Uncertainty in MR tracer kinetic parameters and water exchange rates estimated from T₁‐weighted dynamic contrast enhanced MRI

Abstract

Purpose The aim of this study was to assess the uncertainty in estimation of MR tracer kinetic parameters and water exchange rates in T1-weighted dynamic contrast enhanced (DCE)-MRI. Methods Simulated DCE-MRI data were used to assess four kinetic models; general kinetic model with a vascular compartment (GKM2), GKM2 combined with the 3S2X model (SSM2), adiabatic approximation of the tissue homogeneity model (ATH), and ATH combined 3S2X model (ATHX). Results In GKM2 and SSM2, increase in transfer constant (Ktrans) led to underestimation of vascular volume fraction (vb), and increase in vb led to overestimation of Ktrans. Such coupling between Ktrans and vb was not observed in ATH and ATHX. The precision of estimated intracellular water lifetime (τi) was substantially improved in both SSM2 and ATHX when Ktrans > 0.3 min−1. Ktrans and vb from ATHX model had significantly smaller errors than those from ATH model (p<0.05). Conclusion The results of this study demonstrated the feasibility of measuring τi from DCE-MRI data albeit low precision. While the inclusion of the water exchange model improved the accuracy of Ktrans, vb, and the interstitial volume fraction estimation (ve), it lowered the precision of other kinetic model parameters within the conditions investigated in this study.

Shutter‐speed dynamic contrast‐enhanced MRI: Is it fit for purpose?

“…6 Moreover, it is apparent that estimates of the WX-related parameter made using the shutter-speed model (τ i , the intracellular residence time of water) are imprecise. 6,11 Nevertheless, the shutter-speed model has generated considerable interest in the DCE-MRI community, not least because it often fits the data better than the standard 1-compartment Tofts model, 4,12 produces results which may help in the differential diagnosis of breast cancer 5 and yields estimates of an additional parameter, τ i , which, it has been suggested, may reflect important metabolic characteristics of the cells in the tissue imaged. 13 Given the previously highlighted problems, 6 the purpose of this study was 2-fold.…”

Estimation of cellular‐interstitial water exchange in dynamic contrast enhanced MRI using two flip angles

“…This was based on previous studies that showed that the degree of DCE-MRI data sensitization to the water exchange effect depends on the MRI acquisition protocol, including flip angle. 4,17 Such flip angle dependency is not expected to be observed with other contrast kinetic parameters. Hence, using more than one flip angle in DCE-MRI acquisition may introduce a unique effect of τ i on the timeintensity curve and reduce the uncertainty in τ i estimation.…”

“…Since there has not been a study, to our best knowledge, that measured all the parameters considered in our study, it was not possible to choose the baseline values from one study. Instead, the baseline values above were selected based on several previous studies . These baseline parameters were used to generate a range of parameters around them (Table ) for the numerical simulation studies.…”

“…When a bolus injection of GBCA is used, the degree of water exchange effect can vary continuously during the acquisition depending on the tissue microvascular properties as well as the MRI acquisition protocols. 17,18 Contrast kinetic models combined with the Bloch-McConnell exchange model can be used to estimate the water exchange rate (typically in terms of compartmental water lifetimes and volume fractions assuming that the water concentration is constant in all compartments) along with other contrast kinetic model parameters. 18 Under the assumption that the GBCA concentration in the interstitial space does not reach too high, an approximation of the full water exchange model using only the long relaxation time component of the full model, typically known as the shutter speed model (SSM), has been proposed to estimate intracellular water lifetime (τ i ).…”

“…For instance, the improvement of goodness-of-fit by including τ i could also be achieved by a more advanced modeling of vascular compartment contribution. 17,23 In order to utilize the full potential of τ i as a marker for cell viability and metabolism, the uncertainty in τ i estimation needs to be reduced.…”