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Improved liver R2* mapping by pixel-wise curve fitting with adaptive neighborhood regularization.

Wang, C; Zhang, X; Liu, X; He, T; Chen, W; Feng, Q; Feng, Y (2018) Improved liver R2* mapping by pixel-wise curve fitting with adaptive neighborhood regularization. Magn Reson Med, 80 (2). pp. 792-801. ISSN 1522-2594 https://doi.org/10.1002/mrm.27071
SGUL Authors: He, Taigang

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Abstract

PURPOSE: To improve liver R2* mapping by incorporating adaptive neighborhood regularization into pixel-wise curve fitting. METHODS: Magnetic resonance imaging R2* mapping remains challenging because of the serial images with low signal-to-noise ratio. In this study, we proposed to exploit the neighboring pixels as regularization terms and adaptively determine the regularization parameters according to the interpixel signal similarity. The proposed algorithm, called the pixel-wise curve fitting with adaptive neighborhood regularization (PCANR), was compared with the conventional nonlinear least squares (NLS) and nonlocal means filter-based NLS algorithms on simulated, phantom, and in vivo data. RESULTS: Visually, the PCANR algorithm generates R2* maps with significantly reduced noise and well-preserved tiny structures. Quantitatively, the PCANR algorithm produces R2* maps with lower root mean square errors at varying R2* values and signal-to-noise-ratio levels compared with the NLS and nonlocal means filter-based NLS algorithms. For the high R2* values under low signal-to-noise-ratio levels, the PCANR algorithm outperforms the NLS and nonlocal means filter-based NLS algorithms in the accuracy and precision, in terms of mean and standard deviation of R2* measurements in selected region of interests, respectively. CONCLUSIONS: The PCANR algorithm can reduce the effect of noise on liver R2* mapping, and the improved measurement precision will benefit the assessment of hepatic iron in clinical practice. Magn Reson Med, 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Item Type: Article
Additional Information: This is the peer reviewed version of the following article: Wang, C. , Zhang, X. , Liu, X. , He, T. , Chen, W. , Feng, Q. and Feng, Y. (2018), Improved liver R2* mapping by pixel‐wise curve fitting with adaptive neighborhood regularization. Magn. Reson. Med., 80: 792-801, which has been published in final form at https://doi.org/10.1002/mrm.27071. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Keywords: R2* mapping, MR relaxometry, adaptive neighborhood regularization, hepatic iron concentration, noncentral chi noise, R2* mapping, MR relaxometry, adaptive neighborhood regularization, hepatic iron concentration, noncentral chi noise, Nuclear Medicine & Medical Imaging, 0903 Biomedical Engineering
SGUL Research Institute / Research Centre: Academic Structure > Molecular and Clinical Sciences Research Institute (MCS)
Academic Structure > Molecular and Clinical Sciences Research Institute (MCS) > Cardiac (INCCCA)
Journal or Publication Title: Magn Reson Med
ISSN: 1522-2594
Language: eng
Dates:
DateEvent
20 April 2018Published
15 January 2018Published Online
12 December 2017Accepted
Publisher License: Publisher's own licence
Projects:
Project IDFunderFunder ID
2016M602490China Postdoctoral Science Foundationhttp://dx.doi.org/10.13039/501100002858
2016A030310380Natural Science Foundation of Guangdong ProvinceUNSPECIFIED
2017B090912006Technology R&D Program of GuangdongUNSPECIFIED
61671228National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
61471188National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
61728107National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
81371539National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
81601564National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
81501548National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
2015BAI01B03National Key Technology R&D Program of ChinaUNSPECIFIED
2016YFC0104003National Key Research and Development PlanUNSPECIFIED
PubMed ID: 29334128
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/109561
Publisher's version: https://doi.org/10.1002/mrm.27071

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