Abstract
Purpose: Heating of gradient coils and passive shim components is a common cause of instability in the B 0 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.
Original language | English |
---|---|
Article number | 118430 |
Journal | NEUROIMAGE |
Volume | 241 |
DOIs | |
Publication status | Published - 1 Nov 2021 |
Keywords
- 3T
- Frequency drift
- Magnetic resonance spectroscopy (MRS)
- Multi-site
- Multi-vendor
- Press
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In: NEUROIMAGE, Vol. 241, 118430, 01.11.2021.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Frequency drift in MR spectroscopy at 3T
AU - Hui, Steve C. N.
AU - Mikkelsen, Mark
AU - Zöllner, Helge J.
AU - Ahluwalia, Vishwadeep
AU - Alcauter, Sarael
AU - Baltusis, Laima
AU - Barany, Deborah A.
AU - Barlow, Laura R.
AU - Becker, Robert
AU - Berman, Jeffrey I.
AU - Berrington, Adam
AU - Bhattacharyya, Pallab K.
AU - Blicher, Jakob Udby
AU - Bogner, Wolfgang
AU - Brown, Mark S.
AU - Calhoun, Vince D.
AU - Castillo, Ryan
AU - Cecil, Kim M.
AU - Choi, Yeo Bi
AU - Chu, Winnie C. W.
AU - Clarke, William T.
AU - Craven, Alexander R.
AU - Cuypers, Koen
AU - Dacko, Michael
AU - de la Fuente-Sandoval, Camilo
AU - Desmond, Patricia
AU - Domagalik, Aleksandra
AU - Dumont, Julien
AU - Duncan, Niall W.
AU - Dydak, Ulrike
AU - Dyke, Katherine
AU - Edmondson, David A.
AU - Ende, Gabriele
AU - Ersland, Lars
AU - Evans, C. John
AU - Fermin, Alan S. R.
AU - Ferretti, Antonio
AU - Fillmer, Ariane
AU - Gong, Tao
AU - Greenhouse, Ian
AU - Grist, James T.
AU - Gu, Meng
AU - Harris, Ashley D.
AU - Hat, Katarzyna
AU - Heba, Stefanie
AU - Heckova, Eva
AU - Hegarty, John P.
AU - Heise, Kirstin-Friederike
AU - Jacobson, Aaron
AU - Jansen, Jacobus F. A.
AU - Jenkins, Christopher W.
AU - Johnston, Stephen J.
AU - Juchem, Christoph
AU - Kangarlu, Alayar
AU - Kerr, Adam B.
AU - Landheer, Karl
AU - Lange, Thomas
AU - Lee, Phil
AU - Levendovszky, Swati Rane
AU - Limperopoulos, Catherine
AU - Liu, Feng
AU - Lloyd, William
AU - Lythgoe, David J.
AU - Machizawa, Maro G.
AU - MacMillan, Erin L.
AU - Maddock, Richard J.
AU - Manzhurtsev, Andrei V.
AU - Martinez-Gudino, María L.
AU - Miller, Jack J.
AU - Mirzakhanian, Heline
AU - Moreno-Ortega, Marta
AU - Mullins, Paul G.
AU - Near, Jamie
AU - Noeske, Ralph
AU - Nordhøy, Wibeke
AU - Oeltzschner, Georg
AU - Osorio-Duran, Raul
AU - Otaduy, Maria C. G.
AU - Pasaye, Erick H.
AU - Peeters, Ronald
AU - Peltier, Scott J.
AU - Pilatus, Ulrich
AU - Polomac, Nenad
AU - Porges, Eric C.
AU - Pradhan, Subechhya
AU - Prisciandaro, James Joseph
AU - Puts, Nicolaas A.
AU - Rae, Caroline D.
AU - Reyes-Madrigal, Francisco
AU - Roberts, Timothy P. L.
AU - Robertson, Caroline E.
AU - Rosenberg, Jens T.
AU - Rotaru, Diana-Georgiana
AU - O'Gorman Tuura, Ruth L.
AU - Saleh, Muhammad G.
AU - Sandberg, Kristian
AU - Sangill, Ryan
AU - Schembri, Keith
AU - Schrantee, Anouk
AU - Semenova, Natalia A.
AU - Singel, Debra
AU - Sitnikov, Rouslan
AU - Smith, Jolinda
AU - Song, Yulu
AU - Stark, Craig
AU - Stoffers, Diederick
AU - Swinnen, Stephan P.
AU - Tain, Rongwen
AU - Tanase, Costin
AU - Tapper, Sofie
AU - Tegenthoff, Martin
AU - Thiel, Thomas
AU - Thioux, Marc
AU - Truong, Peter
AU - van Dijk, Pim
AU - Vella, Nolan
AU - Vidyasagar, Rishma
AU - Vovk, Andrej
AU - Wang, Guangbin
AU - Westlye, Lars T.
AU - Wilbur, Timothy K.
AU - Willoughby, William R.
AU - Wilson, Martin
AU - Wittsack, Hans-J. rg
AU - Woods, Adam J.
AU - Wu, Yen-Chien
AU - Xu, Junqian
AU - Lopez, Maria Yanez
AU - Yeung, David K. W.
AU - Zhao, Qun
AU - Zhou, Xiaopeng
AU - Zupan, Gasper
AU - Edden, Richard A. E.
AU - Nakajima, Shinichiro Luke
AU - Honda, Shiori
N1 - Funding Information: This work was supported by NIH grants R01 EB016089, R01 EB023963, R21 AG060245, S10 OD021726, K01 AA025306, K99 AG062230, K99 DA051315, K99 EB028828, R01 MH110270, R01-DC008871, S10 OD012336, S10 OD021648, P41 EB031771, Taiwan MOST grant 108-2410-H-038-008-MY2, JST COI grant JPMJCE1311, ERC grant #249516, DLR 01ZX1909A SySMedSUDs, Natural Sciences and Engineering Research Council of Canada RGPIN/03875-2017, CONACYT grant CF-2019-6390, The European Research Council under the European Union's Horizon 2020 Research and Innovation program (ERC StG 802998), Ariane Fillmer has received funding from the 18HLT09 NeuroMET2 project within the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme. The contribution of Kristian Sandberg and Katarzyna Hat to this article is based upon work from COST Action CA18106, supported by COST (European Cooperation in Science and Technology). Marc Thioux and Pim van Dijk received funding from ZonMW, Dorhout Mees Foundation, Heinsius Houbolt Foundation and the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 764604. NeuRA Imaging, part of the Australian National Imaging Facility, is supported by the National Collaborative Research Infrastructure Scheme. We would also like to thank Dr. Yoshihiro Noda from Keio University for his assistant on data collection. All data were collected prospectively using MRI phantom. They will be available on the NITRC portal in the ?Big Drift? project repository (https://www.nitrc.org/projects/bigdrift/). See appendix in manuscript for more details. Data analysis was performed using MATLAB (R2020b, MathWorks, Natick, USA), and statistical analysis using R (RStudio: Integrated Development for R. RStudio, PBC, Boston, MA). All functions and packages are freely available on MATHWORKS and GITHUB. Funding Information: Jack J. Miller would like to acknowledge the support of a Novo Nordisk Research Fellowship run in conjunction with the University of Oxford. Francisco Reyes-Madrigal has served as a speaker for Janssen (Johnson & Johnson) and AstraZeneca. Marc Thioux and Pim van Dijk were supported by The Netherlands Organization for Health Research and Development (ZonMW) and the Dorhout Mees Foundation. All other authors have no conflict of interest to declare. Funding Information: This work was supported by NIH grants R01 EB016089 , R01 EB023963 , R21 AG060245 , S10 OD021726 , K01 AA025306 , K99 AG062230 , K99 DA051315 , K99 EB028828 , R01 MH110270 , R01-DC008871 , S10 OD012336 , S10 OD021648 , P41 EB031771 , Taiwan MOST grant 108-2410-H-038-008-MY2 , JST COI grant JPMJCE1311 , ERC grant #249516, DLR 01ZX1909A SySMedSUDs, Natural Sciences and Engineering Research Council of Canada RGPIN/03875-2017 , CONACYT grant CF-2019-6390 , The European Research Council under the European Union's Horizon 2020 Research and Innovation program (ERC StG 802998 ), Ariane Fillmer has received funding from the 18HLT09 NeuroMET2 project within the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme. The contribution of Kristian Sandberg and Katarzyna Hat to this article is based upon work from COST Action CA18106, supported by COST (European Cooperation in Science and Technology). Marc Thioux and Pim van Dijk received funding from ZonMW, Dorhout Mees Foundation, Heinsius Houbolt Foundation and the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 764604 . NeuRA Imaging, part of the Australian National Imaging Facility, is supported by the National Collaborative Research Infrastructure Scheme. We would also like to thank Dr. Yoshihiro Noda from Keio University for his assistant on data collection. Publisher Copyright: © 2021 Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/11/1
Y1 - 2021/11/1
N2 - Purpose: Heating of gradient coils and passive shim components is a common cause of instability in the B 0 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.
AB - Purpose: Heating of gradient coils and passive shim components is a common cause of instability in the B 0 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.
KW - 3T
KW - Frequency drift
KW - Magnetic resonance spectroscopy (MRS)
KW - Multi-site
KW - Multi-vendor
KW - Press
UR - http://www.scopus.com/inward/record.url?scp=85111337313&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.neuroimage.2021.118430
DO - https://doi.org/10.1016/j.neuroimage.2021.118430
M3 - Article
C2 - 34314848
SN - 1053-8119
VL - 241
JO - NEUROIMAGE
JF - NEUROIMAGE
M1 - 118430
ER -