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A Framework for Predicting X-Nuclei Transmitter Gain Using (1)H Signal

Commercial human MR scanners are optimised for proton imaging, containing sophisticated prescan algorithms with setting parameters such as RF transmit gain and power. These are not optimal for X-nuclear application and are challenging to apply to hyperpolarised experiments, where the non-renewable m...

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Autores principales: Vaeggemose, Michael, Schulte, Rolf F., Hansen, Esben S. S., Miller, Jack J., Rasmussen, Camilla W., Pilgrim-Morris, Jemima H., Stewart, Neil J., Collier, Guilhem J., Wild, Jim M., Laustsen, Christoffer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10514872/
https://www.ncbi.nlm.nih.gov/pubmed/37736981
http://dx.doi.org/10.3390/tomography9050128
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author Vaeggemose, Michael
Schulte, Rolf F.
Hansen, Esben S. S.
Miller, Jack J.
Rasmussen, Camilla W.
Pilgrim-Morris, Jemima H.
Stewart, Neil J.
Collier, Guilhem J.
Wild, Jim M.
Laustsen, Christoffer
author_facet Vaeggemose, Michael
Schulte, Rolf F.
Hansen, Esben S. S.
Miller, Jack J.
Rasmussen, Camilla W.
Pilgrim-Morris, Jemima H.
Stewart, Neil J.
Collier, Guilhem J.
Wild, Jim M.
Laustsen, Christoffer
author_sort Vaeggemose, Michael
collection PubMed
description Commercial human MR scanners are optimised for proton imaging, containing sophisticated prescan algorithms with setting parameters such as RF transmit gain and power. These are not optimal for X-nuclear application and are challenging to apply to hyperpolarised experiments, where the non-renewable magnetisation signal changes during the experiment. We hypothesised that, despite the complex and inherently nonlinear electrodynamic physics underlying coil loading and spatial variation, simple linear regression would be sufficient to accurately predict X-nuclear transmit gain based on concomitantly acquired data from the proton body coil. We collected data across 156 scan visits at two sites as part of ongoing studies investigating sodium, hyperpolarised carbon, and hyperpolarised xenon. We demonstrate that simple linear regression is able to accurately predict sodium, carbon, or xenon transmit gain as a function of position and proton gain, with variation that is less than the intrasubject variability. In conclusion, sites running multinuclear studies may be able to remove the time-consuming need to separately acquire X-nuclear reference power calibration, inferring it from the proton instead.
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spelling pubmed-105148722023-09-23 A Framework for Predicting X-Nuclei Transmitter Gain Using (1)H Signal Vaeggemose, Michael Schulte, Rolf F. Hansen, Esben S. S. Miller, Jack J. Rasmussen, Camilla W. Pilgrim-Morris, Jemima H. Stewart, Neil J. Collier, Guilhem J. Wild, Jim M. Laustsen, Christoffer Tomography Article Commercial human MR scanners are optimised for proton imaging, containing sophisticated prescan algorithms with setting parameters such as RF transmit gain and power. These are not optimal for X-nuclear application and are challenging to apply to hyperpolarised experiments, where the non-renewable magnetisation signal changes during the experiment. We hypothesised that, despite the complex and inherently nonlinear electrodynamic physics underlying coil loading and spatial variation, simple linear regression would be sufficient to accurately predict X-nuclear transmit gain based on concomitantly acquired data from the proton body coil. We collected data across 156 scan visits at two sites as part of ongoing studies investigating sodium, hyperpolarised carbon, and hyperpolarised xenon. We demonstrate that simple linear regression is able to accurately predict sodium, carbon, or xenon transmit gain as a function of position and proton gain, with variation that is less than the intrasubject variability. In conclusion, sites running multinuclear studies may be able to remove the time-consuming need to separately acquire X-nuclear reference power calibration, inferring it from the proton instead. MDPI 2023-08-24 /pmc/articles/PMC10514872/ /pubmed/37736981 http://dx.doi.org/10.3390/tomography9050128 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Vaeggemose, Michael
Schulte, Rolf F.
Hansen, Esben S. S.
Miller, Jack J.
Rasmussen, Camilla W.
Pilgrim-Morris, Jemima H.
Stewart, Neil J.
Collier, Guilhem J.
Wild, Jim M.
Laustsen, Christoffer
A Framework for Predicting X-Nuclei Transmitter Gain Using (1)H Signal
title A Framework for Predicting X-Nuclei Transmitter Gain Using (1)H Signal
title_full A Framework for Predicting X-Nuclei Transmitter Gain Using (1)H Signal
title_fullStr A Framework for Predicting X-Nuclei Transmitter Gain Using (1)H Signal
title_full_unstemmed A Framework for Predicting X-Nuclei Transmitter Gain Using (1)H Signal
title_short A Framework for Predicting X-Nuclei Transmitter Gain Using (1)H Signal
title_sort framework for predicting x-nuclei transmitter gain using (1)h signal
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10514872/
https://www.ncbi.nlm.nih.gov/pubmed/37736981
http://dx.doi.org/10.3390/tomography9050128
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