaged median and interquartile range (IQR) of GL261 and 4T1 tumors were 93 (IQR = 89) ms and 68 (78) ms, respectively. For the cortical ribbon, the estimated τ_ex averaged median and IQR were 41 (34) ms for C57BL/6 and 30 (17) ms for BALB/c. For invasive ductal carcinoma, the estimated τ_ex median and IQR of the two breast cancers were 70 (94) and 106 (92) ms.

Conclusion: The results of this proof-of-concept study substantiate the feasibility of using tDKI to measure cellular-interstitial water exchange time without using an exogenous contrast agent.

Keywords: cortex; diffusion MRI; diffusional kurtosis imaging; intracellular water lifetime; microstructure; transcytolemmal water exchange; tumor.

FIGURE 1

Representative diffusion-weighted images of one of the mice with the GL261 murine glioma model. The top panel shows one slice in the middle of the tumor with the lowest and highest b-values (ie 200 and 3000 s/mm²) at all the diffusion times used in this study between 20 and 800 ms. A red box marks the area with the tumor. The area in the red box is shown in the lower panel with the estimated D(t) and K(t) maps for individual diffusion times, which demonstrate substantial decreases of K(t) in most voxels

FIGURE 2

A representative example of tDKI data acquired from a GL261 tumor (the same one as shown in Figure 1). A, T₂-weighted RARE sagittal image that was used to identify the tumor. B, Diffusion-weighted images for diffusion time t = 200 ms with different b-values (s/mm²) with tumor (red ROI) and cortex ribbon (blue ROI) delineated on the T₂-weighted RARE image. C, D, Median D(t) (C) and K(t) curves (D) show the diffusion time dependence of diffusivity and kurtosis for the tumor and the cortical ribbon from the bootstrapping analysis

FIGURE 3

tDKI metrics in GL261 tumors. A, B, Diffusivity shows a weak time dependence for t = 200–800 ms, with the slopes of linear model fits (solid lines) close to zero, in each tumor (A) and the data averaged over all tumors (B). C, D, For the same diffusion times, kurtosis shows a distinct time dependence that can be well described by the Kärger model (solid lines) in each tumor (C) and the data averaged over all tumors (D)

FIGURE 4

tDKI metrics in 4T1 tumors. A, B, Diffusivity shows a weak time dependence for t = 200–800 ms, with the slopes of linear model fits (solid lines) close to zero, in each tumor (A) and the data averaged over all tumors (B). C, D, For the same diffusion times, kurtosis shows a distinct time dependence that can be well described by the Kärger model (solid lines) in each tumor (C) and the data averaged over all tumors (D)

FIGURE 5

tDKI metrics in the cortical ribbon of C57BL/6 mouse brains. A, B, Diffusivity shows a weak time dependence for t = 100–800 ms, with the slopes of linear model fits (solid lines) close to zero, in each tumor (A) and the data averaged over all tumors (B). C, D, For the same diffusion times, kurtosis shows a distinct time dependence that can be well described by the Kärger model (solid lines) in each tumor (C) and the data averaged over all tumors (D)

FIGURE 6

tDKI metrics in the cortical ribbon of BALB/c mouse brains. A, B, Diffusivity shows a weak time dependence for t = 100–800 ms, with the slopes of linear model fits (solid lines) close to zero, in each tumor (A) and the data averaged over all tumors (B). C, D, For the same diffusion times, kurtosis shows a distinct time dependence that can be well described by the Kärger model (solid lines) in each tumor (C) and the data averaged over all tumors (D)

FIGURE 7

Estimation of T₁ from S₀ estimates at different mixing times used in this study for GL261 tumors (A), 4T1 tumors (B) and the cortical ribbons of C57BL/6 (C) and BALB/c mice (D). A linear model in the logarithmic scale (ie mono-exponential model) is fit to the S₀(t) values for t = 189–789 ms in tumor ROIs (A, B) and t = 89–789 ms in the cortical ribbon ROIs (C, D). These mixing time ranges correspond to the diffusion times used for the Kärger model fits in Figures 3–6. The T₁ values shown in the plots are from the slopes of the linear model fits. The R² values are close to 1.0 for all cases, supporting the view that the T₁ relaxation in all ROIs is mono-exponential

FIGURE 8

tDKI data of two biopsy proven invasive ductal carcinomas in a 35-year-old woman (A-D) and a 56-year-old woman (E-H). A, E, Post-contrast T₁-weighted images are angled oblique axial slices as per the clinical breast imaging protocol. Two adjacent slices are shown to roughly match the lesion on the axial diffusion-weighted images that are not angled. B, F, Diffusion-weighted images with multiple b-values and diffusion times for one slice with the cancer lesion shown in A, E. The lesion is shown clearly in these diffusion-weighted images with fat suppression (arrow). C, G, D(t) measured from the tumor is shown by a plot of the mean values with the error bars for the standard deviation. D, H, K(t) measured from the tumor is shown by a plot of the mean values with the error bars for the standard deviation. The solid lines are the linear model fits for D(t) and the Kärger model fits for K(t) with the standard deviations shown by the shaded areas