In this study, three different MR pulse sequences, IR-FSE, MP2RAGE, and firstly proposed MP3RAGE, were applied to obtain high-resolution 3D T1 mapping of whole brain at 1.5 Tesla. Among these three sequences, MP2RAGE uses fast gradient echo as readout module. Signals of two different inversion times are acquired at once and can be used to calculate T1 relaxation time according to Bloch equation. However, the magnetization was also influenced by the excitation efficiency of inversion adiabatic pulse, which was usually estimated by numerical simulation and taken as a constant over the field of view in the literature. However, this might not be true in practice. Therefore, a newly modified pulse sequence, MP3RAGE, was proposed to acquire data of three distinct inversion times without increasing scanning time. As a result, the spatial distribution of T1 and inversion efficiency can be assessed by solving nonlinear least square problem. In addition, the IR-FSE sequence with six inversion times was also applied in every experiment to provide T1 value for reference. Results showed that the T1 estimation obtained by MP2RAGE is close to, but slightly lower than that by IR-FSE, which is in agreement with those reported in literatures. In addition, the 3D high-resolution maps of T1 and efficiency were successfully estimated with the use of MP3RAGE. Spatial smoothing on inversion efficiency helps reducing the sensitivity to noise in the nonlinear approach, leading to T1 values closer to those by IR-FSE.

The signal resulting from the IR-FSE sequence has been thoroughly analyzed in order to improve the accuracy of quantitative T1 mapping of the human brain. Several optimized post-processing algorithms have been studied and compared in terms of their T1 mapping accuracy. The modified multipoint two-parameter fitting method was found to produce less underestimation compared to the traditional multipoint three-parameter fitting method, and therefore, to result in a smaller T1 estimation error. Two correction methods were proposed to reduce the underestimation problem which is commonly seen in IR-FSE sequences used for measuring T1, especially when a large turbo factor is used. The intra-scan linear regression method corrects the systematic error effectively but the RMSE may still increase due to the increase of uncertainty in sequences with large turbo factors. The weighted fitting model corrects not only the systematic error but also the random error and therefore the aggregate RMSE for T1 mapping can be effectively reduced. A new fitting model that uses only three different TI measurements for T1 estimation was proposed. The performance for the three-point fitting method is as good as that of the multipoint fitting method with correction in the phantom simulation. In addition, a new ordering scheme that implements the three-point fitting method is proposed; it is theoretically able to reduce the total scan time by 1/3 compared to the TESO-IRFSE sequence. The performance of the three-point fitting method on the real human brain is also evaluated, and the T1 mapping results are consistent to with the conventional IR-FSE sequence. More samples of true anatomy are needed to thoroughly evaluate the performance of the proposed techniques when applied to T1 mapping of the human brain.