QUANTITATIVE MAPPING IN MAGNETIC RESONANCE IMAGING
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Magnetic Resonance Imaging (MRI) produces superior soft tissue contrast that is mostly determined by the tissue relaxation times (T1 and T2) and spin density (PD). This dissertation introduces novel methods to quantify T1, T2 and PD, and explored their value for disease classification, and tracking delivery of cell therapies. First, a novel T2 measurement (Dual-τ) method that employs adiabatic pulses is proposed, that exploits the property that the spins undergo T2 decay during excitation by long adiabatic pulses. The new method is relatively immune to MR static and excitation field inhomogeneity, and has a higher efficiency than the conventional methods. The adiabatic excitation pulse can also serve as a preparation pulse that introduces T2 contrast into the MRI, and can be combined with T1 quantification methods to produce T1 and T2 simultaneously. The method is shown to be most accurate at short T2s. The T2 measurements were validated in phantoms and in vivo in human studies. Second, three methods of mapping T1, T2, and PD simultaneously with the least possible number of acquisitions are presented, also utilizing adiabatic pulses. The first, Dual-τ-Dual-FA method, encodes T1 by varying excitation flip-angle (FA). The second, Dual-τ-Dual-TR method, encodes T1 using the variations in the sequence repetition time (TR). The third method incorporates the FA self-correction to eliminate T1 errors caused by field inhomogeneities, and is called the Four-FA method. All three methods were validated in phantom studies, and the Dual-τ-Dual-FA and Four-FA methods were validated in human brain studies as well. The Four-FA method is demonstrated to have the best overall accuracy compared to the existing methods, such as DESPOT1/2, IR TrueFISP, etc. Combining the multi-parametric mapping methods with intravascular (IV) MRI potentially offers a means of reducing the scan time and increasing the local SNR. For the first time, multi-parametric high-resolution (<200μm) T1, T2, PD and fat images of human vessels are obtained. These maps were used to train a machine-learning based classifier to automatically distinguish early- and advanced-stage vessel disease from healthy and smooth muscle. This application enables differentiation of vessel wall disease types with high sensitivity and specificity compared with histology as the standard. The contrast of cells delivered as therapeutic agents in MRI can be enhanced using capsules impregnated with MRI-sensitive contrast agents. At the end of the dissertation, we explore quantitative cell tracking using 19F-labeled capsules that provide dual modality contrast for both computed tomography (CT) and MRI. The method was validated in rabbit diseased models using clinical imaging systems. Compared with CT, 19F MRI was able to accurately track cells non-invasively in vivo, without the use of ionizing radiation. Two weeks after the cell administration, no significant changes in the volume or concentration of the capsules were observed, and the cells preserved high viability according to histology.