In Magnetic Resonance Imaging, the time required to generate an image is proportional to the number of steps used to encode the spatial information. In rapid imaging, an array of coil elements and receivers are used to reduce the number of encoding steps required to generate an image. This is done using knowledge of the spatial sensitivity of the array and receiver channels. Recently, these arrays have begun to include a large number of coil elements. Ideally, each coil element would have its own receiver channel to acquire the image data. In practice, this is not always possible due to economic or other constraints. In this dissertation, methods are explored to combine a large array to a limited number of receivers so as to optimize the performance for parallel imaging; this dissertation focuses on SENSE in particular. Simple combinations that represent larger coils that might be constructed are discussed. More complex solutions form current sheets. One solution uses Roemer'A?A?A?A?s method to optimize image SNR at a set of points. In this dissertation, Roemer's solution is generalized to give the weighting coefficients that optimize SNR over regions. Also, solutions fitted to ideal profiles that minimize noise amplification are shown. These fitted profiles can allow the SENSE algorithm to function at optimal reduction factors. Finally, a description of how to build the combiner in hardware is discussed.
