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Frequency drift in MR spectroscopy at 3T

Steve C.N. Hui, Mark Mikkelsen, Helge J. Zöllner, Vishwadeep Ahluwalia, Sarael Alcauter, Laima Baltusis, Deborah A. Barany, Laura R. Barlow, Robert Becker, Jeffrey I. Berman, Adam Berrington, Pallab K. Bhattacharyya, Jakob Udby Blicher, Wolfgang Bogner, Mark S. Brown, Vince D. Calhoun, Ryan Castillo, Kim M. Cecil, Yeo Bi Choi, Winnie C.W. Chu, William T. Clarke, Alexander R. Craven, Koen Cuypers, Michael Dacko, Camilo de la Fuente-Sandoval, Patricia Desmond, Aleksandra Domagalik, Julien Dumont, Niall W. Duncan, Ulrike Dydak, Katherine Dyke, David A. Edmondson, Gabriele Ende, Lars Ersland, C. John Evans, Alan S.R. Fermin, Antonio Ferretti, Ariane Fillmer, Tao Gong, Ian Greenhouse, James T. Grist, Meng Gu, Ashley D. Harris, Katarzyna Hat, Stefanie Heba, Eva Heckova, John P. Hegarty, Kirstin-Friederike Heise, Shiori Honda, Aaron Jacobson, Jacobus F.A. Jansen, Christopher W. Jenkins, Stephen Johnston Orcid Logo, Christoph Juchem, Alayar Kangarlu, Adam B. Kerr, Karl Landheer, Thomas Lange, Phil Lee, Swati Rane Levendovszky, Catherine Limperopoulos, Feng Liu, William Lloyd, David J. Lythgoe, Maro G. Machizawa, Erin L. MacMillan, Richard J. Maddock, Andrei V. Manzhurtsev, María L. Martinez-Gudino, Jack J. Miller, Heline Mirzakhanian, Marta Moreno-Ortega, Paul G. Mullins, Shinichiro Nakajima, Jamie Near, Ralph Noeske, Wibeke Nordhøy, Georg Oeltzschner, Raul Osorio-Duran, Maria C.G. Otaduy, Erick H. Pasaye, Ronald Peeters, Scott J. Peltier, Ulrich Pilatus, Nenad Polomac, Eric C. Porges, Subechhya Pradhan, James Joseph Prisciandaro, Nicolaas A Puts, Caroline D. Rae, Francisco Reyes-Madrigal, Timothy P.L. Roberts, Caroline E. Robertson, Jens T. Rosenberg, Diana-Georgiana Rotaru, Ruth L O'Gorman Tuura, Muhammad G. Saleh, Kristian Sandberg, Ryan Sangill, Keith Schembri, Anouk Schrantee, Natalia A. Semenova, Debra Singel, Rouslan Sitnikov, Jolinda Smith, Yulu Song, Craig Stark, Diederick Stoffers, Stephan P. Swinnen, Rongwen Tain, Costin Tanase, Sofie Tapper, Martin Tegenthoff, Thomas Thiel, Marc Thioux, Peter Truong, Pim van Dijk, Nolan Vella, Rishma Vidyasagar, Andrej Vovk, Guangbin Wang, Lars T. Westlye, Timothy K. Wilbur, William R. Willoughby, Martin Wilson, Hans-Jörg Wittsack, Adam J. Woods, Yen-Chien Wu, Junqian Xu, Maria Yanez Lopez, David K.W. Yeung, Qun Zhao, Xiaopeng Zhou, Gasper Zupan, Richard A.E. Edden

NeuroImage, Volume: 241, Start page: 118430

Swansea University Author: Stephen Johnston Orcid Logo

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DOI (Published version): 10.1016/j.neuroimage.2021.118430

Abstract

Purpose Heating of gradient coils and passive shim components is a common cause of instability in the B0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-ti...

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Published in: NeuroImage
ISSN: 1053-8119
Published: Elsevier BV 2021
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-11-29T11:20:51.3188561</datestamp><bib-version>v2</bib-version><id>57471</id><entry>2021-07-29</entry><title>Frequency drift in MR spectroscopy at 3T</title><swanseaauthors><author><sid>a5a4e9fd4ddde98a4cc3c1e3c6fa310f</sid><ORCID>0000-0001-9360-8856</ORCID><firstname>Stephen</firstname><surname>Johnston</surname><name>Stephen Johnston</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-07-29</date><deptcode>HPS</deptcode><abstract>Purpose Heating of gradient coils and passive shim components is a common cause of instability in the B0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. Method A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson&#x2019;s and intraclass correlation coefficients (ICC). Results Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p &lt; 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. Discussion This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed. 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spelling 2022-11-29T11:20:51.3188561 v2 57471 2021-07-29 Frequency drift in MR spectroscopy at 3T a5a4e9fd4ddde98a4cc3c1e3c6fa310f 0000-0001-9360-8856 Stephen Johnston Stephen Johnston true false 2021-07-29 HPS Purpose Heating of gradient coils and passive shim components is a common cause of instability in the B0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. Method A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson’s and intraclass correlation coefficients (ICC). Results Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. Discussion This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed. Keywords: magnetic resonance spectroscopy (MRS), frequency drift, 3T, PRESS, multi-vendor, multi-site Journal Article NeuroImage 241 118430 Elsevier BV 1053-8119 Magnetic resonance spectroscopy (MRS); Frequency drift; 3T; Press; Multi-vendor; Multi-site 1 11 2021 2021-11-01 10.1016/j.neuroimage.2021.118430 COLLEGE NANME Psychology COLLEGE CODE HPS Swansea University 2022-11-29T11:20:51.3188561 2021-07-29T11:01:42.6309593 Faculty of Medicine, Health and Life Sciences School of Psychology Steve C.N. Hui 1 Mark Mikkelsen 2 Helge J. Zöllner 3 Vishwadeep Ahluwalia 4 Sarael Alcauter 5 Laima Baltusis 6 Deborah A. Barany 7 Laura R. Barlow 8 Robert Becker 9 Jeffrey I. Berman 10 Adam Berrington 11 Pallab K. Bhattacharyya 12 Jakob Udby Blicher 13 Wolfgang Bogner 14 Mark S. Brown 15 Vince D. Calhoun 16 Ryan Castillo 17 Kim M. Cecil 18 Yeo Bi Choi 19 Winnie C.W. Chu 20 William T. Clarke 21 Alexander R. Craven 22 Koen Cuypers 23 Michael Dacko 24 Camilo de la Fuente-Sandoval 25 Patricia Desmond 26 Aleksandra Domagalik 27 Julien Dumont 28 Niall W. Duncan 29 Ulrike Dydak 30 Katherine Dyke 31 David A. Edmondson 32 Gabriele Ende 33 Lars Ersland 34 C. John Evans 35 Alan S.R. Fermin 36 Antonio Ferretti 37 Ariane Fillmer 38 Tao Gong 39 Ian Greenhouse 40 James T. Grist 41 Meng Gu 42 Ashley D. Harris 43 Katarzyna Hat 44 Stefanie Heba 45 Eva Heckova 46 John P. Hegarty 47 Kirstin-Friederike Heise 48 Shiori Honda 49 Aaron Jacobson 50 Jacobus F.A. Jansen 51 Christopher W. Jenkins 52 Stephen Johnston 0000-0001-9360-8856 53 Christoph Juchem 54 Alayar Kangarlu 55 Adam B. Kerr 56 Karl Landheer 57 Thomas Lange 58 Phil Lee 59 Swati Rane Levendovszky 60 Catherine Limperopoulos 61 Feng Liu 62 William Lloyd 63 David J. Lythgoe 64 Maro G. Machizawa 65 Erin L. MacMillan 66 Richard J. Maddock 67 Andrei V. Manzhurtsev 68 María L. Martinez-Gudino 69 Jack J. Miller 70 Heline Mirzakhanian 71 Marta Moreno-Ortega 72 Paul G. Mullins 73 Shinichiro Nakajima 74 Jamie Near 75 Ralph Noeske 76 Wibeke Nordhøy 77 Georg Oeltzschner 78 Raul Osorio-Duran 79 Maria C.G. Otaduy 80 Erick H. Pasaye 81 Ronald Peeters 82 Scott J. Peltier 83 Ulrich Pilatus 84 Nenad Polomac 85 Eric C. Porges 86 Subechhya Pradhan 87 James Joseph Prisciandaro 88 Nicolaas A Puts 89 Caroline D. Rae 90 Francisco Reyes-Madrigal 91 Timothy P.L. Roberts 92 Caroline E. Robertson 93 Jens T. Rosenberg 94 Diana-Georgiana Rotaru 95 Ruth L O'Gorman Tuura 96 Muhammad G. Saleh 97 Kristian Sandberg 98 Ryan Sangill 99 Keith Schembri 100 Anouk Schrantee 101 Natalia A. Semenova 102 Debra Singel 103 Rouslan Sitnikov 104 Jolinda Smith 105 Yulu Song 106 Craig Stark 107 Diederick Stoffers 108 Stephan P. Swinnen 109 Rongwen Tain 110 Costin Tanase 111 Sofie Tapper 112 Martin Tegenthoff 113 Thomas Thiel 114 Marc Thioux 115 Peter Truong 116 Pim van Dijk 117 Nolan Vella 118 Rishma Vidyasagar 119 Andrej Vovk 120 Guangbin Wang 121 Lars T. Westlye 122 Timothy K. Wilbur 123 William R. Willoughby 124 Martin Wilson 125 Hans-Jörg Wittsack 126 Adam J. Woods 127 Yen-Chien Wu 128 Junqian Xu 129 Maria Yanez Lopez 130 David K.W. Yeung 131 Qun Zhao 132 Xiaopeng Zhou 133 Gasper Zupan 134 Richard A.E. Edden 135 57471__20675__050b4af3b6764125868bfd1a9e98f605.pdf 57471.pdf 2021-08-19T14:10:07.7961457 Output 2513956 application/pdf Version of Record true ©2021 The Authors. This is an open access article under the CC BY-NC-ND license true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Frequency drift in MR spectroscopy at 3T
spellingShingle Frequency drift in MR spectroscopy at 3T
Stephen Johnston
title_short Frequency drift in MR spectroscopy at 3T
title_full Frequency drift in MR spectroscopy at 3T
title_fullStr Frequency drift in MR spectroscopy at 3T
title_full_unstemmed Frequency drift in MR spectroscopy at 3T
title_sort Frequency drift in MR spectroscopy at 3T
author_id_str_mv a5a4e9fd4ddde98a4cc3c1e3c6fa310f
author_id_fullname_str_mv a5a4e9fd4ddde98a4cc3c1e3c6fa310f_***_Stephen Johnston
author Stephen Johnston
author2 Steve C.N. Hui
Mark Mikkelsen
Helge J. Zöllner
Vishwadeep Ahluwalia
Sarael Alcauter
Laima Baltusis
Deborah A. Barany
Laura R. Barlow
Robert Becker
Jeffrey I. Berman
Adam Berrington
Pallab K. Bhattacharyya
Jakob Udby Blicher
Wolfgang Bogner
Mark S. Brown
Vince D. Calhoun
Ryan Castillo
Kim M. Cecil
Yeo Bi Choi
Winnie C.W. Chu
William T. Clarke
Alexander R. Craven
Koen Cuypers
Michael Dacko
Camilo de la Fuente-Sandoval
Patricia Desmond
Aleksandra Domagalik
Julien Dumont
Niall W. Duncan
Ulrike Dydak
Katherine Dyke
David A. Edmondson
Gabriele Ende
Lars Ersland
C. John Evans
Alan S.R. Fermin
Antonio Ferretti
Ariane Fillmer
Tao Gong
Ian Greenhouse
James T. Grist
Meng Gu
Ashley D. Harris
Katarzyna Hat
Stefanie Heba
Eva Heckova
John P. Hegarty
Kirstin-Friederike Heise
Shiori Honda
Aaron Jacobson
Jacobus F.A. Jansen
Christopher W. Jenkins
Stephen Johnston
Christoph Juchem
Alayar Kangarlu
Adam B. Kerr
Karl Landheer
Thomas Lange
Phil Lee
Swati Rane Levendovszky
Catherine Limperopoulos
Feng Liu
William Lloyd
David J. Lythgoe
Maro G. Machizawa
Erin L. MacMillan
Richard J. Maddock
Andrei V. Manzhurtsev
María L. Martinez-Gudino
Jack J. Miller
Heline Mirzakhanian
Marta Moreno-Ortega
Paul G. Mullins
Shinichiro Nakajima
Jamie Near
Ralph Noeske
Wibeke Nordhøy
Georg Oeltzschner
Raul Osorio-Duran
Maria C.G. Otaduy
Erick H. Pasaye
Ronald Peeters
Scott J. Peltier
Ulrich Pilatus
Nenad Polomac
Eric C. Porges
Subechhya Pradhan
James Joseph Prisciandaro
Nicolaas A Puts
Caroline D. Rae
Francisco Reyes-Madrigal
Timothy P.L. Roberts
Caroline E. Robertson
Jens T. Rosenberg
Diana-Georgiana Rotaru
Ruth L O'Gorman Tuura
Muhammad G. Saleh
Kristian Sandberg
Ryan Sangill
Keith Schembri
Anouk Schrantee
Natalia A. Semenova
Debra Singel
Rouslan Sitnikov
Jolinda Smith
Yulu Song
Craig Stark
Diederick Stoffers
Stephan P. Swinnen
Rongwen Tain
Costin Tanase
Sofie Tapper
Martin Tegenthoff
Thomas Thiel
Marc Thioux
Peter Truong
Pim van Dijk
Nolan Vella
Rishma Vidyasagar
Andrej Vovk
Guangbin Wang
Lars T. Westlye
Timothy K. Wilbur
William R. Willoughby
Martin Wilson
Hans-Jörg Wittsack
Adam J. Woods
Yen-Chien Wu
Junqian Xu
Maria Yanez Lopez
David K.W. Yeung
Qun Zhao
Xiaopeng Zhou
Gasper Zupan
Richard A.E. Edden
format Journal article
container_title NeuroImage
container_volume 241
container_start_page 118430
publishDate 2021
institution Swansea University
issn 1053-8119
doi_str_mv 10.1016/j.neuroimage.2021.118430
publisher Elsevier BV
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str School of Psychology{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}School of Psychology
document_store_str 1
active_str 0
description Purpose Heating of gradient coils and passive shim components is a common cause of instability in the B0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. Method A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson’s and intraclass correlation coefficients (ICC). Results Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. Discussion This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed. Keywords: magnetic resonance spectroscopy (MRS), frequency drift, 3T, PRESS, multi-vendor, multi-site
published_date 2021-11-01T04:13:14Z
_version_ 1763753899665129472
score 11.016235

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