@article{ead7da3f44e346f59744b8686aa4bf17,
title = "Frequency drift in MR spectroscopy at 3T",
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.",
keywords = "Magnetic resonance spectroscopy (MRS), Frequency drift, 3T, Press, Multi-vendor, Multi-site, MAGNETIC-RESONANCE-SPECTROSCOPY, GAMMA-AMINOBUTYRIC-ACID, GABA, ARTIFACTS, NAVIGATOR, BRAIN, PHASE, WATER",
author = "S.C.N. Hui and M. Mikkelsen and H.J. Zollner and V. Ahluwalia and S. Alcauter and L. Baltusis and D.A. Barany and L.R. Barlow and R. Becker and J.I. Berman and A. Berrington and P.K. Bhattacharyya and J.U. Blicher and W. Bogner and M.S. Brown and V.D. Calhoun and R. Castillo and K.M. Cecil and Y.B. Choi and W.C.W. Chu and W.T. Clarke and A.R. Craven and K. Cuypers and M. Dacko and {de la Fuente-Sandoval}, C. and P. Desmond and A. Domagalik and J. Dumont and N.W. Duncan and U. Dydak and K. Dyke and D.A. Edmondson and G. Ende and L. Ersland and C.J. Evans and A.S.R. Fermin and A. Ferretti and A. Fillmer and T. Gong and I. Greenhouse and J.T. Grist and M. Gu and A.D. Harris and K. Hatz and S. Heba and E. Heckova and J.P. Hegarty and K.F. Heise and S. Honda and A. Jacobson and J.F.A. Jansen and Edden, {Richard A. E.}",
note = "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{\l}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 Sk{\l}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. Publisher Copyright: {\textcopyright} 2021",
year = "2021",
month = nov,
day = "1",
doi = "10.1016/j.neuroimage.2021.118430",
language = "English",
volume = "241",
journal = "Neuroimage",
issn = "1053-8119",
publisher = "Elsevier Science",
}