The experimental autoimmune encephalomyelitis (EAE) is used to study multiple sclerosis (MS) pathology and develop novel technologies to quantify inflammation over time. Magnetic resonance imaging (MRI) with gadolinium-based contrast agents (GBCAs) is the state-of-the-art method to assess inflammation in MS patients and its animal model. Fluorine (19F)-MRI is one novel technology to quantify inflammatory immune cells in vivo using 19F-nanoparticles. T1 mapping of contrast-enhancing images is another method that could be implemented to quantify inflammatory lesions. Transient macroscopic changes in the EAE brain confound quantification and necessitate registration methods to spatially align images in longitudinal studies. For 19F-MRI, an additional challenge is the low signal-to-noise ratio (SNR) due to low number of 19F-labeled immune cells in vivo. Transceive surface radiofrequency (RF) probes and SNR-efficient imaging techniques such as RARE (Rapid Acquisition with Relaxation Enhancement) are combined to increase sensitivity in 19F-MRI. However, the strong spatially-varying RF field (B1 inhomogeneity) of transceive surface RF probes further hampers quantification. Retrospective B1 correction methods typically use signal intensity equations, unavailable for complex acquisition methods like RARE. The main goal of this work is to investigate novel B1 correction and registration methods to enable the study of inflammatory diseases using 1H- and 19F-MRI following GBCA and 19F-nanoparticle administration, respectively. For correcting B1 inhomogeneities in 1H- and 19F-MR transceive surface RF probes, a model-based method was developed using empirical measurements and simulations, and then validated and compared with a sensitivity method and a hybrid of both. For 19F-MRI, a workflow to measure anatomical images in vivo and a method to compute 19F-concentration uncertainty after correction using Monte Carlo simulations were developed. To overcome the challenges of EAE brain macroscopic changes, a pipeline for registering images throughout longitudinal studies was developed. The proposed B1 correction methods demonstrated dramatic improvements in signal quantification and T1 contrast on images of test phantoms and mouse brains, allowing quantitative measurement with transceive surface RF probes. For low-SNR scenarios, the model-based method yielded reliable 19F-quantifications when compared to volume resonators. Uncertainty after correction depended linearly on the SNR (≤10% with SNR≥10.1, ≤25% when SNR≥4.25). The implemented registration approach provided successful image alignment despite substantial morphological changes in the EAE brain over time. Consequently, T1 mapping was shown to objectively quantify gadolinium lesion burden as a measure of inflammatory activity in EAE. The 1H- and 19F-MRI methods proposed here are highly relevant for quantitative MR of neuroinflammatory diseases, enabling future (pre)clinical investigations.