There is a need for accurate quantitative non-invasive biomarkers to monitor myelin pathology in vivo and distinguish myelin changes from other pathological features including inflammation and axonal loss. Conventional MRI metrics such as T2, magnetization transfer ratio and radial diffusivity have proven sensitivity but not specificity. In highly coherent white matter bundles, compartment-specific white matter tract integrity (WMTI) metrics can be directly derived from the diffusion and kurtosis tensors: axonal water fraction, intra-axonal diffusivity, and extra-axonal radial and axial diffusivities. We evaluate the potential of WMTI to quantify demyelination by monitoring the effects of both acute (6 weeks) and chronic (12 weeks) cuprizone intoxication and subsequent recovery in the mouse corpus callosum, and compare its performance with that of conventional metrics (T2, magnetization transfer, and DTI parameters). The changes observed in vivo correlated with those obtained from quantitative electron microscopy image analysis. A 6-week intoxication produced a significant decrease in axonal water fraction (p < 0.001), with only mild changes in extra-axonal radial diffusivity, consistent with patchy demyelination, while a 12-week intoxication caused a more marked decrease in extra-axonal radial diffusivity (p = 0.0135), consistent with more severe demyelination and clearance of the extra-axonal space. Results thus revealed increased specificity of the axonal water fraction and extra-axonal radial diffusivity parameters to different degrees and patterns of demyelination. The specificities of these parameters were corroborated by their respective correlations with microstructural features: the axonal water fraction correlated significantly with the electron microscopy derived total axonal water fraction (ρ = 0.66; p = 0.0014) but not with the g-ratio, while the extra-axonal radial diffusivity correlated with the g-ratio (ρ = 0.48; p = 0.0342) but not with the electron microscopy derived axonal water fraction. These parameters represent promising candidates as clinically feasible biomarkers of demyelination and remyelination in the white matter.
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.
Purpose To disentangle the free diffusivity (D0) and cellular membrane restrictions, via their surface-to-volume ratio (S/V), using the frequency-dependence of the diffusion coefficient D(ω), measured in brain tumors in the short diffusion-time regime using oscillating gradients (OGSE). Methods In vivo and ex vivo OGSE experiments were performed on mice bearing the GL261 murine glioma model (n=10) to identify the relevant time/frequency (t/ω) domain where D(ω) linearly decreases with ω−1/2. Parametric maps (S/V, D0) are compared to conventional DWI metrics. The impact of frequency range and temperature (20°C vs. 37°C) on S/V and D0 is investigated ex vivo. Results The validity of the short diffusion-time regime is demonstrated in vivo and ex vivo. Ex vivo measurements confirm that the purely geometric restrictions embodied in S/V are independent from temperature and frequency range, while the temperature dependence of the free diffusivity D0 is similar to that of pure water. Conclusion Our results suggest that D(ω) in the short diffusion-time regime can be used to uncouple the purely geometric restriction effect, such as S/V, from the intrinsic medium diffusivity properties, and provides a non-empirical and objective way to interpret frequency/time-dependent diffusion changes in tumors in terms of objective biophysical tissue parameters.
The present study demonstrates that DTI at multiple diffusion times with the random permeable model analysis allows for noninvasively quantifying muscle fiber microstructural changes during both normal muscle growth and disease progression. Future studies can apply our technique to evaluate current and potential treatments to muscle myopathies.
Purpose To assess the feasibility to combine dynamic contrast enhanced (DCE) MRI with measurement of RF transmit field B1 and pre-contrast longitudinal relaxation time T10. Methods A novel approach has been proposed to simultaneously estimate B1 and T10 from a modified DCE-MRI scan that actively encodes the wash-out phase of the curve with different amount of T1 and B1 weighting using multiple flip angles and repetition times, hence referred to as active contrast encoding (ACE) MRI. ACE-MRI aims to simultaneously measure B1 and T10 together with contrast kinetic parameters, such as transfer constant Ktrans, interstitial space volume fraction ve, and vascular space volume fraction vp. The proposed method was tested using numerical simulations and in vivo studies with mouse models of breast cancer implanted in the flank and mammary fat pad, and glioma in the brain. Results In the numerical simulation study with signal-to-noise ratio of 10, both B1 and T10 were estimated accurately with error of 5.1±3.5% and 12.3±8.8%, and coefficient-of-variance (CV) of 14.9±8.6% and 15.0±5.0%, respectively. Using the same ACE-MRI data, the kinetic parameters, Ktrans, ve, and vp, were also estimated with error of 14.2%±8.3% (CV=13.5±4.6%), 14.7±9.9% (13.3±4.5%), and 14.0±9.3% (14.0±4.5%), respectively. For the in vivo tumor data from eleven mice, voxel-wise comparisons between ACE-MRI and DCE-MRI methods showed that the mean differences for the five parameters are: ΔKtrans=0.006 (/min), Δve=0.016, Δvp=0.000, ΔB1=−0.014 and ΔT1=−0.085 (s), which suggests a good agreement of the two methods. When compared to separately measured B1 and T10, and DCE-MRI estimated kinetic parameters as reference, the mean relative errors of ACE-MRI estimation are B1= −0.3%, T10= −8.5%, Ktrans=11.4%, ve=14.5% and vp=4.5% respectively. Conclusion This proof-of-concept study demonstrates that the proposed ACE-MRI method can be used to estimate B1 and T10 along with contrast kinetic model parameters.
Object To investigate the effect of T2* correction on estimation of kinetic parameters from T1-weighted dynamic contrast enhanced (DCE) MRI data when a reference-tissue arterial input function (AIF) is used. Materials and Methods DCE-MRI data were acquired from 7 mice with 4T1 mouse mammary tumors using a double gradient echo sequence at 7T. The AIF was estimated from a region of interest in the muscle. The extended Tofts model was used to estimate pharmacokinetic parameters in the enhancing part of the tumor, with and without T2* correction of the lesion and AIF. The parameters estimated with T2* correction of both the AIF and lesion time-intensity curve were assumed to be the reference standard. Results For the whole population, there was significant difference (p<0.05) in transfer constant (Ktrans) between T2* corrected and not corrected methods, but not in interstitial volume fraction (ve). Individually, no significant differences were found in Ktrans and ve of four and six tumors, respectively, between the T2* corrected and not corrected methods. In contrast, Ktrans was significantly underestimated, if the T2* correction was not used, in other tumors of which median Ktrans was larger than 0.4 min−1. Conclusion T2* effect on tumors with high Ktrans may not be negligible in kinetic model analysis, even if AIF is estimated from reference tissue where the concentration of contrast agent is relatively low.
To investigate the validity of contrast kinetic parameter estimates from Active Contrast Encoding (ACE)-MRI against those from conventional Dynamic Contrast-Enhanced (DCE)-MRI for evaluation of tumor treatment response in mouse tumor models. Methods The ACE-MRI method that incorporates measurement of T 1 and B 1 into the enhancement curve washout region, was implemented on a 7T MRI scanner to measure tracer kinetic model parameters of 4T1 and GL261 tumors with treatment using bevacizumab and 5FU. A portion of the same ACE-MRI data was used for conventional DCE-MRI data analysis with a separately measured pre-contrast T 1 map. Tracer kinetic model parameters, such as K trans (permeability area surface product) and v e (extracellular space volume fraction), estimated from ACE-MRI were compared with those from DCE-MRI, in terms of correlation and Bland-Altman analyses. Results A threefold increase of the median K trans by treatment was observed in the flank 4T1 tumors by both ACE-MRI and DCE-MRI. In contrast, the brain tumors did not show a significant change by the treatment in either ACE-MRI or DCE-MRI. K trans and v e values of the tumors from ACE-MRI were strongly correlated with those from DCE-MRI methods with correlation coefficients of 0.92 and 0.78, respectively, for the median values of 17 tumors. The Bland-Altman plot analysis showed a mean difference of-0.01 min-1 for K trans with the 95% limits of agreement of-0.12 min-1 to 0.09 min-1 , and-0.05 with-0.37 to 0.26 for v e. Conclusion The tracer kinetic model parameters estimated from ACE-MRI and their changes by treatment closely matched those of DCE-MRI, which suggests that ACE-MRI can be used in
Purpose To investigate the feasibility of using diffusion MRI (dMRI) and dynamic contrast-enhanced (DCE) MRI to evaluate the treatment response of metronomic chemotherapy (MCT) in the 4T1 mammary tumor model of locally advanced breast cancer. Methods Twelve Balb/c mice with metastatic breast cancer were divided into treated and untreated (control) groups. The treated group (n = 6) received five treatments of anti-metabolite agent 5-Fluorouracil (5FU) in the span of two weeks. dMRI and DCE-MRI were acquired for both treated and control groups before and after MCT. Immunohistochemically staining and measurements were performed after the post-MRI measurements for comparison. Results The control mice had significantly (p<0.005) larger tumors than the MCT treated mice. The DCE-MRI analysis showed a decrease in contrast enhancement for the control group, whereas the MCT mice had a more stable enhancement between the pre-chemo and post-chemo time points. This confirms the antiangiogenic effects of 5FU treatment. Comparing amplitude of enhancement revealed a significantly (p<0.05) higher enhancement in the MCT tumors than in the controls. Moreover, the MCT uptake rate was significantly (p<0.001) slower than the controls. dMRI analysis showed the MCT ADC values were significantly larger than the control group at the post-scan time point. Conclusion dMRI and DCE-MRI can be used as potential biomarkers for assessing the treatment response of MCT. The MRI and pathology observations suggested that in addition to the cytotoxic effect of cell kills, the MCT with a cytotoxic drug, 5FU, induced changes in the tumor vasculature similar to the anti-angiogenic effect.
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