This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.The class of autocalibrating data-driven parallel imaging methods has gained attention in recent years due to its ability to achieve high quality reconstructions even in challenging imaging applications. A variety of different data-driven reconstruction methods have been proposed to date. The purpose of this work was to perform a comprehensive analysis of these reconstruction methods to evaluate their relative merits and tradeoffs and ultimately to identify the best method for a given imaging application.
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