Elevated micrometer-scale iron deposits in the brain are a crucial early detection marker for numerous neurodegenerative diseases. Although iron deposits exist below conventional magnetic resonance imaging resolution, sub-voxel information on their spatial properties can be encoded into the MR signal through magnetic susceptibility differences and diffusion effects. Spin-lock pulse sequences have recently emerged as a powerful tool sensitive to diffusion-mediated dephasing, characterized by the time constant T_1ρ. By employing continuous low-frequency radiofrequency pulses, signal dynamics can be sensitized to motions in the order of sub-kilohertz, rendering it sensitive to the effect of diffusion. In this work, the potential of microstructure characterization with T_1ρ was explored through simulation and phantom experiments. A Monte Carlo simulation of a conventional spin-lock pulse showed high sensitivity to microbead radius, concentration, and susceptibility shift through R_1ρ dispersion magnitude and inflection point. Phantom experiments of a balanced and refocused spin-lock pulse demonstrated minimal changes in relaxation rate, suggesting that a considerable susceptibility gradient must be present before signal dynamics are affected. By overcoming current experimental limitations, spin-lock pulse sequences hold great promise as reliable tools for probing structures of micrometer size.

Myocardial perfusion, the blood flow to the heart muscle, can be evaluated by tracing the passage of a contrast agent using cardiac magnetic resonance (CMR) imaging. This technique, well-established for diagnosing coronary artery disease, is limited by the necessity for breath-holding, subjective assessment, and low myocardial coverage. In this thesis, we aim to address these limitations of contrast-enhanced myocardial perfusion CMR. We developed a pulse sequence and post-processing pipeline to quantify myocardial perfusion using free-breathing 3D contrast-enhanced CMR. To restrict volume acquisition to the diastolic phase, characterized by minimal cardiac motion, we employed optimal slice oversampling, maximal partial Fourier acquisition, and cartesian undersampling in spatial and temporal domains. To mitigate the effects of breathing, respiratory tracking and image registration were performed. Collaborations for the reconstruction of raw data utilizing deep learning and image registration were established. Validation in healthy volunteers demonstrates that the developed pulse sequence enables isotropic 3D acquisition (3.6 x 3.6 x 3.6 mm^3) of an arterial input function (AIF) and myocardial signal during each cardiac cycle, up to heart rates of 76 bpm. Obtained AIF images exhibit sufficient resolution for extracting the left ventricular blood pool signal and registered myocardial images are of good quality. We investigated and validated a method to convert signal intensity to T1, which is required for MBF quantification. While T1 estimates from the AIF images approximate reference values up to 500 ms well, underestimation was observed from the myocardial images and for high T1 values.