Spatiotemporal imaging with partially separable functions

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2007 4th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro - Proceedings
Pages988-991
Number of pages4
DOIs
StatePublished - Nov 27 2007
Event2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 - Arlington, VA, United States
Duration: Apr 12 2007Apr 15 2007

Publication series

Name2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings

Other

Other2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07
Country/TerritoryUnited States
CityArlington, VA
Period4/12/074/15/07

Keywords

  • Hubert spaces
  • Separable functions
  • Spatiotemporal modeling
  • Tensor-product spaces

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Medicine(all)

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