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The transfer constant K trans is commonly employed in dynamic contrast-enhanced MRI studies, but the utility and interpretation of K trans as a potential biomarker of tumor vasculature remains unclear. In this study, computer simulations based on a comprehensive tracer kinetic model with multiple pathways was used to provide clarification on the interpretation and application of K trans . Tissue concentration-time curves pertaining to a wide range of transport conditions were simulated using the multiple-pathway (MP) model and fitted using the generalized kinetic (GK) and extended GK models. Relationships between K trans and plasma flow F p , vessel permeability PS and extraction rate EF p under various transport conditions were assessed by correlation and regression analysis. Results show that the MP model provides an alternative two-tier interpretation of K trans based on the vascular transit time. K trans is primarily associated with F p and EF p respectively, in the slow and rapid vascular transit states, independent of the magnitude of PS. The relative magnitudes of PS and F p only serve as secondary constraints for which K trans can be further associated with EF p and PS in the slow and rapid transit states, respectively.

Contrary to the common notion that tumor necrotic regions are non-enhancing after contrast administration, recent evidence has shown that necrotic regions exhibit delayed and slow uptake of gadolinium tracer on dynamic contrast-enhanced MRI (DCE MRI). The purpose of this study is to explore whether the mapping of tumor voxels with delayed and slow enhancement on DCE MRI can be used to derive estimates of tumor necrotic fraction. Patient-derived tumor xenograft lines of seven human cancers were implanted in 26 mice which were subjected to DCE MRI performed using a spoiled gradient recalled sequence. Gadolinium tracer concentration was estimated using the variable flip angle technique. To identify tumor voxels exhibiting delayed and slow uptake of contrast medium, clustering analysis was performed using a k-means clustering algorithm that classified tumor voxels according to their contrast enhancement patterns. Comparison of the percentage of tumor voxels exhibiting delayed and slow enhancement with the tumor necrotic fraction estimated on histology showed a strong correlation (r = 0.962, p < 0.001). The mapping of tumor regions with delayed and slow contrast uptake on DCE MRI correlated strongly with tumor necrotic fraction, and can potentially serve as a non-invasive imaging surrogate for the in vivo assessment of necrotic fraction., (Copyright © 2014 John Wiley & Sons, Ltd.)

Compartmental tracer kinetic models currently used for analysis of dynamic contrast-enhanced MRI data yield poor fittings or parameter values that are unphysiological in necrotic regions of the tumor, as these models only describe microcirculation in perfused tissue. In this study, we explore the use of Fick's law of diffusion as an alternative method for analysis of dynamic contrast-enhanced MRI data in the necrotic regions. Xenografts of various human cancer cell lines were implanted in 14 mice that were subjected to dynamic contrast-enhanced MRI performed using a spoiled gradient recalled sequence. Tracer concentration was estimated using the variable flip angle technique. Poorly perfused and necrotic tumor regions exhibiting delayed and slow enhancement were identified using a k-means clustering algorithm. Tracer behavior in necrotic regions was shown to be consistent with Fick's diffusion equation and the in vivo gadolinium diffusivity was estimated to be 2.08 (±0.88) × 10(-4) mm(2)/s. This study proposes the use of gadolinium diffusivity as an alternative parameter for quantifying tracer transport within necrotic tumor regions., (Copyright © 2012 Wiley Periodicals, Inc.)

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been widely applied to evaluate microcirculatory parameters in clinical settings. However, pre-clinical studies involving DCE-MRI of small animals remain challenging with the requirement for high spatial and temporal resolution for quantitative tracer kinetic analysis. This study illustrates the feasibility of applying a high temporal resolution (2 s) protocol for liver imaging in mice by analyzing the DCE-MRI datasets of mice liver with a dual-input two-compartment tracer kinetic model. Phantom studies were performed to validate the T(1) estimates derived by the proposed protocol before applying it in mice studies. The DCE-MRI datasets of mice liver were amendable to tracer kinetic analysis using a dual-input two-compartment model. Estimated micro-circulatory parameters were consistent with liver physiology, indicating viability of applying the technique for pre-clinical drug developments.

Dynamic contrast-enhanced (DCE) CT imaging of four patients with hepatocellular carcinoma (HCC) was performed using a dual-phase imaging protocol designed with initial rapid dynamic imaging to capture the initial increase in contrast medium enhancement in order to assess perfusion, followed by a delayed imaging phase with progressively longer intervals to monitor subsequent tissue enhancement behaviour in order to assess tissue permeability. The DCE CT images were analysed using a dual-input two-compartment distributed parameter model to yield separate estimates for blood flow and permeability, as well as fractional intravascular and extravascular volumes. The HCCs and surrounding cirrhotic liver tissues were found to exhibit enhancement curves that can be appropriately described by two distinct compartments separated by a semipermeable barrier. Early contrast arrival was also found for HCC as compared with background liver. These findings are consistent with the current understanding of sinusoidal capillarization and hepatocarcinogenesis.