| Magnetic resonance imaging technology is one of the most promising investigative and diagnostic methodologies in brain research and in clinical neurological practice. To acquire informative magnetic resonance signals, a variety of equipment parameters have to be set and combined in complex ways, depending on which neuronal metabolites and brain tissues are of interest and on what types of clinical or research questions are being asked. Due to the complicated interrelations among the available parameters, it is necessary to develop computerized tools that can design appropriate novel settings to control the activities of the magnet and the process of data acquisition. The goal of the present thesis is to provide a constraint satisfaction solution to address the constraints associated with different desired scanning protocols.; The thesis has designed and implemented a generic constraint-resolving engine that accepts text-based constraint specifications. Through a constraint-parsing mechanism, the constraint specifications are interpreted and, driven by the constraints, a constraint-resolving program is implemented automatically. The constraint-resolving program enumerates the value domains of the variables and enforces the constraints associated with each possible setting. A context-free grammar has been defined to guide constraint recognition and interpretation. Effective algorithms and strategies of constraint satisfaction programming are evaluated and applied to reduce search space during the parsing and resolving procedures. The software produces a complete set of all valid parameter settings with which the activities of the magnetic device can be controlled during signal acquisition. |
