Journal Article FZJ-2020-04538

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Efficient eddy current characterization using a 2D image‐based sampling scheme and a model‐based fitting approach

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2021
Wiley-Liss New York, NY [u.a.]

Magnetic resonance in medicine 85(5), 2892-2903 () [10.1002/mrm.28597]

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Abstract: PurposeTo propose two innovations to existing eddy current characterization techniques, which include (1) an efficient spatio‐temporal sampling scheme and (2) a model‐based fitting of spherical harmonic eddy current components.Theory and MethodsThis work introduces a three‐plane 2D image‐based acquisition scheme to efficiently sample eddy current fields. Additionally, a model‐based spherical harmonic decomposition is presented, which reduces fitting noise using a rank minimization to impose an exponential decay on the eddy current amplitude evolution. Both techniques are applied in combination and analyzed in simulations for their applicability in reconstructing suitable pre‐emphasis parameters. In a proof‐of‐concept measurement, the routine is tested for its propriety to correctly quantify user‐defined field dynamics. Furthermore, based on acquired precompensation and postcompensation eddy current data, the suitability of pre‐emphasis parameters calculated based on the proposed technique is evaluated.ResultsSimulation results derived from 500 data sets demonstrate the applicability of the acquisition scheme for the spatio‐temporal sampling of eddy current fields. Compared with a conventional data processing strategy, the proposed model‐based approach yields pre‐emphasis parameters that reduce the average maximum residual field offset within a 10‐cm‐diameter spherical volume from 3.17 Hz to 0.58 Hz. Experimental data prove the proposed routine to be suitable to measure and effectively compensate for eddy currents within 10 minutes of acquisition time.ConclusionThe proposed framework was found to be well‐suited to efficiently characterize and compensate for eddy current fields in a one‐time calibration effort. It can be applied to facilitate pre‐emphasis implementations, such as for dynamic B0 shimming applications

Classification:

Contributing Institute(s):
  1. Physik der Medizinischen Bildgebung (INM-4)
  2. Jara-Institut Quantum Information (INM-11)
  3. Jülich-Aachen Research Alliance - Translational Brain Medicine (JARA-BRAIN)
Research Program(s):
  1. 5253 - Neuroimaging (POF4-525) (POF4-525)

Appears in the scientific report 2021
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Medline ; Creative Commons Attribution CC BY 4.0 ; OpenAccess ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Current Contents - Life Sciences ; DEAL Wiley ; Essential Science Indicators ; IF < 5 ; JCR ; NationallizenzNationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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 Record created 2020-11-17, last modified 2022-09-30


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