Quantitative Estimation of Dynamic Contrast Enhanced MRI Parameters in Rat Brain Gliomas Using a Dual Surface Coil System

Rationale and Objectives

The study of vascular targeted cancer therapies is critically dependent on the development of noninvasive methods for assessing changes in vascular permeability and blood flow in small-animal tumor models.

Materials and Methods

A multicoil apparatus consisting of two receive-only surface coils for observation of the rat brain and chest, a whole-body transmit-only volume coil, and the switching circuitry necessary for sharing a single receiver channel between the two surface coils was constructed for the parallel observation of left ventricular arterial input function and magnetic resonance imaging (MRI) signal intensity kinetics in the rat brain. Dynamic contrast-enhanced MRI was performed on four Fischer rats bearing intracranial 9L gliomas, and the dynamic data were evaluated using the bolus-enhanced relaxation overview (BOLERO) model yielding maps of K trans , v e , and τ i values from the tumor.

Results

The use of the multicoil apparatus resulted in images with high signal-to-noise ratios from the rat brain and chest in parallel, with no detectable crosstalk between the surface coils. The BOLERO method accurately fit the observed data to within experimental error. Mean values of the parameters generated by the BOLERO analysis for the tumor were K trans = 0.023 ± 0.014 s −1 , τ i = 1.3 ± 0.6 seconds, and v e = 0.51. K trans and τ i values were slightly elevated in the tumor periphery, whereas v e was elevated in the tumor core.

Conclusion

These results demonstrate the feasibility of measuring quantitative dynamic contrast-enhanced MRI parameters in a rat brain tumor model using a multicoil apparatus. These methods might play an important role in determining the efficacy of antiangiogenic therapies in small-animal models.

The ongoing efforts to develop cancer therapies that target tumor vasculature would benefit significantly from the development of noninvasive methods for assessing vascular permeability and changes in blood flow in tumors in small-animal models. Dynamic magnetic resonance imaging (MRI) techniques show promise for providing such information. These methods use rapid imaging techniques, typically a fast gradient-echo protocol, to track a bolus of a paramagnetic contrast agent as it passes through the tissues of interest. The observed changes in signal intensity are then fit to a suitable pharmacokinetic model to yield tissue parameter maps. The precise nature of the information provided by dynamic MRI methods depends in part on the specific imaging protocol used. Dynamic T 1 -weighted imaging, also referred to as dynamic contrast-enhanced (DCE) MRI, provides measures of vascular permeability and extracellular volume fraction. Analysis of DCE MRI data requires an estimate of the relaxivity of the contrast agent and knowledge of the instantaneous concentration of the tracer in the arteries feeding the tissues of interest, known as the arterial input function (AIF). Numerous approaches to determining the AIF have been suggested, including using a fixed model , extracting the AIF from the target-tissue signal intensity data using curve-fitting techniques , extraction of the AIF from the signal evolution in a reference tissue , and direct observation of the AIF in large arteries.

Previous studies have suggested that there are significant intersubject and shot-to-shot variations in the AIF , making techniques that rely on assumptions about its functional form unreliable. In contrast to the other approaches, direct observation of the AIF makes no assumptions about the AIF or the vascular permeability parameters of adjacent tissues and as such is the preferred method. In large animals (such as dogs), the AIF can often be extracted from arterial pixels in the dynamic images . Automated routines for selecting pixels associated with arteries in DCE images have been demonstrated . Estimates of the AIF extracted from the temporal evolution of the signal intensity of the automatically selected arterial pixels have been found to be consistent with expectations.

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Materials and methods

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Theory

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Ct(t)=Ktrans∫0tCp(t′)exp{−kep(t−t′)}dt′, C

(

)

trans

∫

(

′

)

exp

{

−

(

−

′

)

}

′

where C t is the tracer concentration in the tissue, C p is the AIF, k ep is the tissue exchange rate constant, and K trans is the tissue transfer constant, which is given by k ep = K trans / v e , where v e is the extravascular, extracellular volume fraction of water. In dynamic imaging studies, in which the tracer is detected directly, the above relation is sufficient to describe the observed signal changes following bolus administration. However, paramagnetic MRI contrast agents are indirectly detected by their effects on the relaxation rate of the indigenous water. In isotropic solutions, the longitudinal relaxation rate ( R 1 = 1/ T 1 ) in the presence of a paramagnetic contrast agent is given by

R1=R01+r1C, R

where R01 R

0 is the relaxation rate in the absence of contrast agent, C is the concentration of the agent, and r 1 is a constant for a given agent known as the relaxivity. Water protons must come in close contact with the paramagnetic center of the contrast agent for the water relaxation rate to be affected. Tissues are highly compartmentalized, consisting of intracellular, extravascular extracellular, and intravascular spaces. The contrast agent generally permeates the intravascular and extravascular extracellular spaces but is excluded from the cytoplasm of intact cells. If it is assumed that the exchange rate of mobile water protons between the intracellular and extracellular spaces is fast relative to the difference in relaxation rate between these compartments, the observed relaxation rate is the water volume fraction weighted average of relaxation rates in the two compartments. This condition, referred to as the fast exchange limit (FXL), is commonly assumed in the analysis of DCE MRI data. If it is also assumed that the intracellular and extracellular water relaxation rates are identical in the absence of contrast agent, the observed relaxation rate takes the form

R1=R01+ver1Ct. R

This equation may be rearranged to express the tissue contrast agent concentration, C t , in terms of the relaxation rate. Substitution of the resulting expression into Equation 1 yields an expression for the tissue tracer concentration in terms of observable MRI parameters. This method of analysis of DCE data is referred to in this report as the FXL model.

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R∗1=(2R01+r1C+1/τive)−(r1C+1/τive)2−4r1C/τi√2, R

∗

(

)

−

(

)

−

where τ i is the mean lifetime of the intracellular water, and the remaining variables have been defined previously. Equation 4 is the main result of the BOLERO method for analyzing DCE data . Note that Equation 4 differs slightly from the previously published BOLERO equation in that the intracellular and extracellular relaxation rates are assumed to be identical in the absence of contrast agent. In analogy to the FXL case, this result may be combined with Equation 1 to yield an expression for the tissue contrast agent concentration in terms of MRI observable parameters.

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R1(t)=1Tsln[S(t)S0(1−exp{−TsR01})−1], R

(

)

[

(

)

(

−

exp

{

−

}

)

−

]

where S 0 is the observed signal intensity in the absence of contrast agent, and T s is the saturation recovery time. Maps of the instantaneous relaxation rate can thus be determined from saturation recovery images if the pre–contrast agent relaxation rate, R01 R

0 , and the baseline signal intensity are known.

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Experimental

Cell Culture and Tumor Inoculation

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MRI Hardware

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Animal Preparation for MRI

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MRI Protocol

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Data Analysis

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Results

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Table 1

Mean Values of Physiological Parameter Estimates Generated in the Dynamic Contrast-enhanced Magnetic Resonance Imaging Study

Animal No.T 1 Gray Matter (seconds)T 1 Blood (seconds)K trans (s −1 ) τ 1 (seconds)v e 1 1.48 ± 0.08 1.67 ± 0.19 0.016 ± 0.010 1.52 ± 0.34 0.57 ± 0.32 2 1.48 ± 0.15 1.69 ± 0.34 0.022 ± 0.011 1.19 ± 0.52 0.46 ± 0.27 3 1.00 ± 0.45 1.73 ± 0.19 0.036 ± 0.021 1.01 ± 0.43 0.51 ± 0.37 4 1.22 ± 0.10 1.66 ± 0.60 0.021 ± 0.013 1.50 ± 0.1 0.51 ± 0.25 Mean 1.30 ± 0.19 1.69 ± 0.33 0.023 ± 0.014 1.30 ± 0.57 0.52 ± 0.30

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Discussion

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Conclusion

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Acknowledgments

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References

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Quantitative Estimation of Dynamic Contrast Enhanced MRI Parameters in Rat Brain Gliomas Using a Dual Surface Coil System

Rationale and Objectives

Materials and Methods

Results

Conclusion

Materials and methods

Theory

Experimental

Cell Culture and Tumor Inoculation

MRI Hardware

Animal Preparation for MRI

MRI Protocol

Data Analysis

Results

Discussion

Conclusion

Acknowledgments

References

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