This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. MRI is basically a Fourier transform-based imaging technique. Although the Fourier reconstruction algorithm is optimal in the minimum-norm, least-squares sense, it suffers from a number of practical problems, most notably, limited resolution and Gibbs ringing artifact, when the number of encodings measured is small. These problems have limited the speed, efficiency, and quantitative accuracy of MRI. Although these problems are tolerable to a large extent in conventional anatomical imaging, they have become an important obstacle for functional and metabolic imaging. Over the past decade, the investigators of the Image Reconstruction Core have made great effort to address the image reconstruction problem from different angles, resulting in several very promising ideas and techniques that can use prior (or side) information effectively to compensate for the lack of sufficient measured imaging data, thus giving rise to much higher resolution and imaging speeds than the Fourier transform-based counterparts do. Subproject 1 of the Image reconstruction Core aims to provide an effective method to exploit spatiotemporal correlations of (k, t)-space signals for sparse sampling (thus reducing imaging time). Project 1 (Generalized Series Reconstruction from Spatiotemporal Imaging with Sparsely Sampled (k, t)-Space Data) has three specific aims:
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.
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