Automated High-Order Shimming for Neuroimaging Studies

Jia Xu; Baolian Yang; Douglas Kelley; Vincent A Magnotta

doi:10.3390/tomography9060168

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Automated High-Order Shimming for Neuroimaging Studies

Journal article

Open access

Peer reviewed

Automated High-Order Shimming for Neuroimaging Studies

Jia Xu, Baolian Yang, Douglas Kelley and Vincent A Magnotta

Tomography (Ann Arbor), Vol.9(6), pp.2148-2157

12/01/2023

DOI: 10.3390/tomography9060168

PMCID: PMC10748357

PMID: 38133072

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https://doi.org/10.3390/tomography9060168View

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Abstract

B0 inhomogeneity presents a significant challenge in MRI and MR spectroscopy, particularly at high-field strengths, leading to image distortion, signal loss, and spectral broadening. Existing high-order shimming methods can alleviate these issues but often require time-consuming and subjective manual selection of regions of interest (ROIs). To address this, we proposed an automated high-order shimming (autoHOS) method, incorporating deep-learning-based brain extraction and image-based high-order shimming. This approach performs automated real-time brain extraction to define the ROI of the field map to be used in the shimming algorithm. The shimming performance of autoHOS was assessed through in vivo echo-planar imaging (EPI) and spectroscopic studies at both 3T and 7T field strengths. AutoHOS outperforms linear shimming and manual high-order shimming, enhancing both the image and spectral quality by reducing the EPI image distortion and narrowing the MRS spectral lineshapes. Therefore, autoHOS demonstrated a significant improvement in correcting B0 inhomogeneity while eliminating the need for additional user interaction.

fMRI

image quality

B0 inhomogeneity

high-order shimming

magnetic resonance spectroscopy

spectral lineshape

EPI

Mrs.

Details

Title: Subtitle: Automated High-Order Shimming for Neuroimaging Studies
Creators: Jia Xu - University of Iowa, Radiology
Baolian Yang
Douglas Kelley
Vincent A Magnotta - Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
Resource Type: Journal article
Publication Details: Tomography (Ann Arbor), Vol.9(6), pp.2148-2157
DOI: 10.3390/tomography9060168
PMID: 38133072
PMCID: PMC10748357
NLM abbreviation: Tomography
eISSN: 2379-139X
Grant note: S10OD025025; S10RR028821; P50HD103556 / NIH HHS
Language: English
Date published: 12/01/2023
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Psychiatry; Iowa Neuroscience Institute
Record Identifier: 9984533453302771

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