Iterative image reconstruction for PROPELLER-MRI using the nonuniform fast fourier transform

Ashish A. Tamhane, Mark A. Anastasio, Minzhi Gui, Konstantinos Arfanakis

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: To investigate an iterative image reconstruction algorithm using the nonuniform fast Fourier transform (NUFFT) for PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction) MRI. Materials and Methods: Numerical simulations, as well as experiments on a phantom and a healthy human subject were used to evaluate the performance of the iterative image reconstruction algorithm for PROPELLER, and compare it with that of conventional gridding. The tradeoff between spatial resolution, signal to noise ratio, and image artifacts, was investigated for different values of the regularization parameter. The performance of the iterative image reconstruction algorithm in the presence of motion was also evaluated. Results: It was demonstrated that, for a certain range of values of the regularization parameter, iterative reconstruction produced images with significantly increased signal to noise ratio, reduced artifacts, for similar spatial resolution, compared with gridding. Furthermore, the ability to reduce the effects of motion in PROPELLER-MRI was maintained when using the iterative reconstruction approach. Conclusion: An iterative image reconstruction technique based on the NUFFT was investigated for PROPELLER MRI. For a certain range of values of the regularization parameter, the new reconstruction technique may provide PROPELLER images with improved image quality compared with conventional gridding.

Original languageEnglish (US)
Pages (from-to)211-217
Number of pages7
JournalJournal of Magnetic Resonance Imaging
Volume32
Issue number1
DOIs
StatePublished - Jul 2010
Externally publishedYes

Keywords

  • Gridding
  • Iterative
  • NUFFT
  • PROPELLER
  • Reconstruction
  • Turboprop

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • General Medicine

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