The traveling heads 2.0: Multicenter reproducibility of quantitative imaging methods at 7 Tesla

Voelker, Maximilian N.; Kraff, Oliver; Goerke, Steffen; Laun, Frederik Bernd; Hanspach, Jannis; Pine, Kerrin; Ehses, Philipp; Zaiß, Moritz; Liebert, Andrzej; Straub, Sina; Eckstein, Korbinian; Robinson, Simon; Nagel, Armin M.; Stefanescu, Maria R.; Wollrab, Astrid; Klix, Sabrina; Felder, Jörg; Hock, Michael; Bosch, Dario; Weiskopf, Nikolaus; Speck, Oliver; Ladd, Mark E.; Quick, Harald H.

doi:10.1016/j.neuroimage.2021.117910

Cited by 36 publications

(42 citation statements)

References 77 publications

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“…The overall higher CoV value of the MTR Rex (APT) contrast could be linked to a lower SNR of the APT in comparison to the rNOE effect. The observed CoV in µMTR Rex (rNOE) is much smaller than the variation between different scanner systems reported by Voelker et al 44 Unfortunately, the exact causes for the 2 outlier measurements of volunteer 2 could not be clearly identified. Because an increased value of MTR Rex (NOE) and MTR Rex (APT) is visible in both tissue types, it could indicate a change in the effective amplitude of the saturation train.…”

Section: Discussioncontrasting

confidence: 64%

“…The overall higher CoV value of the MTR Rex (APT) contrast could be linked to a lower SNR of the APT in comparison to the rNOE effect. The observed CoV in µMTR Rex (rNOE) is much smaller than the variation between different scanner systems reported by Voelker et al 44 Unfortunately, the exact causes for the F I G U R E 8 Boxplots presenting the µMTR Rex (APT) and µMTR Rex (rNOE) of MIMOSA FOCUS acquisitions without B + 1 correction (N corr = 0) and with B + 1 correction with N corr = 1:6 for WM and GM. A slight decrease in the boxplot size can be observed with the increase of N corr .…”

Section: Significant Difference For B +mentioning

confidence: 69%

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Whole‐brain quantitative CEST MRI at 7T using parallel transmission methods and correction

Liebert

Tkotz

Herrler

et al. 2021

Magnetic Resonance in Med

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Section: Discussioncontrasting

confidence: 64%

“…The overall higher CoV value of the MTR Rex (APT) contrast could be linked to a lower SNR of the APT in comparison to the rNOE effect. The observed CoV in µMTR Rex (rNOE) is much smaller than the variation between different scanner systems reported by Voelker et al 44 Unfortunately, the exact causes for the F I G U R E 8 Boxplots presenting the µMTR Rex (APT) and µMTR Rex (rNOE) of MIMOSA FOCUS acquisitions without B + 1 correction (N corr = 0) and with B + 1 correction with N corr = 1:6 for WM and GM. A slight decrease in the boxplot size can be observed with the increase of N corr .…”

Section: Significant Difference For B +mentioning

confidence: 69%

Whole‐brain quantitative CEST MRI at 7T using parallel transmission methods and correction

Liebert

Tkotz

Herrler

et al. 2021

Magnetic Resonance in Med

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“…Moreover, no dedicated B 0 and B 1 correction or correction for gradient delays was performed. Susceptibility mapping has been shown not to depend on B 1 inhomogeneties even in multi-center ultra-high field studies QSM ( Rua et al, 2020 ; Voelker et al, 2021 ), but the correction of dynamic B 0 fluctuations improves image quality in susceptibility-based methods and could thus enable a better characterization for multi-contrast approaches including QSM segmentation ( Jorge et al, 2020 ). However, MTR measurements are more sensitive to B 1 inhomogeneites and especially for multi-site studies, B 1 correction would be advisable ( Barker et al, 2005 ).…”

Section: Discussionmentioning

confidence: 99%

Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain

Schneider

Wagner

et al. 2021

Front. Neurosci.

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Objectives To characterize subcortical nuclei by multi-parametric quantitative magnetic resonance imaging.Materials and Methods: The following quantitative multiparametric MR data of five healthy volunteers were acquired on a 7T MRI system: 3D gradient echo (GRE) data for the calculation of quantitative susceptibility maps (QSM), GRE sequences with and without off-resonant magnetic transfer pulse for magnetization transfer ratio (MTR) calculation, a magnetization−prepared 2 rapid acquisition gradient echo sequence for T1 mapping, and (after a coil change) a density-adapted 3D radial pulse sequence for 23Na imaging. First, all data were co-registered to the GRE data, volumes of interest (VOIs) for 21 subcortical structures were drawn manually for each volunteer, and a combined voxel-wise analysis of the four MR contrasts (QSM, MTR, T1, 23Na) in each structure was conducted to assess the quantitative, MR value-based differentiability of structures. Second, a machine learning algorithm based on random forests was trained to automatically classify the groups of multi-parametric voxel values from each VOI according to their association to one of the 21 subcortical structures.Results The analysis of the integrated multimodal visualization of quantitative MR values in each structure yielded a successful classification among nuclei of the ascending reticular activation system (ARAS), the limbic system and the extrapyramidal system, while classification among (epi-)thalamic nuclei was less successful. The machine learning-based approach facilitated quantitative MR value-based structure classification especially in the group of extrapyramidal nuclei and reached an overall accuracy of 85% regarding all selected nuclei.Conclusion Multimodal quantitative MR enabled excellent differentiation of a wide spectrum of subcortical nuclei with reasonable accuracy and may thus enable sensitive detection of disease and nucleus-specific MR-based contrast alterations in the future.

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“…This holder allows flexible placement of the samples in three orthogonal orientations with regards to the static magnetic field in the MRI systems. The phantom is filled with oil (yellow, transparent) to provide homogeneous background signal the Nova Medical RF head coil is widely available at most 7T sites [21] and also proved to provide high reproducibility in multi-center brain imaging studies [22,23]. The selected MR sequences for all phantom experiments were a gradient echo and spin echo sequence according to ASTM F2119 [12] which are specified in Table 1.…”

Section: Experimental Mr Imaging Setupmentioning

confidence: 99%

Development and evaluation of a numerical simulation approach to predict metal artifacts from passive implants in MRI

Spronk

Kraff

Kreutner

et al. 2021

Magn Reson Mater Phy

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Objective This study presents the development and evaluation of a numerical approach to simulate artifacts of metallic implants in an MR environment that can be applied to improve the testing procedure for MR image artifacts in medical implants according to ASTM F2119. Methods The numerical approach is validated by comparing simulations and measurements of two metallic test objects made of titanium and stainless steel at three different field strengths (1.5T, 3T and 7T). The difference in artifact size and shape between the simulated and measured artifacts were evaluated. A trend analysis of the artifact sizes in relation to the field strength was performed. Results The numerical simulation approach shows high similarity (between 75% and 84%) of simulated and measured artifact sizes of metallic implants. Simulated and measured artifact sizes in relation to the field strength resulted in a calculation guideline to determine and predict the artifact size at one field strength (e.g., 3T or 7T) based on a measurement that was obtained at another field strength only (e.g. 1.5T). Conclusion This work presents a novel tool to improve the MR image artifact testing procedure of passive medical implants. With the help of this tool detailed artifact investigations can be performed, which would otherwise only be possible with substantial measurement effort on different MRI systems and field strengths.

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The traveling heads 2.0: Multicenter reproducibility of quantitative imaging methods at 7 Tesla

Cited by 36 publications

References 77 publications

Whole‐brain quantitative CEST MRI at 7T using parallel transmission methods and correction

Whole‐brain quantitative CEST MRI at 7T using parallel transmission methods and correction

Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain

Development and evaluation of a numerical simulation approach to predict metal artifacts from passive implants in MRI

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