Cell membrane water exchange effects in prostate DCE-MRI

Abstract

Prostate Dynamic-Contrast-Enhanced (DCE) MRI often exhibits fast and extensive global contrast reagent (CR) extravasation -measured by K trans , a pharmacokinetic parameter proportional to its rate. This implies that the CR concentration ( [CR]) is high in the extracellular, extravascular space (EES) during a large portion of the DCE-MRI study. Since [CR] is detected indirectly, through water proton signal change, the effects of equilibrium transcytolemmal water exchange may be significant in the data and thus should be admitted in DCE-MRI pharmacokinetic modeling. The implications for parameter values were investigated through simulations, and analyses of actual prostate data, with different models. Model parameter correlation and precision were also explored. A near-optimal version of the exchange-sensitized model was found. Our results indicate that ΔK trans (the K trans difference returned by this version and a model assuming exchange to be effectively infinitely fast) may be a very useful biomarker for discriminating malignant from benign prostate tissue. Using an exchange-sensitized model, we find that the mean intracellular water lifetime (τ i ) -an exchange measure -can be meaningfully mapped for the prostate. Our results show prostate glandular zone differences in τ i values.

Dynamic Contrast-Enhanced MRI–Derived Intracellular Water Lifetime (τi): A Prognostic Marker for Patients with Head and Neck Squamous Cell Carcinomas

“…Similarly, some studies have reported that increased metabolic activity is associated with lower τ i (median=0.16–1.03s) in regions of prostrate, 15, 16 and esophageal cancer 17 compared to normal tissues. In yet another study, 18 lower τ i (0.11±0.02s vs. 0.29±0.53s) was observed from normal liver parenchyma (a metabolically more active region) compared to hepatocellular carcinomas.…”

“…12 The unique strength of τ i lies in the fact that it is less sensitive to AIF scaling variations than K trans 13 indicating that τ i is a more reproducible and reliable marker. The parameter τ i has been used to characterize breast, 14 prostate, 15, 16 esophageal 17 and hepatocellular cancer. 18 A recent study 19 also demonstrated the prognostic utility of τ i in predicting survival in patients with hepatocellular carcinomas.…”

Relative sensitivities of DCE-MRI pharmacokinetic parameters to arterial input function (AIF) scaling

“…In this study we used the fast-exchange-regime (FXR)-allowed shutter-speed paradigm model that takes into account this exchange but assumes a single exponential recovery for the 1 H 2 O signal [5,17,18]. This is expressed in $${\mathrm{R}}_{1\mathrm{normalt}}false(\mathrm{normalt}false)=\frac{1}{2}\left[{\mathrm{R}}_{1\mathrm{normali}}+{\mathrm{r}}_{1\mathrm{normalo}}false[{\text{CR}}_{\mathrm{o}}false]false(\mathrm{normalt}false)+{\mathrm{R}}_{1\mathrm{normalo}0}+{\mathrm{k}}_{\text{io}}+\frac{{\mathrm{k}}_{\text{io}}{\mathrm{v}}_{\mathrm{i}}}{{\mathrm{normalv}}_{\mathrm{normale}}}\right]-\frac{1}{2}{\left\{{\left[{\mathrm{R}}_{1\mathrm{normali}}-{\mathrm{r}}_{1\mathrm{normalo}}false[{\text{CR}}_{\mathrm{o}}false]false(\mathrm{normalt}false)-{\mathrm{R}}_{1\mathrm{normalo}0}+{\mathrm{k}}_{\text{io}}-\frac{{\mathrm{k}}_{\text{io}}{\mathrm{v}}_{\mathrm{i}}}{{\mathrm{normalv}}_{\mathrm{normale}}}\right]}^2+\frac{{(2{\mathrm{normalk}}_{\text{io}})}^2{\mathrm{normalv}}_{\mathrm{normali}}}{{\mathrm{v}}_{\mathrm{e}}}\right\}}^{½}$$ where R 1i and R 1o0 are, respectively, the longitudinal relaxation rate constants for the intra- and extracellular 1 H 2 O signals (in the absence of exchange and CR); r 1o is the interstitial CR relaxivity; k io is the unidirectional equilibrium cellular water efflux rate constant (reciprocal of the mean intracellular water lifetime, τ i ); and v i and v e are the respective intra- and extracellular volume fractions.…”

“…This is usually an incorrect over-simplification. The direct consequences of using tracer paradigm (TP) models are systematic errors in the estimated pharmacokinetic parameters such as K trans [example below] and v e [1–3,5–7]. Nevertheless, since tracer models find common use in the literature because of their relative simplicity, we also used one, the standard, fast-exchange-limit (FXL)-constrained Tofts tracer model [22], in addition to the FXR-SSP model in assessing the effects of AIF scaling uncertainty.…”

“…However, the accuracy of AIF derived from DCE-MRI data can be influenced by several inherent experimental conditions, such as: 1) partial volume effect (signal from the image voxel, ideally containing only blood, is contaminated with signal from other non-blood tissues); 2) in-flow effect (signals generated by water molecules in the blood that were originally outside of the imaging volume flow in during signal acquisition and impact the final “blood” signal detected); 3) flow-profile dependency (when vessel cross-section contains more than one image voxel, measured voxel blood CR concentration is flow-profile dependent); and 4) no visible vessel in the imaging field-of-view (FOV). These factors can cause errors in the AIF amplitudes computed directly from blood vessel signals, and a scaling factor may be applied to reduce the errors [5,6]. Alternative methods mitigating the challenges of AIF quantification from blood signal have been proposed.…”