Plump, ChristinaChristinaPlumpHoinkiss, Daniel C.Daniel C.HoinkissHuber, JörnJörnHuberBerger, Bernhard JohannesBernhard JohannesBergerGünther, MatthiasMatthiasGüntherLüth, ChristophChristophLüthDrechsler, RolfRolfDrechsler2024-09-092024-09-092024-08-08IEEE Congress on Evolutionary Computation, CEC 20249798350308365https://hdl.handle.net/11420/48986Magnetic Resonance Imaging (MRI) is an essential tool for medical diagnosis. At the same time, its usage requires profound expert knowledge to determine the ideal MR sequence and protocol to be run. Until now, the contrast and quality of the resulting image have relied mainly on the radiologist's expertise. When confronted with clinical requirements and patient information, the radiologist chooses suitable sequence protocols for the examination. We propose a workflow that supports medical personnel in finding the optimal sequence for a given diagnostic task. To that end, we combine evolutionary algorithms for the optimisation, machine learning techniques for training a surrogate optimisation function from simulated MRI data, and domain-specific languages to allow non-programmers to formulate their requirements and constraints semi-formally. In this paper, we focus on the efficient usage of real-world application-motivated adaptions of the used evolutionary algorithm and evaluate their effects on four real-life sequence examples. We show that it is essential to use an adaption for the surrogate model to obtain realistic solutions and use correlation information about the search space to stay in feasible areas of the search space and thus improve optimisation quality. These findings are a first step in automating the entire MRI-sequence optimisation flow, which is necessary to allow a more widespread usage of this essential medical diagnostic technique.enConstraint HandlingDomain-Specifc LanguageEvolutionary AlgorithmsMedical ApplicationSurrogate ModelMLE@TUHHComputer Science, Information and General Works::003: Systems Theory::003.5: Communication and ControlTechnology::621: Applied Physics::621.3: Electrical Engineering, Electronic EngineeringConference Paper10.1109/CEC60901.2024.10612198Conference Paper