Direct reconstruction of spiral MRI using least squares quantization table

Dong Liang, Edmund Y. Lam, George S.K. Fung

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

Abstract

The least squares quantization table (LSQT) method is proposed to accelerate the direct Fourier transform for reconstructing images from nonuniformly sampled data, similar to the look-up table (LUT) and equal-phase-line (EPL) methods published recently. First, it classifies all the image pixels into several groups using the Lloyd-Max quantization scheme, and stores the representative value of each group in a small-size LSQT in advance. For each k-space data, the contribution is calculated only once for each group. Then, each image pixel is mapped into the nearest group and uses its representative value. The experiments show that the LSQT method requires far smaller memory size than the LUT method. Moreover, it is superior to the EPL and Kaiser-Bessel gridding methods in minimizing reconstruction error and requires fewer complex multiplications than the LUT and EPL methods. Additionally, the inherent parallel structure makes the LSQT method easily adaptable to a multiprocessor system.

Original languageEnglish (US)
Title of host publication2007 4th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro - Proceedings
Pages105-108
Number of pages4
DOIs
StatePublished - 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
CountryUnited States
CityArlington, VA
Period4/12/074/15/07

Keywords

  • Image reconstruction
  • Least squares quantization table
  • Lloyd-Max quantization
  • Spiral MRI

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Medicine(all)

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