Iterative multiple reference tissue method for estimating pharmacokinetic parameters on prostate DCE MRI

Pharmacokinetic (PK) parameters are probes of tissue status that can be assessed by analysis of dynamic contrast-enhanced (DCE) MRI and are useful for prostate cancer (CaP) detection and grading. Traditionally, PK analysis requires knowledge of the time-resolved concentration of the contrast agent in the blood plasma, the arterial input function (AIF), which is typically estimated in an artery in the field-of-view (FOV). In cases when no suitable artery is present in the FOV, the multiple reference tissue method (MRTM) enables the estimation of PK parameters without the AIF by leveraging PK parameter values from the literature for a reference tissue in the FOV. Nevertheless, PK parameters estimated in the prostate vary significantly between patients. Consequently, population-based values obtained from the literature may introduce error into PK parameter estimation via MRTM. The objectives of this paper are two-fold. First we present a novel scheme, iterative MRTM (IMRTM), to estimate PK parameter values in the absence of the AIF without making assumptions about the PK constants associated with a reference tissue. Then, using IMRTM we investigate differences in PK constants between CaP in the peripheral zone (PZ) and CaP in the central gland (CG), as CG and PZ CaP have previously been shown to differ significantly in terms of both texture and prognosis. We apply IMRTM to 15 patients with CaP in either the CG or the PZ who were scheduled for a radical prostatectomy and a pre-operative MRI. Values for the PK parameters K-trans and v(e) estimated via IMRTM average 0.29 and 0.60 for normal central gland (CG), 0.29 and 0.64 for normal peripheral zone (PZ), and 0.30 and 0.53 for CaP. It is noteworthy that PK constants estimated in PZ CaP are significantly higher than those estimated in CG CaP (p < 0:05). While both MRTM and IMRTM provide PK parameter values that are biologically feasible, IMRTM has the advantage that it invokes patient-specific information rather than relying on population-based PK constants in performing PK analysis.