Name
Abstract | ||
|---|---|---|
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 |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ISBI.2007.357020 | ISBI |
Keywords | Field | DocType |
metabolic cancer imaging,spectroscopic imaging,cardiology,neurophysiology,biomedical optical imaging,data space sampling,sparse matrices,partially separable functions,image resolution,spatiotemporal phenomena,spatiotemporal imaging,dynamic imaging,image sampling,cancer,cardiac imaging,spatiotemporal resolution,brain,functional neuroimaging,sparse sampling,hilbert space,spatial resolution,indexing terms,spectroscopy,sampling methods,tensor product,neuroimaging,curse of dimensionality,application software | Computer vision,Data space,Pattern recognition,Neurophysiology,Computer science,Separable space,Curse of dimensionality,Artificial intelligence,Cardiac imaging,Dynamic imaging,Image resolution,Sparse matrix | Conference |
ISSN | ISBN | Citations |
1945-7928 | 1-4244-0672-2 | 58 |
PageRank | References | Authors |
5.19 | 2 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi-Pei Liang | 1 | 522 | 64.94 |