Automated slice-specific z-shimming for functional magnetic resonance imaging of the human spinal cord

Kaptan, Merve, Vannesjo, S. Johanna, Mildner, Toralf, Horn, Ulrike, Hartley-Davies, Ronald, Oliva, Valeria, Brooks, Jonathan C. W. ORCID: https://orcid.org/0000-0003-3335-6209, Weiskopf, Nikolaus, Finsterbusch, Jürgen and Eippert, Falk (2022) Automated slice-specific z-shimming for functional magnetic resonance imaging of the human spinal cord. Human Brain Mapping. ISSN 1065-9471

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Abstract

Functional magnetic resonance imaging (fMRI) of the human spinal cord faces many challenges, such as signal loss due to local magnetic field inhomogeneities. This issue can be addressed with slice-specific z-shimming, which compensates for the dephasing effect of the inhomogeneities using a slice-specific gradient pulse. Here, we aim to address outstanding issues regarding this technique by evaluating its effects on several aspects that are directly relevant for spinal fMRI and by developing two automated procedures in order to improve upon the time-consuming and subjective nature of manual selection of z-shims: one procedure finds the z-shim that maximizes signal intensity in each slice of an EPI reference-scan and the other finds the through-slice field inhomogeneity for each EPI-slice in field map data and calculates the required compensation gradient moment. We demonstrate that the beneficial effects of z-shimming are apparent across different echo times, hold true for both the dorsal and ventral horn, and are also apparent in the temporal signal-to-noise ratio (tSNR) of EPI time-series data. Both of our automated approaches were faster than the manual approach, lead to significant improvements in gray matter tSNR compared to no z-shimming and resulted in beneficial effects that were stable across time. While the field-map-based approach performed slightly worse than the manual approach, the EPI-based approach performed as well as the manual one and was furthermore validated on an external corticospinal data-set (N > 100). Together, automated z-shimming may improve the data quality of future spinal fMRI studies and lead to increased reproducibility in longitudinal studies.

Item Type: Article
Additional Information: Funding information: Bundesministerium für Bildung und Forschung, Grant/Award Number: 01EW1711A & B; FP7 Ideas: European Research Council, Grant/Award Number: 616905; H2020 European Research Council, Grant/Award Numbers: 681094, 758974; Max-Planck-Gesellschaft; Medical Research Council, Grant/Award Number: MR/N026969/1; Wellcome Trust, Grant/Award Number: 203963/Z/16/Z
Uncontrolled Keywords: automated z-shim,functional magnetic resonance imaging,magnetic field inhomogeneities,signal loss,spinal cord,temporal signal-to-noise ratio,bold sensitivity losses,of-the-art,signal losses,optimized epi,gradient-echo,compensation,fmri,field,connectivity,single,anatomy,radiological and ultrasound technology,radiology nuclear medicine and imaging,neurology,clinical neurology ,/dk/atira/pure/subjectarea/asjc/2700/2702
Faculty \ School: Faculty of Social Sciences > School of Psychology
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Depositing User: LivePure Connector
Date Deposited: 07 Sep 2022 11:30
Last Modified: 24 Oct 2022 09:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/87778
DOI: 10.1002/hbm.26018

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