IMAGE RECONSTRUCTION FROM SPARSELY SAMPLED (K, T)-SPACE DATA

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Aim 1: Optimizing a novel (k, t)-space formulation of the generalized series model to allow joint spatiotemporal modeling of the time-varying object function encountered in various spatiotemporal imaging applications of the proposed imaging center (e.g., dynamic perfusion imaging). Aim 2: Development of an efficient image reconstruction algorithm that can handle both conventional and sensitivity-encoded (k, t)-space data collected using a single or multiple phased array coils. Aim 3: Validation of the proposed (k, t)-space imaging method using the methodologies described in the Validation section.