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...

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Main Authors: Li Guo, Yingjie Mei, Jijing Guan, Xiangliang Tan, Yikai Xu, Wufan Chen, Qianjin Feng, Yanqiu Feng
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2018-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5940224?pdf=render
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spelling doaj-7efefc02eeba486ba914d1b2deae89f02020-11-25T02:45:01ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01135e019692210.1371/journal.pone.0196922Morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase.Li GuoYingjie MeiJijing GuanXiangliang TanYikai XuWufan ChenQianjin FengYanqiu FengQuantitative 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.http://europepmc.org/articles/PMC5940224?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Li Guo
Yingjie Mei
Jijing Guan
Xiangliang Tan
Yikai Xu
Wufan Chen
Qianjin Feng
Yanqiu Feng
spellingShingle Li Guo
Yingjie Mei
Jijing Guan
Xiangliang Tan
Yikai Xu
Wufan Chen
Qianjin Feng
Yanqiu Feng
Morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase.
PLoS ONE
author_facet Li Guo
Yingjie Mei
Jijing Guan
Xiangliang Tan
Yikai Xu
Wufan Chen
Qianjin Feng
Yanqiu Feng
author_sort Li Guo
title 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_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_sort morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2018-01-01
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.
url http://europepmc.org/articles/PMC5940224?pdf=render
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