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. 2021 Nov 1:241:118430.
doi: 10.1016/j.neuroimage.2021.118430. Epub 2021 Jul 24.

Frequency drift in MR spectroscopy at 3T

Steve C N Hui  1 Mark Mikkelsen  1 Helge J Zöllner  1 Vishwadeep Ahluwalia  2 Sarael Alcauter  3 Laima Baltusis  4 Deborah A Barany  5 Laura R Barlow  6 Robert Becker  7 Jeffrey I Berman  8 Adam Berrington  9 Pallab K Bhattacharyya  10 Jakob Udby Blicher  11 Wolfgang Bogner  12 Mark S Brown  13 Vince D Calhoun  14 Ryan Castillo  15 Kim M Cecil  16 Yeo Bi Choi  17 Winnie C W Chu  18 William T Clarke  19 Alexander R Craven  20 Koen Cuypers  21 Michael Dacko  22 Camilo de la Fuente-Sandoval  23 Patricia Desmond  24 Aleksandra Domagalik  25 Julien Dumont  26 Niall W Duncan  27 Ulrike Dydak  28 Katherine Dyke  29 David A Edmondson  16 Gabriele Ende  7 Lars Ersland  30 C John Evans  31 Alan S R Fermin  32 Antonio Ferretti  33 Ariane Fillmer  34 Tao Gong  35 Ian Greenhouse  36 James T Grist  37 Meng Gu  38 Ashley D Harris  39 Katarzyna Hat  40 Stefanie Heba  41 Eva Heckova  12 John P Hegarty 2nd  42 Kirstin-Friederike Heise  43 Shiori Honda  44 Aaron Jacobson  45 Jacobus F A Jansen  46 Christopher W Jenkins  31 Stephen J Johnston  47 Christoph Juchem  48 Alayar Kangarlu  49 Adam B Kerr  4 Karl Landheer  48 Thomas Lange  22 Phil Lee  50 Swati Rane Levendovszky  51 Catherine Limperopoulos  52 Feng Liu  49 William Lloyd  53 David J Lythgoe  54 Maro G Machizawa  32 Erin L MacMillan  55 Richard J Maddock  56 Andrei V Manzhurtsev  57 María L Martinez-Gudino  58 Jack J Miller  59 Heline Mirzakhanian  45 Marta Moreno-Ortega  49 Paul G Mullins  60 Shinichiro Nakajima  44 Jamie Near  61 Ralph Noeske  62 Wibeke Nordhøy  63 Georg Oeltzschner  1 Raul Osorio-Duran  58 Maria C G Otaduy  64 Erick H Pasaye  3 Ronald Peeters  65 Scott J Peltier  66 Ulrich Pilatus  67 Nenad Polomac  67 Eric C Porges  68 Subechhya Pradhan  52 James Joseph Prisciandaro  69 Nicolaas A Puts  70 Caroline D Rae  15 Francisco Reyes-Madrigal  23 Timothy P L Roberts  8 Caroline E Robertson  17 Jens T Rosenberg  71 Diana-Georgiana Rotaru  54 Ruth L O'Gorman Tuura  72 Muhammad G Saleh  73 Kristian Sandberg  11 Ryan Sangill  11 Keith Schembri  74 Anouk Schrantee  75 Natalia A Semenova  57 Debra Singel  76 Rouslan Sitnikov  77 Jolinda Smith  78 Yulu Song  35 Craig Stark  79 Diederick Stoffers  80 Stephan P Swinnen  43 Rongwen Tain  79 Costin Tanase  56 Sofie Tapper  1 Martin Tegenthoff  41 Thomas Thiel  81 Marc Thioux  82 Peter Truong  83 Pim van Dijk  82 Nolan Vella  74 Rishma Vidyasagar  84 Andrej Vovk  85 Guangbin Wang  35 Lars T Westlye  63 Timothy K Wilbur  51 William R Willoughby  86 Martin Wilson  87 Hans-Jörg Wittsack  88 Adam J Woods  68 Yen-Chien Wu  89 Junqian Xu  90 Maria Yanez Lopez  91 David K W Yeung  18 Qun Zhao  92 Xiaopeng Zhou  28 Gasper Zupan  85 Richard A E Edden  93
Affiliations

Frequency drift in MR spectroscopy at 3T

Steve C N Hui et al. Neuroimage. .

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'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: 3T; Frequency drift; Magnetic resonance spectroscopy (MRS); Multi-site; Multi-vendor; Press.

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Conflict of interest statement

Declaration of Competing Interest 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.

Figures

Fig 1
Fig. 1
a) Individual transients pre- and post-fMRI PRESS (plotted in blue and red, respectively) from one of the highest drift datasets. The frequency offset derived from modeling the water signals is plotted (middle). Three hundred sixty averages correspond to 30 min total scan duration. Panel (b) shows water offset traces for all 95 scanners before and after fMRI for GE (green and light blue), Philips (orange and brown) and Siemens (blue and purple). Panel (c) shows the pre-fMRI PRESS traces and the same period (5:20 min) for post-fMRI traces.
Fig 2
Fig. 2
a) Violin plots of mean absolute frequency offsets for all 95 scanners (median (solid line) and IQR (dashed line)); data from GE (green), Philips (orange) and Siemens (blue) are plotted. P-values show the mean values are significantly different before and after running the fMRI sequence. Panel (b) shows a scatterplot between pre-fMRI and early post-fMRI (first 5:20 min) with the confidence interval shaded in grey, in which a moderate correlation was observed.
Fig 3
Fig. 3
Frequency offsets from day 1 against day 2 a) before fMRI and b) after fMRI, for GE (green), Philips (orange) and Siemens (blue). Inserts show only those traces that remain within the gray box on the primary plot to allow for visualization of the lower-drift traces.
Fig 4
Fig. 4
Comparison of simulated spectra with frequency offsets applied between minimum and maximum drift for pre- and post-fMRI PRESS data. The minimum-drift case for each vendor (50% opacity) is overlaid with the maximum-drift case (opaque).
Fig 5
Fig. 5
Impact of linear drift. Panel (a) shows the change in simulated NAA signal height as a function of the range of linear drift. Panel (b) shows the simulated GABA integral changes as a function of the same linear drift, due to editing efficiency losses.
Fig 6
Fig. 6
Temperature change and water frequency offset before and after fMRI for the long PRESS acquisition. Panel (a) shows the corresponding gradient temperatures from sensors at different locations as well as the bore and scan room temperature. Panel (b) shows the change of frequency offsets after fMRI.

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