Spatiotemporal imaging has a wide range ofapplications from functional neuroimaging, cardiac imaging to metabolic cancer imaging. A long-standing practical problem lies in obtaining high spatiotemporal resolution because the amount of data required increases exponentially as the physical dimension increases. This paper describes a new way for spatiotemporal imaging using partially separable functions. This model admits highly sparse sampling of the data space, providing a novel, effective way to achieve high spatiotemporal resolution. Practical imaging data will also be presented to demonstrate the performance of the new method.
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