Quantification of the longitudinal relaxation time for in vivo MRI : influence of magnetisation transfer (2012)

MRI contrast in vivo is influenced by biophysical and biochemical mechanisms, in particular by the exchange of the signal - creating protons between chemically different environments, termed magnetisation transfer (MT). These effects, though ubiquitous, are difficult to characterise due to their intrinsic complexity and strong dependence on sequence parameters and design. Quantifying the longitudinal relaxation time, T1, has been of particular interest in the realm of MRI. One motivating aspect behind this thesis was the aim of investigating the relationship between T1 and MT. Thus, a careful and thorough investigation of the behaviour of T1 quantification in the presence of MT was performed. A simulation framework for MRI was extended to consider MT. Simulations were complemented and confirmed by phantom and in vivo measurements. The framework was then employed to assess issues of T1 quantification in the presence of MT, which are not amenable to measurement. Tissue parameters influencing the observed longitudinal relaxation time were assessed based on the established two-pool model for MT. It was shown that estimation of the recovery time depends on almost all tissue parameters, e.g. the exchange rates, the pool sizes and the longitudinal relaxation times of the single pools. In the presence of MT, the observed T1 values are generally shorter than the largest T1 value of the single pools. This shortening arises from the exchange process. Common sequences used for T1 estimations and changes in their signal equations arising from MT were derived analytically and calculated. The results were validated by simulations as well as by phantom measurements. The consequences of neglecting MT effects on the estimated T1 values were investigated. Three types of sequence were investigated with and without the MT effect, namely inversion recovery (IR), T1 quantification by means of the Ernst equation, and Look-Locker type sequences. The simulations showed that IR-based methods give T1 estimations with high accuracy (better than 2%) and precision (better than 5%), even in the presence of MT. However, this high accuracy can only be achieved if MT is considered by neglecting very short inversion times. Techniques employing the Ernst equation are usually less accurate and show worse precision than the inversion recovery methods, especially if only few sample points are considered in the former. Finally, the Look-Locker based method showed a strong dependence (in the presence of MT effects) of the estimated T1 values on the sequence parameters used. It was demonstrated that a controlled variation of the inversion time gives rise to a novel method of MT quantification. Although this new method, the TAPIR TI approach, does not quantify all MT sample parameters, it offers the great advantage of fitting only three parameters to reveal some MT properties of the sample. The simulation results were successfully confirmed by in vivo measurements on 10 volunteers and thus the experimental feasibility of this method was proven. The final part of this work investigates possible applications of T1 quantification. An existing method for estimating the permeability of the blood-brain barrier, based on the Patlak model, was improved and a comparison with FET- PET measurements on tumour patients was obtained. A new approach to detect the linear part of a Patlak plot was developed and successfully implemented. It was demonstrated that the Patlak results give additional information about the tumour blood volume and the blood-brain barrier permeability.