Spiral MRI reconstruction using least square quantization table

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

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

Abstract

Recently, the authors introduced least square quantization table (LSQT) method to accelerate the direct Fourier transform to reconstruct magnetic resonance images acquired using a spiral trajectory. In this paper, we will discuss the LSQT further in its adaptability, reusability and choice of the number of groups. The experimental results show that the LSQT method has better adaptability for the different reconstruction cases than the equal phase line (EPL) and Kaiser-Bessel gridding methods. Additionally, it can be reused for reconstructing different images of varied sizes.

Original languageEnglish (US)
Title of host publicationMedical Imaging and Informatics - 2nd International Conference, MIMI 2007, Revised Selected Papers
Pages287-293
Number of pages7
DOIs
StatePublished - Jul 1 2008
Event2nd International Conference on Medical Imaging and Informatics, MIMI 2007 - Beijing, China
Duration: Aug 14 2007Aug 16 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4987 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Conference on Medical Imaging and Informatics, MIMI 2007
CountryChina
CityBeijing
Period8/14/078/16/07

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Keywords

  • Adaptability
  • Image reconstruction
  • Least square quantization table
  • Reusability
  • Spiral trajectory

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Liang, D., Lam, E. Y., Fung, G. S. K., & Zhang, X. (2008). Spiral MRI reconstruction using least square quantization table. In Medical Imaging and Informatics - 2nd International Conference, MIMI 2007, Revised Selected Papers (pp. 287-293). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4987 LNCS). https://doi.org/10.1007/978-3-540-79490-5_35