Denoising MRI using spectral subtraction

M. Arcan Erturk, Paul A. Bottomley, Abdel Monem M. El-Sharkawy

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Improving the signal-to-noise-ratio (SNR) of magnetic resonance imaging (MRI) using denoising techniques could enhance their value, provided that signal statistics and image resolution are not compromised. Here, a new denoising method based on spectral subtraction of the measured noise power from each signal acquisition is presented. Spectral subtraction denoising (SSD) assumes no prior knowledge of the acquired signal and does not increase acquisition time. Whereas conventional denoising/filtering methods are compromised in parallel imaging by spatially dependent noise statistics, SSD is performed on signals acquired from each coil separately, prior to reconstruction. Using numerical simulations, we show that SSD can improve SNR by up to ∼45% in MRI reconstructed from both single and array coils, without compromising image resolution. Application of SSD to phantom, human heart, and brain MRI achieved SNR improvements of ∼40% compared to conventional reconstruction. Comparison of SSD with anisotropic diffusion filtering showed comparable SNR enhancement at low-SNR levels (SNR = 5-15) but improved accuracy and retention of structural detail at a reduced computational load.

Original languageEnglish (US)
Article number6409421
Pages (from-to)1556-1562
Number of pages7
JournalIEEE Transactions on Biomedical Engineering
Volume60
Issue number6
DOIs
StatePublished - 2013

Keywords

  • Magnetic resonance imaging (MRI) denoising
  • SENSE
  • parallel imaging
  • spectral subtraction

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