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

Michael Vaeggemose; Rolf F Schulte; Esben S S Hansen; Jack Miller; Camilla W Rasmussen; Jemima H Pilgrim-Morris; Neil J Stewart; Guilhem J Collier; Jim M Wild; Christoffer Laustsen

doi:10.3390/tomography9050128

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

Michael Vaeggemose, Rolf F Schulte, Esben S S Hansen, Jack Miller, Camilla W Rasmussen, Jemima H Pilgrim-Morris, Neil J Stewart, Guilhem J Collier, Jim M Wild, Christoffer Laustsen^*

^*Corresponding author for this work

Department of Clinical Medicine - The MR Research Centre

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

1 Citation (Scopus)

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.

Original language	English
Journal	Tomography - A Journal for Imaging Research
Volume	9
Issue	5
Pages (from-to)	1603-1616
Number of pages	14
ISSN	2379-1381
DOIs	https://doi.org/10.3390/tomography9050128
Publication status	Published - Oct 2023

Keywords

Algorithms
Calibration
Carbon
Humans
Protons
Xenon

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title = "A Framework for Predicting X-Nuclei Transmitter Gain Using 1H Signal",

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.",

keywords = "Algorithms, Calibration, Carbon, Humans, Protons, Xenon",

author = "Michael Vaeggemose and Schulte, {Rolf F} and Hansen, {Esben S S} and Jack Miller and Rasmussen, {Camilla W} and Pilgrim-Morris, {Jemima H} and Stewart, {Neil J} and Collier, {Guilhem J} and Wild, {Jim M} and Christoffer Laustsen",

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A Framework for Predicting X-Nuclei Transmitter Gain Using ¹H Signal. / Vaeggemose, Michael; Schulte, Rolf F; Hansen, Esben S S et al.
In: Tomography - A Journal for Imaging Research, Vol. 9, No. 5, 10.2023, p. 1603-1616.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

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AU - Pilgrim-Morris, Jemima H

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AU - Laustsen, Christoffer

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