Vaeggemose, M. orcid.org/0000-0001-7666-6819, Schulte, R.F. orcid.org/0000-0002-1334-1264, Hansen, E.S.S. orcid.org/0000-0001-5512-9870 et al. (7 more authors) (2023) A framework for predicting X-nuclei transmitter gain using 1H signal. Tomography, 9 (5). pp. 1603-1616. ISSN 2379-1381
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 by the authors. 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/). |
Keywords: | X-nuclei imaging; radio frequency setting; magnetic resonance imaging; sodium; carbon; xenon |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) |
Funding Information: | Funder Grant number MEDICAL RESEARCH COUNCIL MR/M008894/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Sep 2023 15:31 |
Last Modified: | 11 Sep 2023 15:31 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/tomography9050128 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:203264 |