Demonstration of Non-Gaussian Restricted Diffusion in Tumor Cells Using Diffusion Time-Dependent Diffusion-Weighted Magnetic Resonance Imaging Contrast

Abstract

The diffusion-weighted magnetic resonance imaging (DWI) technique enables quantification of water mobility for probing microstructural properties of biological tissue and has become an effective tool for collecting information about the underlying pathology of cancerous tissue. Measurements using multiple b-values have indicated biexponential signal attenuation, ascribed to “fast” (high ADC) and “slow” (low ADC) diffusion components. In this empirical study, we investigate the properties of the diffusion time (Δ)-dependent components of the diffusion-weighted (DW) signal in a constant b-value experiment. A xenograft gliobastoma mouse was imaged using Δ = 11 ms, 20 ms, 40 ms, 60 ms, and b = 500–4000 s/mm2 in intervals of 500 s/mm2. Data were corrected for EPI distortions, and the Δ-dependence on the DW-signal was measured within three regions of interest [intermediate- and high-density tumor regions and normal-appearing brain (NAB) tissue regions]. In this study, we verify the assumption that the slow decaying component of the DW-signal is non-Gaussian and dependent on Δ, consistent with restricted diffusion of the intracellular space. As the DW-signal is a function of Δ and is specific to restricted diffusion, manipulating Δ at constant b-value (cb) provides a complementary and direct approach for separating the restricted from the hindered diffusion component. We found that Δ-dependence is specific to the tumor tissue signal. Based on an extended biexponential model, we verified the interpretation of the diffusion time-dependent contrast and successfully estimated the intracellular restricted ADC, signal volume fraction, and cell size within each ROI.

Time‐dependent diffusion MRI to distinguish malignant from benign head and neck tumors

“…20 Imaging of cell size and density of solid tumors has been developed using imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED) by combining the OGSE and PGSE sequences in human breast cancers 24 and human tumor xenografts in mice. 25 The observed decrease in ADC values with increasing diffusion time in tumors is in agreement with the literature and our preclinical investigations, 11,12,[25][26][27][28] while in the phantom, deviations between OGSE and PGSE measurements were negligible, regardless of the PVP concentration. This suggests that diffusion hindrance increases with diffusion time in tumors, as more molecules collide with boundaries, such as cell membranes, while free diffusion without hindrance results in the stable ADC values in PVP regardless of diffusion times.…”

Modeling the diffusion‐weighted imaging signal for breast lesions in the b = 200 to 3000 s/mm2 range: quality of fit and classification accuracy for different representations

“…Biexponential representation (Equation ):where, if interpreted as a 2‐component model, the signal fraction f corresponds to the fraction of “fast” diffusion component with the apparent diffusion coefficient D 1 , while (1 – f ) and D 2 represents the fraction and apparent diffusion coefficient of the “slow” component ( D 1 > D 2 ). The biexponential model presented here is mathematically identical to the IVIM model.…”

Time-Dependent Diffusion MRI in Cancer: Tissue Modeling and Applications

“…Additional parameters are required to describe the ECS diffusion outside the tortuosity limit (D = cste) and short-time regime (Equations 1-2), or to model additional compartments, such as vasculature with VERDICT [16]. In practice, multiple PGSE [16,45] or a combination of PGSE and OGSE [14,15] measurements are combined in order to probe diffusion in a specific or over several frequency/time domains.…”

“…At preclinical level, in vivo time-dependent studies have focused on brain gliomas using rat [45,75,76] and mice models [15,18,48], as well as mice xenografts models of colorectal [16,46,51] and ovarian cancer [35].…”