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A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping

BACKGROUND: Accurate phase unwrapping is a critical prerequisite for successful applications in phase-related MRI, including quantitative susceptibility mapping (QSM) and susceptibility weighted imaging. However, many existing 3D phase unwrapping algorithms face challenges in the presence of severe...

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Autores principales: Cheng, Junying, Song, Manli, Xu, Zhongbiao, Zheng, Qian, Zhu, Li, Chen, Wufan, Feng, Yanqiu, Bao, Jianfeng, Cheng, Jingliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10684715/
https://www.ncbi.nlm.nih.gov/pubmed/38033538
http://dx.doi.org/10.3389/fnins.2023.1287788
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author Cheng, Junying
Song, Manli
Xu, Zhongbiao
Zheng, Qian
Zhu, Li
Chen, Wufan
Feng, Yanqiu
Bao, Jianfeng
Cheng, Jingliang
author_facet Cheng, Junying
Song, Manli
Xu, Zhongbiao
Zheng, Qian
Zhu, Li
Chen, Wufan
Feng, Yanqiu
Bao, Jianfeng
Cheng, Jingliang
author_sort Cheng, Junying
collection PubMed
description BACKGROUND: Accurate phase unwrapping is a critical prerequisite for successful applications in phase-related MRI, including quantitative susceptibility mapping (QSM) and susceptibility weighted imaging. However, many existing 3D phase unwrapping algorithms face challenges in the presence of severe noise, rapidly changing phase, and open-end cutline. METHODS: In this study, we introduce a novel 3D phase unwrapping approach utilizing region partitioning and a local polynomial model. Initially, the method leverages phase partitioning to create initial regions. Noisy voxels connecting areas within these regions are excluded and grouped into residual voxels. The connected regions within the region of interest are then reidentified and categorized into blocks and residual voxels based on voxel count thresholds. Subsequently, the method sequentially performs inter-block and residual voxel phase unwrapping using the local polynomial model. The proposed method was evaluated on simulation and in vivo abdominal QSM data, and was compared with the classical Region-growing, Laplacian_based, Graph-cut, and PRELUDE methods. RESULTS: Simulation experiments, conducted under different signal-to-noise ratios and phase change levels, consistently demonstrate that the proposed method achieves accurate unwrapping results, with mean error ratios not exceeding 0.01%. In contrast, the error ratios of Region-growing (N/A, 84.47%), Laplacian_based (20.65%, N/A), Graph-cut (2.26%, 20.71%), and PRELUDE (4.28%, 10.33%) methods are all substantially higher than those of the proposed method. In vivo abdominal QSM experiments further confirm the effectiveness of the proposed method in unwrapping phase data and successfully reconstructing susceptibility maps, even in scenarios with significant noise, rapidly changing phase, and open-end cutline in a large field of view. CONCLUSION: The proposed method demonstrates robust and accurate phase unwrapping capabilities, positioning it as a promising option for abdominal QSM applications.
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spelling pubmed-106847152023-11-30 A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping Cheng, Junying Song, Manli Xu, Zhongbiao Zheng, Qian Zhu, Li Chen, Wufan Feng, Yanqiu Bao, Jianfeng Cheng, Jingliang Front Neurosci Neuroscience BACKGROUND: Accurate phase unwrapping is a critical prerequisite for successful applications in phase-related MRI, including quantitative susceptibility mapping (QSM) and susceptibility weighted imaging. However, many existing 3D phase unwrapping algorithms face challenges in the presence of severe noise, rapidly changing phase, and open-end cutline. METHODS: In this study, we introduce a novel 3D phase unwrapping approach utilizing region partitioning and a local polynomial model. Initially, the method leverages phase partitioning to create initial regions. Noisy voxels connecting areas within these regions are excluded and grouped into residual voxels. The connected regions within the region of interest are then reidentified and categorized into blocks and residual voxels based on voxel count thresholds. Subsequently, the method sequentially performs inter-block and residual voxel phase unwrapping using the local polynomial model. The proposed method was evaluated on simulation and in vivo abdominal QSM data, and was compared with the classical Region-growing, Laplacian_based, Graph-cut, and PRELUDE methods. RESULTS: Simulation experiments, conducted under different signal-to-noise ratios and phase change levels, consistently demonstrate that the proposed method achieves accurate unwrapping results, with mean error ratios not exceeding 0.01%. In contrast, the error ratios of Region-growing (N/A, 84.47%), Laplacian_based (20.65%, N/A), Graph-cut (2.26%, 20.71%), and PRELUDE (4.28%, 10.33%) methods are all substantially higher than those of the proposed method. In vivo abdominal QSM experiments further confirm the effectiveness of the proposed method in unwrapping phase data and successfully reconstructing susceptibility maps, even in scenarios with significant noise, rapidly changing phase, and open-end cutline in a large field of view. CONCLUSION: The proposed method demonstrates robust and accurate phase unwrapping capabilities, positioning it as a promising option for abdominal QSM applications. Frontiers Media S.A. 2023-11-15 /pmc/articles/PMC10684715/ /pubmed/38033538 http://dx.doi.org/10.3389/fnins.2023.1287788 Text en Copyright © 2023 Cheng, Song, Xu, Zheng, Zhu, Chen, Feng, Bao and Cheng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Cheng, Junying
Song, Manli
Xu, Zhongbiao
Zheng, Qian
Zhu, Li
Chen, Wufan
Feng, Yanqiu
Bao, Jianfeng
Cheng, Jingliang
A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping
title A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping
title_full A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping
title_fullStr A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping
title_full_unstemmed A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping
title_short A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping
title_sort new 3d phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10684715/
https://www.ncbi.nlm.nih.gov/pubmed/38033538
http://dx.doi.org/10.3389/fnins.2023.1287788
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