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Improved Liver R2* Mapping by Averaging Decay Curves.

Zhang, X; Peng, J; Wang, C; Feng, Y; Feng, Q; Li, X; Chen, W; He, T (2017) Improved Liver R2* Mapping by Averaging Decay Curves. Sci Rep, 7 (1). p. 6158. ISSN 2045-2322 https://doi.org/10.1038/s41598-017-05683-5
SGUL Authors: He, Taigang

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Abstract

Liver R2* mapping is often degraded by the low signal-to-noise ratio (SNR) especially in the presence of severe iron. This study aims to improve liver R2* mapping at low SNRs by averaging decay curves before the process of curve-fitting. Independently filtering echo images by nonlocal means (NLM) demonstrated improved quality of R2* mapping, but may introduce new errors due to the nonlinear nature of the NLM filter, during which the averaging weights may vary with different image contents at multiple echo times. In addition, the image denoising effect of the NLM may decline when no sufficient similar patches are available. To overcome these drawbacks, we proposed to filter decay curves instead of images. In this novel scheme, decay curves were averaged in a local window, each with a weight assigned according to the curve-similarity measured by the distance between one of the neighboring curves and the targeted one. The proposed method was tested on simulated, phantom and patient data. The results demonstrate that the proposed method can provide more accurate R2* mapping compared with the NLM algorithm, and hence has the potential to improve diagnosis and therapy in patients with liver iron.

Item Type: Article
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2017
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: Sci Rep
ISSN: 2045-2322
Language: eng
Dates:
DateEvent
21 July 2017Published
1 June 2017Accepted
Publisher License: Creative Commons: Attribution 4.0
PubMed ID: 28733666
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/109011
Publisher's version: https://doi.org/10.1038/s41598-017-05683-5

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