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.

Magnetic Resonance Imaging (MRI) is an important imaging modality, since it can create high-resolution cross-sectional images of the human body. In MRI scanners, the nuclear spin magnetization is excited using radio-frequency pulses. Images are created based on the time-evolution of this magnetization, which is characterized by relaxation times (T₁,T₂,T_1ρ,…). These relaxation times change from tissue to tissue, and between healthy and diseased tissue.

Rotating frame (T_1ρ) relaxation measurements are a promising technique for assessing slow molecular interactions in tissue. This has applications in articular cartilage imaging, and cardiac imaging without contrast agent injection. T_1ρ measurements require continuous application of an electromagnetic excitation field. Variations of both the main magnetic field and the excitation field strength cause this excitation to be off-resonant. This in turn leads to contrast loss in the final images.

Adiabatic pulses, whose orientation changes slowly in time, are resistant to these off-resonance effects. Their effectiveness is dependent on their parameters, such as the peak sharpness β or the frequency modulation amplitude A. Conventional optimization techniques for these parameters neglect off-resonance effects.

In this project Redfield theory was used to create a pulse optimization algorithm that can take this off-resonance behaviour into account.