A new method for imaging perfusion and contrast extraction fraction: Input functions derived from reference tissues

Abstract

This study describes a new method for analysis of dynamic MR contrast data that greatly increases the time available for data acquisition. The capillary input function, CB(t), is estimated from the rate of contrast agent uptake in a reference tissue such as muscle, based on literature values for perfusion rate, extraction fraction, and extracellular volume. The rate constant for contrast uptake (the product of perfusion rate, F, and extraction fraction, E; F x E) is then determined in each image pixel using CB(t), extracellular volume (relative to the reference tissue) measured from MR and the tissue concentration of contrast media as a function of time calculated from the MR data. The "reference tissue method" was tested using rats with mammary (n = 10) or prostate (n = 15) tumors implanted in the hindlimb. Dynamic MR images at 4.7 T were acquired before and after Gd-DTPA intravenous bolus injections to determine F x E(Gd-DTPA). Acquisition parameters were optimized for detection of the first pass of the contrast agent bolus, so that "first-pass analysis" could be used as the "gold standard" for determination of F x E. The accuracy of values of F x E determined using the reference tissue method was determined based on comparison with first-pass analysis. In some cases, deuterated water (D2O) was injected i.v. immediately after Gd-DTPA measurements, and the reference tissue method was used to calculate F, based on the rate of uptake of D2O. Comparison of rate constants for Gd-DTPA uptake and D2O uptake allowed calculation of E(Gd-DTPA). Values for F x E(Gd-DTPA), F, and E(Gd-DTPA) were determined for selected regions and on a pixel-by-pixel basis. Values for F x E and E(Gd-DTPA) measured using the reference tissue method correlated well (P = .90 with a standard error of +/- .016, n = 15) with values determined based on first-pass contrast media uptake. The reference tissue method has important advantages: (a) A large volume of reference tissue can be used to determine the contrast agent input function with high precision. (b) Data obtained for 20 minutes after injection are used to calculate F or F x E. The greatly increased acquisition time can be used to increase the spatial resolution, field of view or SNR of measurements. The reference tissue method is most useful when the volume of tissue that must be imaged and/or the spatial resolution required precludes use of traditional first-pass methods.

Fundamentals of tracer kinetics for dynamic contrast‐enhanced MRI

“…The reference region/tissue (RR) method allows for assessment of tracer kinetic parameters in the tissue of interest without direct sampling of the AIF, by reference to other normal tissues with known (literature) values for the kinetic parameters, or by using one or more such reference tissues to estimate the AIF. The initial RR methods involve a single reference tissue with innovative implementations of the GK model in differential (93) and integral (94) forms. These methods are subsequently extended to include the use of two reference tissues (95), model AIFs (96,97), and approaches to reduce the use of literature values in the reference tissues (98).…”

Method for quantitative mapping of dynamic MRI contrast agent uptake in human tumors

“…However, to minimize variations among patients and different measurements caused by variable systemic blood supply, it is necessary to apply some kind of normalization. To do so, signal enhancement of healthy tissue may serve as a reference (12,13), but a more direct reference would be to measure concentration-time curves of the contrast agent in the feeding vessels (arterial input function (AIF)). Furthermore, parametric analysis alone does not provide insight into the underlying physiology of the tumor.…”

Arterial input functions in dynamic contrast-enhanced magnetic resonance imaging: which model performs best when assessing breast cancer response?

“…Another method uses normal reference tissue sampling (e.g. muscle) and knowledge of leakage space to infer the AIF (Kovar method 5 ). The most commonly used and convenient method is to use assumed or population-based AIFs.…”