High-SNR multiple T2(*)-contrast magnetic resonance imaging using a robust denoising method based on tissue characteristics

Taejoon Eo, Taeseong Kim, Yohan Jun, Hongpyo Lee, Sung Soo Ahn, Donghyun Kim, Do Sik Hwang

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Abstract

Purpose: To develop an effective method that can suppress noise in successive multiecho T2(*)-weighted magnetic resonance (MR) brain images while preventing filtering artifacts. Materials and Methods: For the simulation experiments, we used multiple T2-weighted images of an anatomical brain phantom. For in vivo experiments, successive multiecho MR brain images were acquired from five healthy subjects using a multiecho gradient-recalled-echo (MGRE) sequence with a 3T MRI system. Our denoising method is a nonlinear filter whose filtering weights are determined by tissue characteristics among pixels. The similarity of the tissue characteristics is measured based on the l2-difference between two temporal decay signals. Both numerical and subjective evaluations were performed in order to compare the effectiveness of our denoising method with those of conventional filters, including Gaussian low-pass filter (LPF), anisotropic diffusion filter (ADF), and bilateral filter. Root-mean-square error (RMSE), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were used in the numerical evaluation. Five observers, including one radiologist, assessed the image quality and rated subjective scores in the subjective evaluation. Results: Our denoising method significantly improves RMSE, SNR, and CNR of numerical phantom images, and CNR of in vivo brain images in comparison with conventional filters (P < 0.005). It also receives the highest scores for structure conspicuity (8.2 to 9.4 out of 10) and naturalness (9.2 to 9.8 out of 10) among the conventional filters in the subjective evaluation. Conclusion: This study demonstrates that high-SNR multiple T2(*)-contrast MR images can be obtained using our denoising method based on tissue characteristics without noticeable artifacts. Evidence level: 2. J. MAGN. RESON. IMAGING 2017;45:1835–1845.

Original languageEnglish
Pages (from-to)1835-1845
Number of pages11
JournalJournal of Magnetic Resonance Imaging
Volume45
Issue number6
DOIs
Publication statusPublished - 2017 Jun 1

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Signal-To-Noise Ratio
Magnetic Resonance Imaging
Noise
Magnetic Resonance Spectroscopy
Brain
Artifacts
Healthy Volunteers
Weights and Measures

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

Cite this

@article{35a325d34f6c40139c8235ac34b6737e,
title = "High-SNR multiple T2(*)-contrast magnetic resonance imaging using a robust denoising method based on tissue characteristics",
abstract = "Purpose: To develop an effective method that can suppress noise in successive multiecho T2(*)-weighted magnetic resonance (MR) brain images while preventing filtering artifacts. Materials and Methods: For the simulation experiments, we used multiple T2-weighted images of an anatomical brain phantom. For in vivo experiments, successive multiecho MR brain images were acquired from five healthy subjects using a multiecho gradient-recalled-echo (MGRE) sequence with a 3T MRI system. Our denoising method is a nonlinear filter whose filtering weights are determined by tissue characteristics among pixels. The similarity of the tissue characteristics is measured based on the l2-difference between two temporal decay signals. Both numerical and subjective evaluations were performed in order to compare the effectiveness of our denoising method with those of conventional filters, including Gaussian low-pass filter (LPF), anisotropic diffusion filter (ADF), and bilateral filter. Root-mean-square error (RMSE), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were used in the numerical evaluation. Five observers, including one radiologist, assessed the image quality and rated subjective scores in the subjective evaluation. Results: Our denoising method significantly improves RMSE, SNR, and CNR of numerical phantom images, and CNR of in vivo brain images in comparison with conventional filters (P < 0.005). It also receives the highest scores for structure conspicuity (8.2 to 9.4 out of 10) and naturalness (9.2 to 9.8 out of 10) among the conventional filters in the subjective evaluation. Conclusion: This study demonstrates that high-SNR multiple T2(*)-contrast MR images can be obtained using our denoising method based on tissue characteristics without noticeable artifacts. Evidence level: 2. J. MAGN. RESON. IMAGING 2017;45:1835–1845.",
author = "Taejoon Eo and Taeseong Kim and Yohan Jun and Hongpyo Lee and Ahn, {Sung Soo} and Donghyun Kim and Hwang, {Do Sik}",
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High-SNR multiple T2(*)-contrast magnetic resonance imaging using a robust denoising method based on tissue characteristics. / Eo, Taejoon; Kim, Taeseong; Jun, Yohan; Lee, Hongpyo; Ahn, Sung Soo; Kim, Donghyun; Hwang, Do Sik.

In: Journal of Magnetic Resonance Imaging, Vol. 45, No. 6, 01.06.2017, p. 1835-1845.

Research output: Contribution to journalArticle

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T1 - High-SNR multiple T2(*)-contrast magnetic resonance imaging using a robust denoising method based on tissue characteristics

AU - Eo, Taejoon

AU - Kim, Taeseong

AU - Jun, Yohan

AU - Lee, Hongpyo

AU - Ahn, Sung Soo

AU - Kim, Donghyun

AU - Hwang, Do Sik

PY - 2017/6/1

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N2 - Purpose: To develop an effective method that can suppress noise in successive multiecho T2(*)-weighted magnetic resonance (MR) brain images while preventing filtering artifacts. Materials and Methods: For the simulation experiments, we used multiple T2-weighted images of an anatomical brain phantom. For in vivo experiments, successive multiecho MR brain images were acquired from five healthy subjects using a multiecho gradient-recalled-echo (MGRE) sequence with a 3T MRI system. Our denoising method is a nonlinear filter whose filtering weights are determined by tissue characteristics among pixels. The similarity of the tissue characteristics is measured based on the l2-difference between two temporal decay signals. Both numerical and subjective evaluations were performed in order to compare the effectiveness of our denoising method with those of conventional filters, including Gaussian low-pass filter (LPF), anisotropic diffusion filter (ADF), and bilateral filter. Root-mean-square error (RMSE), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were used in the numerical evaluation. Five observers, including one radiologist, assessed the image quality and rated subjective scores in the subjective evaluation. Results: Our denoising method significantly improves RMSE, SNR, and CNR of numerical phantom images, and CNR of in vivo brain images in comparison with conventional filters (P < 0.005). It also receives the highest scores for structure conspicuity (8.2 to 9.4 out of 10) and naturalness (9.2 to 9.8 out of 10) among the conventional filters in the subjective evaluation. Conclusion: This study demonstrates that high-SNR multiple T2(*)-contrast MR images can be obtained using our denoising method based on tissue characteristics without noticeable artifacts. Evidence level: 2. J. MAGN. RESON. IMAGING 2017;45:1835–1845.

AB - Purpose: To develop an effective method that can suppress noise in successive multiecho T2(*)-weighted magnetic resonance (MR) brain images while preventing filtering artifacts. Materials and Methods: For the simulation experiments, we used multiple T2-weighted images of an anatomical brain phantom. For in vivo experiments, successive multiecho MR brain images were acquired from five healthy subjects using a multiecho gradient-recalled-echo (MGRE) sequence with a 3T MRI system. Our denoising method is a nonlinear filter whose filtering weights are determined by tissue characteristics among pixels. The similarity of the tissue characteristics is measured based on the l2-difference between two temporal decay signals. Both numerical and subjective evaluations were performed in order to compare the effectiveness of our denoising method with those of conventional filters, including Gaussian low-pass filter (LPF), anisotropic diffusion filter (ADF), and bilateral filter. Root-mean-square error (RMSE), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were used in the numerical evaluation. Five observers, including one radiologist, assessed the image quality and rated subjective scores in the subjective evaluation. Results: Our denoising method significantly improves RMSE, SNR, and CNR of numerical phantom images, and CNR of in vivo brain images in comparison with conventional filters (P < 0.005). It also receives the highest scores for structure conspicuity (8.2 to 9.4 out of 10) and naturalness (9.2 to 9.8 out of 10) among the conventional filters in the subjective evaluation. Conclusion: This study demonstrates that high-SNR multiple T2(*)-contrast MR images can be obtained using our denoising method based on tissue characteristics without noticeable artifacts. Evidence level: 2. J. MAGN. RESON. IMAGING 2017;45:1835–1845.

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