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

Authors
 Taejoon Eo  ;  Taeseong Kim  ;  Yohan Jun  ;  Hongpyo Lee  ;  Sung Soo Ahn  ;  Dong‐Hyun Kim  ;  Dosik Hwang 
Citation
 JOURNAL OF MAGNETIC RESONANCE IMAGING, Vol.45(6) : 1835-1845, 2017 
Journal Title
JOURNAL OF MAGNETIC RESONANCE IMAGING
ISSN
 1053-1807 
Issue Date
2017
MeSH
Adult ; Algorithms* ; Artifacts* ; Brain/anatomy & histology* ; Brain/diagnostic imaging* ; Computer Simulation ; Contrast Media ; Humans ; Image Enhancement/methods* ; Image Interpretation, Computer-Assisted/methods ; Magnetic Resonance Imaging/instrumentation ; Magnetic Resonance Imaging/methods* ; Male ; Models, Biological ; Models, Statistical ; Phantoms, Imaging ; Reproducibility of Results ; Sensitivity and Specificity ; Signal-To-Noise Ratio
Keywords
T2(*)-weighted ; multiple echoes ; noise reduction ; spatial filter ; 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.
Full Text
https://onlinelibrary.wiley.com/doi/abs/10.1002/jmri.25477
DOI
10.1002/jmri.25477
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Ahn, Sung Soo(안성수) ORCID logo https://orcid.org/0000-0002-0503-5558
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/160192
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