Denoising MRI using spectral subtraction

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

Research output: Contribution to journalArticle

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

Fingerprint

Magnetic resonance
Signal to noise ratio
Imaging techniques
Image resolution
Statistics
Brain
Computer simulation

Keywords

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

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

Denoising MRI using spectral subtraction. / Arcan Erturk, M.; Bottomley, Paul A; El-Sharkawy, Abdel Monem M.

In: IEEE Transactions on Biomedical Engineering, Vol. 60, No. 6, 6409421, 2013, p. 1556-1562.

Research output: Contribution to journalArticle

Arcan Erturk, M. ; Bottomley, Paul A ; El-Sharkawy, Abdel Monem M. / Denoising MRI using spectral subtraction. In: IEEE Transactions on Biomedical Engineering. 2013 ; Vol. 60, No. 6. pp. 1556-1562.
@article{3968f12f88b845889e60ba8358992f7c,
title = "Denoising MRI using spectral subtraction",
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.",
keywords = "Magnetic resonance imaging (MRI) denoising, parallel imaging, SENSE, spectral subtraction",
author = "{Arcan Erturk}, M. and Bottomley, {Paul A} and El-Sharkawy, {Abdel Monem M}",
year = "2013",
doi = "10.1109/TBME.2013.2239293",
language = "English (US)",
volume = "60",
pages = "1556--1562",
journal = "IEEE Transactions on Biomedical Engineering",
issn = "0018-9294",
publisher = "IEEE Computer Society",
number = "6",

}

TY - JOUR

T1 - Denoising MRI using spectral subtraction

AU - Arcan Erturk, M.

AU - Bottomley, Paul A

AU - El-Sharkawy, Abdel Monem M

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Magnetic resonance imaging (MRI) denoising

KW - parallel imaging

KW - SENSE

KW - spectral subtraction

UR - http://www.scopus.com/inward/record.url?scp=84877879522&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84877879522&partnerID=8YFLogxK

U2 - 10.1109/TBME.2013.2239293

DO - 10.1109/TBME.2013.2239293

M3 - Article

C2 - 23322757

AN - SCOPUS:84877879522

VL - 60

SP - 1556

EP - 1562

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 6

M1 - 6409421

ER -