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Quantitative susceptibility mapping using plug-and-play alternating direction method of multipliers

Quantitative susceptibility mapping employs regularization to reduce artifacts, yet many recent denoisers are unavailable for reconstruction. We developed a plug-and-play approach to QSM reconstruction (PnP QSM) and show its flexibility using several patch-based denoisers. We developed PnP QSM using...

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Autores principales: Kamesh Iyer, Srikant, Moon, Brianna F., Josselyn, Nicholas, Kurtz, Robert M., Song, Jae W., Ware, Jeffrey B., Nabavizadeh, S. Ali, Witschey, Walter R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755132/
https://www.ncbi.nlm.nih.gov/pubmed/36522372
http://dx.doi.org/10.1038/s41598-022-22778-w
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author Kamesh Iyer, Srikant
Moon, Brianna F.
Josselyn, Nicholas
Kurtz, Robert M.
Song, Jae W.
Ware, Jeffrey B.
Nabavizadeh, S. Ali
Witschey, Walter R.
author_facet Kamesh Iyer, Srikant
Moon, Brianna F.
Josselyn, Nicholas
Kurtz, Robert M.
Song, Jae W.
Ware, Jeffrey B.
Nabavizadeh, S. Ali
Witschey, Walter R.
author_sort Kamesh Iyer, Srikant
collection PubMed
description Quantitative susceptibility mapping employs regularization to reduce artifacts, yet many recent denoisers are unavailable for reconstruction. We developed a plug-and-play approach to QSM reconstruction (PnP QSM) and show its flexibility using several patch-based denoisers. We developed PnP QSM using alternating direction method of multiplier framework and applied collaborative filtering denoisers. We apply the technique to the 2016 QSM Challenge and in 10 glioblastoma multiforme datasets. We compared its performance with four published QSM techniques and a multi-orientation QSM method. We analyzed magnetic susceptibility accuracy using brain region-of-interest measurements, and image quality using global error metrics. Reconstructions on glioblastoma data were analyzed using ranked and semiquantitative image grading by three neuroradiologist observers to assess image quality (IQ) and sharpness (IS). PnP-BM4D QSM showed good correlation (β = 0.84, R(2) = 0.98, p < 0.05) with COSMOS and no significant bias (bias = 0.007 ± 0.012). PnP-BM4D QSM achieved excellent quality when assessed using structural similarity index metric (SSIM = 0.860), high frequency error norm (HFEN = 58.5), cross correlation (CC = 0.804), and mutual information (MI = 0.475) and also maintained good conspicuity of fine features. In glioblastoma datasets, PnP-BM4D QSM showed higher performance (IQ(Grade) = 2.4 ± 0.4, IS(Grade) = 2.7 ± 0.3, IQ(Rank) = 3.7 ± 0.3, IS(Rank) = 3.9 ± 0.3) compared to MEDI (IQ(Grade) = 2.1 ± 0.5, IS(Grade) = 2.1 ± 0.6, IQ(Rank) = 2.4 ± 0.6, IS(Rank) = 2.9 ± 0.2) and FANSI-TGV (IQ(Grade) = 2.2 ± 0.6, IS(Grade) = 2.1 ± 0.6, IQ(Rank) = 2.7 ± 0.3, IS(Rank) = 2.2 ± 0.2). We illustrated the modularity of PnP QSM by interchanging two additional patch-based denoisers. PnP QSM reconstruction was feasible, and its flexibility was shown using several patch-based denoisers. This technique may allow rapid prototyping and validation of new denoisers for QSM reconstruction for an array of useful clinical applications.
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spelling pubmed-97551322022-12-17 Quantitative susceptibility mapping using plug-and-play alternating direction method of multipliers Kamesh Iyer, Srikant Moon, Brianna F. Josselyn, Nicholas Kurtz, Robert M. Song, Jae W. Ware, Jeffrey B. Nabavizadeh, S. Ali Witschey, Walter R. Sci Rep Article Quantitative susceptibility mapping employs regularization to reduce artifacts, yet many recent denoisers are unavailable for reconstruction. We developed a plug-and-play approach to QSM reconstruction (PnP QSM) and show its flexibility using several patch-based denoisers. We developed PnP QSM using alternating direction method of multiplier framework and applied collaborative filtering denoisers. We apply the technique to the 2016 QSM Challenge and in 10 glioblastoma multiforme datasets. We compared its performance with four published QSM techniques and a multi-orientation QSM method. We analyzed magnetic susceptibility accuracy using brain region-of-interest measurements, and image quality using global error metrics. Reconstructions on glioblastoma data were analyzed using ranked and semiquantitative image grading by three neuroradiologist observers to assess image quality (IQ) and sharpness (IS). PnP-BM4D QSM showed good correlation (β = 0.84, R(2) = 0.98, p < 0.05) with COSMOS and no significant bias (bias = 0.007 ± 0.012). PnP-BM4D QSM achieved excellent quality when assessed using structural similarity index metric (SSIM = 0.860), high frequency error norm (HFEN = 58.5), cross correlation (CC = 0.804), and mutual information (MI = 0.475) and also maintained good conspicuity of fine features. In glioblastoma datasets, PnP-BM4D QSM showed higher performance (IQ(Grade) = 2.4 ± 0.4, IS(Grade) = 2.7 ± 0.3, IQ(Rank) = 3.7 ± 0.3, IS(Rank) = 3.9 ± 0.3) compared to MEDI (IQ(Grade) = 2.1 ± 0.5, IS(Grade) = 2.1 ± 0.6, IQ(Rank) = 2.4 ± 0.6, IS(Rank) = 2.9 ± 0.2) and FANSI-TGV (IQ(Grade) = 2.2 ± 0.6, IS(Grade) = 2.1 ± 0.6, IQ(Rank) = 2.7 ± 0.3, IS(Rank) = 2.2 ± 0.2). We illustrated the modularity of PnP QSM by interchanging two additional patch-based denoisers. PnP QSM reconstruction was feasible, and its flexibility was shown using several patch-based denoisers. This technique may allow rapid prototyping and validation of new denoisers for QSM reconstruction for an array of useful clinical applications. Nature Publishing Group UK 2022-12-15 /pmc/articles/PMC9755132/ /pubmed/36522372 http://dx.doi.org/10.1038/s41598-022-22778-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Kamesh Iyer, Srikant
Moon, Brianna F.
Josselyn, Nicholas
Kurtz, Robert M.
Song, Jae W.
Ware, Jeffrey B.
Nabavizadeh, S. Ali
Witschey, Walter R.
Quantitative susceptibility mapping using plug-and-play alternating direction method of multipliers
title Quantitative susceptibility mapping using plug-and-play alternating direction method of multipliers
title_full Quantitative susceptibility mapping using plug-and-play alternating direction method of multipliers
title_fullStr Quantitative susceptibility mapping using plug-and-play alternating direction method of multipliers
title_full_unstemmed Quantitative susceptibility mapping using plug-and-play alternating direction method of multipliers
title_short Quantitative susceptibility mapping using plug-and-play alternating direction method of multipliers
title_sort quantitative susceptibility mapping using plug-and-play alternating direction method of multipliers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755132/
https://www.ncbi.nlm.nih.gov/pubmed/36522372
http://dx.doi.org/10.1038/s41598-022-22778-w
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