Improvement of the SNR and resolution of susceptibility-weighted venography by model-based multi-echo denoising

Ung Jang, Yoonho Nam, Dong Hyun Kim, Dosik Hwang

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

The vein structures of the brain are important for understanding brain function and structure, especially when functional magnetic resonance imaging (fMRI) is utilized, as fMRI is based on changes in the blood-oxygen-level-dependent (BOLD) signal, which is directly related to veins. The aim of the present study was to develop an effective method to produce high signal-to-noise-ratio (SNR) and high-resolution multi-contrast susceptibility-weighted (SW) images of vein structures from 3T magnetic resonance (MR) scanners using multi-gradient-echo MR acquisition and a successive denoising process for both magnitude and phase data. Successive multi-echo MR images were acquired at multiple time points using a multigradient-recalled echo sequence at 3T, and noise in the magnitude and phase data was effectively suppressed using model-based denoising methods. A T2* relaxation model was used to denoise the magnitude data and a linear phase model was used to denoise the phase data. SW venography images were obtained from the denoised MR data and compared with conventional SW venography. To evaluate the performance of our denoising methods, we conducted numerical simulation studies and compared the mean-squared-error (MSE), SNR, and contrast-to-noise ratio (CNR) that we obtained using our procedure with those obtained using conventional denoising methods. In addition, images were inspected visually. Numerical simulations showed that our proposed model-based denoising methods were the most effective at suppressing noise. In vivo experiments also showed a substantial increase in the SNR of the phase mask obtained using the proposed denoising process (twice that of the conventional GRE-based phase mask). The T2* relaxation model method improved the SNR of the magnitude image (1.17-1.35 times that of the GRE-based magnitude image). Noise suppression of both magnitude and phase data using our proposed method resulted in an overall increase in the SNR and CNR in the final SW venography (1.1-1.5-fold and 1.96-fold higher SNR and CNR, respectively, than that of the GRE-based SW venography). We demonstrated that high SNR and high-resolution SW venograms can be obtained using multi-echo gradient-recalled acquisition and successive model-based denoising of both magnitude and phase data.

Original languageEnglish
Pages (from-to)308-316
Number of pages9
JournalNeuroImage
Volume70
DOIs
Publication statusPublished - 2013 Apr 15

Fingerprint

Phlebography
Signal-To-Noise Ratio
Noise
Magnetic Resonance Spectroscopy
Veins
Masks
Magnetic Resonance Imaging
Brain
Linear Models
Oxygen

All Science Journal Classification (ASJC) codes

  • Neurology
  • Cognitive Neuroscience

Cite this

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title = "Improvement of the SNR and resolution of susceptibility-weighted venography by model-based multi-echo denoising",
abstract = "The vein structures of the brain are important for understanding brain function and structure, especially when functional magnetic resonance imaging (fMRI) is utilized, as fMRI is based on changes in the blood-oxygen-level-dependent (BOLD) signal, which is directly related to veins. The aim of the present study was to develop an effective method to produce high signal-to-noise-ratio (SNR) and high-resolution multi-contrast susceptibility-weighted (SW) images of vein structures from 3T magnetic resonance (MR) scanners using multi-gradient-echo MR acquisition and a successive denoising process for both magnitude and phase data. Successive multi-echo MR images were acquired at multiple time points using a multigradient-recalled echo sequence at 3T, and noise in the magnitude and phase data was effectively suppressed using model-based denoising methods. A T2* relaxation model was used to denoise the magnitude data and a linear phase model was used to denoise the phase data. SW venography images were obtained from the denoised MR data and compared with conventional SW venography. To evaluate the performance of our denoising methods, we conducted numerical simulation studies and compared the mean-squared-error (MSE), SNR, and contrast-to-noise ratio (CNR) that we obtained using our procedure with those obtained using conventional denoising methods. In addition, images were inspected visually. Numerical simulations showed that our proposed model-based denoising methods were the most effective at suppressing noise. In vivo experiments also showed a substantial increase in the SNR of the phase mask obtained using the proposed denoising process (twice that of the conventional GRE-based phase mask). The T2* relaxation model method improved the SNR of the magnitude image (1.17-1.35 times that of the GRE-based magnitude image). Noise suppression of both magnitude and phase data using our proposed method resulted in an overall increase in the SNR and CNR in the final SW venography (1.1-1.5-fold and 1.96-fold higher SNR and CNR, respectively, than that of the GRE-based SW venography). We demonstrated that high SNR and high-resolution SW venograms can be obtained using multi-echo gradient-recalled acquisition and successive model-based denoising of both magnitude and phase data.",
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Improvement of the SNR and resolution of susceptibility-weighted venography by model-based multi-echo denoising. / Jang, Ung; Nam, Yoonho; Kim, Dong Hyun; Hwang, Dosik.

In: NeuroImage, Vol. 70, 15.04.2013, p. 308-316.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Improvement of the SNR and resolution of susceptibility-weighted venography by model-based multi-echo denoising

AU - Jang, Ung

AU - Nam, Yoonho

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N2 - The vein structures of the brain are important for understanding brain function and structure, especially when functional magnetic resonance imaging (fMRI) is utilized, as fMRI is based on changes in the blood-oxygen-level-dependent (BOLD) signal, which is directly related to veins. The aim of the present study was to develop an effective method to produce high signal-to-noise-ratio (SNR) and high-resolution multi-contrast susceptibility-weighted (SW) images of vein structures from 3T magnetic resonance (MR) scanners using multi-gradient-echo MR acquisition and a successive denoising process for both magnitude and phase data. Successive multi-echo MR images were acquired at multiple time points using a multigradient-recalled echo sequence at 3T, and noise in the magnitude and phase data was effectively suppressed using model-based denoising methods. A T2* relaxation model was used to denoise the magnitude data and a linear phase model was used to denoise the phase data. SW venography images were obtained from the denoised MR data and compared with conventional SW venography. To evaluate the performance of our denoising methods, we conducted numerical simulation studies and compared the mean-squared-error (MSE), SNR, and contrast-to-noise ratio (CNR) that we obtained using our procedure with those obtained using conventional denoising methods. In addition, images were inspected visually. Numerical simulations showed that our proposed model-based denoising methods were the most effective at suppressing noise. In vivo experiments also showed a substantial increase in the SNR of the phase mask obtained using the proposed denoising process (twice that of the conventional GRE-based phase mask). The T2* relaxation model method improved the SNR of the magnitude image (1.17-1.35 times that of the GRE-based magnitude image). Noise suppression of both magnitude and phase data using our proposed method resulted in an overall increase in the SNR and CNR in the final SW venography (1.1-1.5-fold and 1.96-fold higher SNR and CNR, respectively, than that of the GRE-based SW venography). We demonstrated that high SNR and high-resolution SW venograms can be obtained using multi-echo gradient-recalled acquisition and successive model-based denoising of both magnitude and phase data.

AB - The vein structures of the brain are important for understanding brain function and structure, especially when functional magnetic resonance imaging (fMRI) is utilized, as fMRI is based on changes in the blood-oxygen-level-dependent (BOLD) signal, which is directly related to veins. The aim of the present study was to develop an effective method to produce high signal-to-noise-ratio (SNR) and high-resolution multi-contrast susceptibility-weighted (SW) images of vein structures from 3T magnetic resonance (MR) scanners using multi-gradient-echo MR acquisition and a successive denoising process for both magnitude and phase data. Successive multi-echo MR images were acquired at multiple time points using a multigradient-recalled echo sequence at 3T, and noise in the magnitude and phase data was effectively suppressed using model-based denoising methods. A T2* relaxation model was used to denoise the magnitude data and a linear phase model was used to denoise the phase data. SW venography images were obtained from the denoised MR data and compared with conventional SW venography. To evaluate the performance of our denoising methods, we conducted numerical simulation studies and compared the mean-squared-error (MSE), SNR, and contrast-to-noise ratio (CNR) that we obtained using our procedure with those obtained using conventional denoising methods. In addition, images were inspected visually. Numerical simulations showed that our proposed model-based denoising methods were the most effective at suppressing noise. In vivo experiments also showed a substantial increase in the SNR of the phase mask obtained using the proposed denoising process (twice that of the conventional GRE-based phase mask). The T2* relaxation model method improved the SNR of the magnitude image (1.17-1.35 times that of the GRE-based magnitude image). Noise suppression of both magnitude and phase data using our proposed method resulted in an overall increase in the SNR and CNR in the final SW venography (1.1-1.5-fold and 1.96-fold higher SNR and CNR, respectively, than that of the GRE-based SW venography). We demonstrated that high SNR and high-resolution SW venograms can be obtained using multi-echo gradient-recalled acquisition and successive model-based denoising of both magnitude and phase data.

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