Pulsed and oscillating gradient MRI for assessment of cell size and extracellular space (POMACE) in mouse gliomas

Abstract

Solid tumor microstructure is related to aggressiveness of tumor, interstitial pressure and drug delivery pathways that are closely associated with treatment response, metastatic spread and prognosis. In this study, we introduce a novel diffusion MRI data analysis framework, Pulsed and Oscillating gradient MRI for Assessment of Cell size and Extracellular space (POMACE), and demonstrate its feasibility in a mouse tumor model. In vivo and ex vivo POMACE experiments were performed on mice bearing the GL261 murine glioma model (n=8). Since the complete diffusion time-dependence is in general non-analytical, the tumor microstructure was modeled in an appropriate time/frequency regime by impermeable spheres (radius Rcell, intracellular diffusivity Dics) surrounded by extracellular space (approximated by constant apparent diffusivity Decs in volume fraction ECS). POMACE parametric maps (ECS, Rcell, Dics, Decs) were compared with conventional diffusion weighted imaging metrics, electron microscopy (EM), alternative ECS determination based on effective medium theory (EMT), and optical microscopy performed on the same samples. It was shown that Decs can be approximated by its long-time tortuosity limit in the range [1/(88 Hz) - 31 ms]. ECS estimations (44±7% in vivo and 54±11% ex vivo) were in agreement with EMT-based ECS and literature on brain gliomas. Ex vivo, ECS maps correlated well with optical microscopy. Cell sizes (Rcell=4.8±1.3 in vivo and 4.3±1.4 μm ex vivo) were consistent with EM measurements (4.7±1.8 μm). In conclusion, Rcell and ECS can be quantified and mapped in vivo and ex vivo in brain tumors using the proposed POMACE method. Our experimental results support that POMACE provides a way to interpret the frequency- or time-dependence of the diffusion coefficient in tumors in terms of objective biophysical parameters of neuronal tissue, which can be used for non-invasive monitoring of preclinical cancer studies and treatment efficacy.

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

“…In addition to the unrealistic case of infinite impermeable membranes already described by Tanner and Stejskal [41], similar expressions were derived for diffusion inside spherical shells [42] and infinite cylinders [43]. The former, in order to represent cellular nuclei and cytoplasm, adds two extra degrees of freedom to a problem already prone to overfitting [15]. The latter was shown successful in estimating the size of small cylinders in the absence of an extracellular medium [44] and could be promising for axonal size estimation but is of little use for MR in cancer.…”

“…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.…”

“…The surface-to-volume ratio and free diffusivity are first evaluated using high-frequency OGSE in the short-time regime [18] using Equation (2). These values are then used as constraints when fitting the lowfrequency OGSE and PGSE data ( Figure 5, f OGSE < 88 Hz) to a model of impermeable spheres bathing in ECS [15].…”

“…Oscillations frequencies are normalized to the tissue characteristic frequency D 0 /R 2 . The EM was extracted from the dataset used for cell size measurement in Reynaud et al [15].…”

“…Using these results, the main models used to characterize tumor tissue using TDD are reviewed: IMPULSED [14], POMACE [15], and VERDICT [16]. Modeling cells as impermeable spheres, additional assumptions are made to describe the ECS, and finally estimate diffusivities, cell size and volume fraction ex vivo and in vivo.…”