Dynamic Contrast Enhanced MRI is an important technique to assess the pharmacokinetic properties of tissues. This thesis addresses two major steps necessary for quantitative DCE-MRI: the estimation of the tissue’s T1-time and local B1-field strength, and the estimation of the time-dependent concentration of contrast agent in the blood supply to the tissue of interest. In quantitative pharmacokinetic analysis, the perfusion and vascularization of tissues are estimated by measuring the response to an intravenous injection of contrast agent. This analysis relies on knowledge of the concentrations of contrast agent in both the tissue and in the blood perfusing the tissue. The contrast agent affects the T1 relaxation time of the tissue, and if the T1-time of a tissue is known, the concentration profile can be computed. However, local B1-inhomogeneities can affect the MRI signal strength, complicating the measurement of T1 using conventional methods. Furthermore, the inflow of fresh blood into the field of view causes an additional, location dependent signal enhancement in the blood, which makes a direct measurement of the T1-time (and thus the concentration) in blood impossible. This thesis introduces a new method to estimate a T1-map of tissues in the presence of B1-inhomogeneities. We do this by combining two MRI scans that can each be acquired within breath-holds: one that yields a precise T1-map, though biased by the inhomogeneous B1-field; and one that delivers an unbiased, but imprecise estimate. Combining the information of these two scans yields an estimate of the B1-field, which is then used to correct the T1-map. We validate our method in a phantom study, and in an in vivo study. We found that the proposed method successfully merges the high resolution of the first method with the insensitivity to B1-inhomogeneities of the second. This thesis also introduces a new method to estimate the time-dependent concentration of contrast agent in blood (i.e., the arterial input function (AIF)), which is affected by signal enhancement due to the inflow effect. We do this by first estimating the number of RF-pulses by incorporating knowledge about the average AIF in a population. We then use the number of pulses to re-estimate the concentration from the measured MRI signal, thereby correcting for the inflow effect. We validate our method by means of Monte Carlo simulations and with a controlled flow phantom experiment. We then apply our method to two patient datasets, and use the estimated arterial input function for pharmacokinetic modelling. The first dataset consisted of patients with spine related injuries, and was acquired under a variety of scan settings to assess the method’s robustness. The second dataset consisted of patients with Crohn’s Disease which had a clinically relevant CDEIS score available. In both datasets, we found that our method yields realistic pharmacokinetic model parameters. Instead, estimating the AIF from a distally placed region of interest, as is often done in literature, led to large variation and unrealistic parameters. Furthermore, in the Crohn’s patients we found a better correlation between the estimated pharmacokinetic parameter Ktrans and the CDEIS score, compared to traditional methods. Though the rationale for developing these methods were the presence of B1- inhomogeneities, and pronounced inflow effects in the aorta, other applications of pharmacokinetic modelling (e.g., in other parts of the body) may benefit from our methods, since they are generally applicable.
|Award date||21 Dec 2016|
|Publication status||Published - 2016|