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Morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase
Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging technique that quantifies the magnetic susceptibility distribution within biological tissues. QSM calculates the underlying magnetic susceptibility by deconvolving the tissue magnetic field map with a unit dipole kernel. Howev...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940224/ https://www.ncbi.nlm.nih.gov/pubmed/29738526 http://dx.doi.org/10.1371/journal.pone.0196922 |
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author | Guo, Li Mei, Yingjie Guan, Jijing Tan, Xiangliang Xu, Yikai Chen, Wufan Feng, Qianjin Feng, Yanqiu |
author_facet | Guo, Li Mei, Yingjie Guan, Jijing Tan, Xiangliang Xu, Yikai Chen, Wufan Feng, Qianjin Feng, Yanqiu |
author_sort | Guo, Li |
collection | PubMed |
description | Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging technique that quantifies the magnetic susceptibility distribution within biological tissues. QSM calculates the underlying magnetic susceptibility by deconvolving the tissue magnetic field map with a unit dipole kernel. However, this deconvolution problem is ill-posed. The morphology enabled dipole inversion (MEDI) introduces total variation (TV) to regularize the susceptibility reconstruction. However, MEDI results still contain artifacts near tissue boundaries because MEDI only imposes TV constraint on voxels inside smooth regions. We introduce a Morphology-Adaptive TV (MATV) for improving TV-regularized QSM. The MATV method first classifies imaging target into smooth and nonsmooth regions by thresholding magnitude gradients. In the dipole inversion for QSM, the TV regularization weights are a monotonically decreasing function of magnitude gradients. Thus, voxels inside smooth regions are assigned with larger weights than those in nonsmooth regions. Using phantom and in vivo datasets, we compared the performance of MATV with that of MEDI. MATV results had better visual quality than MEDI results, especially near tissue boundaries. Preliminary brain imaging results illustrated that MATV has potential to improve the reconstruction of regions near tissue boundaries. |
format | Online Article Text |
id | pubmed-5940224 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-59402242018-05-18 Morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase Guo, Li Mei, Yingjie Guan, Jijing Tan, Xiangliang Xu, Yikai Chen, Wufan Feng, Qianjin Feng, Yanqiu PLoS One Research Article Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging technique that quantifies the magnetic susceptibility distribution within biological tissues. QSM calculates the underlying magnetic susceptibility by deconvolving the tissue magnetic field map with a unit dipole kernel. However, this deconvolution problem is ill-posed. The morphology enabled dipole inversion (MEDI) introduces total variation (TV) to regularize the susceptibility reconstruction. However, MEDI results still contain artifacts near tissue boundaries because MEDI only imposes TV constraint on voxels inside smooth regions. We introduce a Morphology-Adaptive TV (MATV) for improving TV-regularized QSM. The MATV method first classifies imaging target into smooth and nonsmooth regions by thresholding magnitude gradients. In the dipole inversion for QSM, the TV regularization weights are a monotonically decreasing function of magnitude gradients. Thus, voxels inside smooth regions are assigned with larger weights than those in nonsmooth regions. Using phantom and in vivo datasets, we compared the performance of MATV with that of MEDI. MATV results had better visual quality than MEDI results, especially near tissue boundaries. Preliminary brain imaging results illustrated that MATV has potential to improve the reconstruction of regions near tissue boundaries. Public Library of Science 2018-05-08 /pmc/articles/PMC5940224/ /pubmed/29738526 http://dx.doi.org/10.1371/journal.pone.0196922 Text en © 2018 Guo et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Guo, Li Mei, Yingjie Guan, Jijing Tan, Xiangliang Xu, Yikai Chen, Wufan Feng, Qianjin Feng, Yanqiu Morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase |
title | Morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase |
title_full | Morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase |
title_fullStr | Morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase |
title_full_unstemmed | Morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase |
title_short | Morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase |
title_sort | morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940224/ https://www.ncbi.nlm.nih.gov/pubmed/29738526 http://dx.doi.org/10.1371/journal.pone.0196922 |
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