Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) consists of the continuous acquisition of images before, during, and after the injection of a contrast agent. DCE-MRI allows for noninvasive evaluation of tumor parameters related to vascular perfusion and permeability and tissue volume fractions, and is frequently employed in both preclinical and clinical investigations. However, the experimental and analytical subtleties of the technique are not frequently discussed in the literature, nor are its relationships to other commonly used quantitative imaging techniques. This review aims to provide practical information on the development, implementation, and validation of a DCE-MRI study in the context of a preclinical study (though we do frequently refer to clinical studies that are related to these topics).

For quantitative analysis of DCE-MRI data, the time course of the concentration of the contrast agent in the blood plasma, or vascular input function (VIF), is required. We compared pharmacokinetic parameters derived using individual and population-based VIFs in mice for two different contrast agents, Gd-DTPA and P846. Eleven mice with subcutaneous 4T1 breast cancer xenografts were imaged at 7T. A pre-contrast T1 map was acquired along with dynamic T1-weighted gradient echo images before, during, and after a bolus injection of contrast agent delivered via a syringe pump. Each animal's individual VIF (VIFind) and derived population-averaged VIF (VIFpop) were used to extract parameters from the signal-time curves of tumor tissue at both the region of interest (ROI) and voxel level. The results indicate that for both contrast agents, Ktrans values estimated using VIFpop have a high correlation (CCC > 0.85) with Ktrans values estimated using VIFind on both an ROI and voxel level. This work supports the validity of using of a population-based VIF with a stringent injection protocol in pre-clinical DCE-MRI studies.

Accurate quantification of pharmacokinetic parameters in dynamic contrast-enhanced (DCE) MRI may be affected by the passive diffusion of contrast agent (CA) within the tissue. By introducing an additional term into the standard Tofts-Kety (STK) model, we correct for the effects of CA diffusion. We first develop the theory describing a CA diffusion corrected STK model (DTK). The model is then tested in simulation with simple models of diffusion. The DTK model is also fit to 18 in vivo DCE-MRI acquisitions from murine models of cancer and results are compared to those from the STK model. The DTK model returned estimates with significantly lower error than the STK model (p≪0.001). In poorly-perfused (i.e., Ktrans≤0.05 min−1) regions the STK model returned unphysical ve values, while the DTK model estimated ve with less than 7% error in noise-free simulations. Results in vivo data revealed similar trends. For voxels with low Ktrans values and late peak concentration times the STK model returned ve estimates >1.0 in 40% of the voxels as compared to only 16% for the DTK model. The DTK model presented here shows promise in estimating accurate kinetic parameters in the presence of passive contrast agent diffusion.

Overall, our data showed a significant difference in the contrast enhancement kinetic parameters between benign and malignant ovarian masses.

Quantitative analysis of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data requires the accurate determination of the arterial input function (AIF). A novel method for obtaining the AIF is presented here and pharmacokinetic parameters derived from individual and population based AIFs are then compared. A Philips 3.0 T Achieva MR scanner was used to obtain 20 DCE-MRI data sets from ten breast cancer patients prior to and after one cycle of chemotherapy. Using a semi-automated method to estimate the AIF from the axillary artery, we obtain the AIF for each patient, AIFind, and compute a population averaged AIF, AIFpop. The extended standard model is used to estimate the physiological parameters using the two types of AIFs. The mean concordance correlation coefficient (CCC) for the AIFs segmented manually and by the proposed AIF tracking approach is 0.96, indicating accurate and automatic tracking of an AIF in DCE-MRI data of the breast is possible. Regarding the kinetic parameters, the CCC values for Ktrans, vp, and ve as estimated by AIFind and AIFpop are 0.65, 0.74, and 0.31, respectively, based on region of interest analysis. The average CCC values for the voxel-by-voxel analysis are 0.76, 0.84, and 0.68 for Ktrans, vp, and ve, respectively. This work indicates that Ktrans and vp show a good agreement between AIFpop and AIFind while there is a weak agreement on ve.

Dynamic contrast enhanced MRI (DCE-MRI) involves the acquisition of images before, during, and after the injection of a contrast agent (CA). In order to perform quantitative modeling on the resulting signal intensity time course, data must be acquired rapidly, which compromises spatial resolution, signal-to-noise, and/or field of view. One approach that may allow for gains in temporal or spatial resolution or signal-to-noise of an individual image is to use compressed sensing (CS) MRI. In this study, we demonstrate the accuracy of extracted pharmacokinetic parameters from DCE-MRI data obtained as part of pre-clinical and clinical studies in which fully sampled acquisitions have been retrospectively undersampled by factors of 2, 3, and 4 in Fourier space and then reconstructed with CS. The mean voxel-level concordance correlation coefficient for Ktrans (i.e., the volume transfer constant) obtained from the 2× accelerated and the fully sampled data is 0.92 and 0.90 for mouse and human data, respectively; for 3× the results are 0.79 and 0.79, respectively; for 4×, the results are 0.64 and 0.70, respectively. The mean error in the tumor mean Ktrans for the mouse and human data at 2× acceleration is 1.8% and −4.2%, respectively; at 3×, 3.6% and −10%, respectively; at 4×, 7.8% and −12%, respectively. These results suggest that CS combined with appropriate reduced acquisitions may be an effective approach to improving image quality in DCE-MRI.

We build on previous work to show how serial diffusion-weighted MRI (DW-MRI) data can be used to estimate proliferation rates in a rat model of brain cancer. Thirteen rats were inoculated intracranially with 9L tumor cells; eight rats were treated with the chemotherapeutic drug 1,3-bis(2-chloroethyl)-1-nitrosourea and five rats were untreated controls. All animals underwent DW-MRI immediately before, one day and three days after treatment. Values of the apparent diffusion coefficient (ADC) were calculated from the DW-MRI data and then used to estimate the number of cells in each voxel and also for whole tumor regions of interest. The data from the first two imaging time points were then used to estimate the proliferation rate of each tumor. The proliferation rates were used to predict the number of tumor cells at day three and this was correlated to the corresponding experimental data. The voxel-by-voxel analysis yielded Pearson’s correlation coefficients ranging from −0.06 to 0.65, whereas the region of interest analysis provided Pearson’s and concordance correlation coefficients of 0.88 and 0.80, respectively. Additionally, the ratio of positive to negative proliferation values was used to separate the treated and control animals (p < 0.05) at an earlier point than the mean ADC values. These results further illustrate how quantitative measurements of tumor state obtained non-invasively by imaging can be incorporated into mathematical models that predict tumor growth.

Purpose To determine the relationship between the apparent diffusion coefficient (ADC, from diffusion weighted (DW) MRI), the extravascular, extracellular volume fraction (ve, from dynamic contrast enhanced (DCE) MRI), and histological measurement of the extracellular space fraction. Methods Athymic nude mice were injected with either human epidermal growth factor receptor 2 positive (HER2+) BT474 (n = 15) or triple negative MDA-MB-231 (n = 20) breast cancer cells, treated with either Herceptin (n = 8), Abraxane (low dose n = 7, high dose n =6), or saline (n = 7 for each cell line), and imaged using DW- and DCE-MRI before, during, and after treatment. After the final imaging acquisition, the tissue was resected and evaluated by histological analysis. H&E stained central slices were scanned using a digital brightfield microscope and evaluated with thresholding techniques to calculate the extracellular space. Results For both BT474 and MDA-MB-231, the median ADC of the central slice exhibited a significantly positive correlation with the corresponding central slice extracellular space as measured by H&E (p = 0.03, p < 0.01, respectively). Median ve calculated from the central slice showed differing results between the two cell lines. For BT474, a significant correlation between ve and extracellular space was calculated (p = 0.02), while MDA-MB-231 tumors did not demonstrate a significant correlation (p = 0.64). Additionally, there was no correlation discovered between ADC and ve with either whole tumor analysis or central slice analysis (p > 0.05). Conclusion While ADC correlates well with the histologically determined fraction of extracellular space, this data adds to the growing body of literature which suggests that ve derived from DCE-MRI is not a reliable biomarker of extracellular space for a range of physiological conditions.