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In vivo imaging of phosphocreatine with artificial neural networks

Phosphocreatine (PCr) plays a vital role in neuron and myocyte energy homeostasis. Currently, there are no routine diagnostic tests to noninvasively map PCr distribution with clinically relevant spatial resolution and scan time. Here, we demonstrate that artificial neural network-based chemical exch...

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Autores principales: Chen, Lin, Schär, Michael, Chan, Kannie W. Y., Huang, Jianpan, Wei, Zhiliang, Lu, Hanzhang, Qin, Qin, Weiss, Robert G., van Zijl, Peter C. M., Xu, Jiadi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7044432/
https://www.ncbi.nlm.nih.gov/pubmed/32102999
http://dx.doi.org/10.1038/s41467-020-14874-0
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author Chen, Lin
Schär, Michael
Chan, Kannie W. Y.
Huang, Jianpan
Wei, Zhiliang
Lu, Hanzhang
Qin, Qin
Weiss, Robert G.
van Zijl, Peter C. M.
Xu, Jiadi
author_facet Chen, Lin
Schär, Michael
Chan, Kannie W. Y.
Huang, Jianpan
Wei, Zhiliang
Lu, Hanzhang
Qin, Qin
Weiss, Robert G.
van Zijl, Peter C. M.
Xu, Jiadi
author_sort Chen, Lin
collection PubMed
description Phosphocreatine (PCr) plays a vital role in neuron and myocyte energy homeostasis. Currently, there are no routine diagnostic tests to noninvasively map PCr distribution with clinically relevant spatial resolution and scan time. Here, we demonstrate that artificial neural network-based chemical exchange saturation transfer (ANNCEST) can be used to rapidly quantify PCr concentration with robust immunity to commonly seen MRI interferences. High-quality PCr mapping of human skeletal muscle, as well as the information of exchange rate, magnetic field and radio-frequency transmission inhomogeneities, can be obtained within 1.5 min on a 3 T standard MRI scanner using ANNCEST. For further validation, we apply ANNCEST to measure the PCr concentrations in exercised skeletal muscle. The ANNCEST outcomes strongly correlate with those from (31)P magnetic resonance spectroscopy (R = 0.813, p < 0.001, t test). These results suggest that ANNCEST has potential as a cost-effective and widely available method for measuring PCr and diagnosing related diseases.
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spelling pubmed-70444322020-03-04 In vivo imaging of phosphocreatine with artificial neural networks Chen, Lin Schär, Michael Chan, Kannie W. Y. Huang, Jianpan Wei, Zhiliang Lu, Hanzhang Qin, Qin Weiss, Robert G. van Zijl, Peter C. M. Xu, Jiadi Nat Commun Article Phosphocreatine (PCr) plays a vital role in neuron and myocyte energy homeostasis. Currently, there are no routine diagnostic tests to noninvasively map PCr distribution with clinically relevant spatial resolution and scan time. Here, we demonstrate that artificial neural network-based chemical exchange saturation transfer (ANNCEST) can be used to rapidly quantify PCr concentration with robust immunity to commonly seen MRI interferences. High-quality PCr mapping of human skeletal muscle, as well as the information of exchange rate, magnetic field and radio-frequency transmission inhomogeneities, can be obtained within 1.5 min on a 3 T standard MRI scanner using ANNCEST. For further validation, we apply ANNCEST to measure the PCr concentrations in exercised skeletal muscle. The ANNCEST outcomes strongly correlate with those from (31)P magnetic resonance spectroscopy (R = 0.813, p < 0.001, t test). These results suggest that ANNCEST has potential as a cost-effective and widely available method for measuring PCr and diagnosing related diseases. Nature Publishing Group UK 2020-02-26 /pmc/articles/PMC7044432/ /pubmed/32102999 http://dx.doi.org/10.1038/s41467-020-14874-0 Text en © The Author(s) 2020 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 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/.
spellingShingle Article
Chen, Lin
Schär, Michael
Chan, Kannie W. Y.
Huang, Jianpan
Wei, Zhiliang
Lu, Hanzhang
Qin, Qin
Weiss, Robert G.
van Zijl, Peter C. M.
Xu, Jiadi
In vivo imaging of phosphocreatine with artificial neural networks
title In vivo imaging of phosphocreatine with artificial neural networks
title_full In vivo imaging of phosphocreatine with artificial neural networks
title_fullStr In vivo imaging of phosphocreatine with artificial neural networks
title_full_unstemmed In vivo imaging of phosphocreatine with artificial neural networks
title_short In vivo imaging of phosphocreatine with artificial neural networks
title_sort in vivo imaging of phosphocreatine with artificial neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7044432/
https://www.ncbi.nlm.nih.gov/pubmed/32102999
http://dx.doi.org/10.1038/s41467-020-14874-0
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