Improved diffusion imaging through SNR-enhancing joint reconstruction

Justin P. Haldar, Van J. Wedeen, Marzieh Nezamzadeh, Guangping Dai, Michael W. Weiner, Norbert Schuff, Zhi Pei Liang

Research output: Contribution to journalArticlepeer-review

Abstract

Quantitative diffusion imaging is a powerful technique for the characterization of complex tissue microarchitecture. However, long acquisition times and limited signal-to-noise ratio represent significant hurdles for many in vivo applications. This article presents a new approach to reduce noise while largely maintaining resolution in diffusion weighted images, using a statistical reconstruction method that takes advantage of the high level of structural correlation observed in typical datasets. Compared to existing denoising methods, the proposed method performs reconstruction directly from the measured complex k-space data, allowing for Gaussian noise modeling and theoretical characterizations of the resolution and signal-to-noise ratio of the reconstructed images. In addition, the proposed method is compatible with many different models of the diffusion signal (e.g., diffusion tensor modeling and q-space modeling). The joint reconstruction method can provide significant improvements in signal-to-noise ratio relative to conventional reconstruction techniques, with a relatively minor corresponding loss in image resolution. Results are shown in the context of diffusion spectrum imaging tractography and diffusion tensor imaging, illustrating the potential of this signal-to-noise ratio-enhancing joint reconstruction approach for a range of different diffusion imaging experiments. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

Original languageEnglish (US)
Pages (from-to)277-289
Number of pages13
JournalMagnetic Resonance in Medicine
Volume69
Issue number1
DOIs
StatePublished - Jan 2013

Keywords

  • denoising
  • diffusion imaging
  • feature preservation
  • statistical reconstruction

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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