Fast algorithms for GS-model-based image reconstruction in data-sharing Fourier imaging

Zhi Pei Liang, Bruno Madore, Gary H. Glover, Norbert J. Pelc

Research output: Contribution to journalArticlepeer-review

Abstract

Many imaging experiments involve acquiring a time series of images. To improve imaging speed, several "data-sharing" methods have been proposed, which collect one (or a few) high-resolution reference(s) and a sequence of reduced data sets. In image reconstruction, two methods, known as "Keyhole" and reduced-encoding imaging by generalized-series reconstruction (RIGR), have been used. Keyhole fills in the unmeasured high-frequency data simply with those from the reference data set(s), whereas RIGR recovers the unmeasured data using a generalized series (GS) model, of which the basis functions are constructed based on the reference image(s). This correspondence presents a fast algorithm (and two extensions) for GS-based image reconstruction. The proposed algorithms have the same computational complexity as the Keyhole algorithm, but are more capable of capturing high-resolution dynamic signal changes.

Original languageEnglish (US)
Pages (from-to)1026-1030
Number of pages5
JournalIEEE transactions on medical imaging
Volume22
Issue number8
DOIs
StatePublished - Aug 1 2003

Keywords

  • Data-sharing imaging
  • Dynamic imaging
  • Fast algorithm
  • Generalized series (GS)

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology
  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computational Theory and Mathematics

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