Spatiotemporal imaging with partially separable functions

Spatiotemporal imaging, including both dynamic imaging and spectroscopic imaging, has a wide range of applications from functional neuroimaging, cardiac imaging to metabolic cancer imaging. A practical challenge lies in obtaining high spatiotemporal resolution because the amount of data required increases exponentially as the physical dimension increases (curse of dimensionality). This paper describes a new way for spatiotemporal imaging using partially separable functions. This model admits highly sparse sampling of the data space, providing an effective way to achieve high spatiotemporal resolution. Practical imaging data will also be presented to demonstrate the performance of the new method.