Spatiotemporal Imaging with Partially Separable Functions
Spatiotemporal imaging has a wide range of applications 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.
2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging