In this thesis, the development of a novel magnetic resonance imaging (MRI) pulse sequence based on magnetic resonance fingerprinting (MRF) is presented. The proposed technique, termed "Flow-MRF", allows time-resolved velocities and relaxation constants to be quantified simultaneously, in shorter acquisition times than conventional MR-based velocimetry. The simultaneous quantification of both sets of parameters was achieved by formulating the combined problem in the MRF framework. An MRF

more »

... was designed to create minimal coupling between the relaxometric and velocimetric parameter encoding. Flow-MRF was validated and tested in simulations, phantom experiments, and an in vivo study targeting the popliteal artery and the gastrocnemius muscle. In each investigation, Flow-MRF quantified relaxation constants and flow velocities in strong agreement with literature and reference measurements. Furthermore, the use of high velocity encoding moments (∆m 1 = 60 mT/m · ms 2 ) was demonstrated while maintaining a range of correctly quantifiable velocities beyond 800 cm/s. In the volunteer study, Flow-MRF determined an average longitudinal relaxation time of (1384 ± 75) ms and a transverse relaxation time of (26 ± 4) ms in the gastrocnemius muscle. The average velocity deviation over all three volunteers between Flow-MRF and the reference was (−2.6 ± 5.2) cm/s. Lastly, the potential to quantify the complete Reynolds stress tensor with Flow-MRF was investigated and shown in a stenotic flow phantom experiment. Flow-MRF presents a novel method of quantifying velocities in up to fourfold shorter measurement times than conventional velocity mapping techniques, while simultaneously providing relaxometric maps of static tissue. These improvements can potentially be helpful in the assessment of pathologies such as arteriosclerosis. Über die simultane Quantifizierung von Strömungsgeschwindigkeiten und Relaxationszeiten mit Hilfe von Magnetic Resonance Fingerprinting In dieser Arbeit wird die Entwicklung einer neuen magnetresonanztomographischen Bildgebungssequenz basierend auf Magnetic Resonance Fingerprinting (MRF) vorgestellt. Die entwickelte Technik namens "Flow-MRF" erlaubt die simultane Quantifizierung von zeitaufgelösten Strömungsgeschwindigkeiten und Relaxationszeiten in verkürzter Messdauer verglichen zu konventioneller, MR-basierter Geschwindigkeitsquantifizierung. Durch die Formulierung des kombinierten Quantifizierungsproblems im Rahmen des MRF-Konzepts wurde die gleichzeitige Bestimmung beider Parameter ermöglicht. Dafür wurde ein MRF-Muster entworfen, welches eine minimale Kopplung zwischen der Kodierung der Geschwindigkeiten und den relaxometrischen Parametern verursacht. Flow-MRF wurde in Simulationen, Phantom-Experimenten und in einer In Vivo Studie, zur Untersuchung der Kniekehlarterie und des zweibäuchigen Wadenmuskels, getestet. Alle Untersuchungen zeigen eine hohe Übereinstimmung der mit Flow-MRF quantifizierten Geschwindigkeiten und Relaxationszeiten mit Literaturwerten und Referenzmessungen. Des Weiteren wurde die Kodierung der Geschwindigkeit mit hohen Momenten (∆m 1 = 60 mT/m · ms 2 ) demonstriert und der Bereich der korrekt quantifizierbaren Geschwindigkeiten auf über 800 cm/s bestimmt. In der Probandenstudie wurde im Wadenmuskel eine longitudinale Relaxationszeit von (1384 ± 75) ms und eine transversale Relaxationszeit von (26 ± 4) ms bestimmt. Die mittlere Abweichung der bestimmten Geschwindigkeiten über alle Probanden zwischen Flow-MRF und der Referenz beträgt (−2.6 ± 5.2) cm/s. Schließlich wurde die Möglichkeit, mit Flow-MRF den vollständigen Reynoldsschen Spannungstensors zu quantifizieren, in einer Messung mit stenotischen Fließbedingungen untersucht und gezeigt. Flow-MRF stellt eine neue Methode zur simultanen Quantifizierung von Strömungsgeschwindigkeiten dar und ermöglicht dabei eine bis vierfach verkürzte Messdauer als konventionelle Methoden. Zusätzlich werden bei Flow-MRF die Relaxationszeiten von statischem Gewebe bestimmt. Diese Neuerungen sind potenziell hilfreich bei der Untersuchung von Pathologien wie Arteriosklerose. Introduction Medical imaging has become a cornerstone of medical diagnostics in the last decades. Among all medical imaging modalities, MRI holds a unique list of benefits: an excellent soft tissue contrast, insight into physiological processes, and highly resolved spatiotemporal morphology, to name a few, while avoiding any exposure to ionizing radiation. These characteristics qualify MRI based diagnostics as the medical imaging modality of choice in many clinical questions. The majority of the clinical MR images, however, are qualitative in their contrast, unlike many other imaging modalities. No quantitative value can be assigned to each voxel in the final image, despite the inherently quantitative amplitude of the MR signal, due to multitude of scaling factors during the acquisition, digitization, and reconstruction. The qualitative or weighted images do not represent a prohibitive drawback in current radiology because the diagnosis is based on the expertise and knowledge of highly trained physicians capable of coping with varying contrast and signal levels. The steadily increasing number of MRI examinations over the last decade in Germany (185 %-fold increase) [1], however, might indicate that in the future, the radiologists might need to be assisted by automated diagnostic tools based on machine learning techniques. These techniques thrive on consistent and quantitative input data. Furthermore, ever-larger multi-center studies can strongly benefit from consistent imaging parameters and identical imaging hardware. This, however, is often not feasible due to different coil availability, varying gradient hardware or even different MRI-vendors. Truly quantitative imaging would immediately alleviate all problems of inter-site variability as the fundamental properties of the biological sample are measured. Large databases of normal and abnormal relaxation times, for example, could provide the basis for radiological reporting systems, allowing comprehensible, standardized and objective evaluations of the MRI data. The potential advantages of these reporting systems have been successfully demonstrated by the Prostate Imaging Reporting and Data System (PI-RADS), for example [2] . The quantification of relaxometric constants can provide an optimal image contrast compared to weighted images, and allow synthetic images with pure contrast to be generated. Many fast imaging sequences generate mixed contrasts based on both T 1 and T 2 . Disfavorable combinations of these parameters can result in low contrast as can be seen in fluid-attenuated inversion recovery (FLAIR) sequences at 7 T [3].