Cargando…

Template‐based field map prediction for rapid whole brain B(0) shimming

PURPOSE: In typical MRI protocols, time is spent acquiring a field map to calculate the shim settings for best image quality. We propose a fast template‐based field map prediction method that yields near‐optimal shims without measuring the field. METHODS: The template‐based prediction method uses pr...

Descripción completa

Detalles Bibliográficos
Autores principales: Shi, Yuhang, Vannesjo, S. Johanna, Miller, Karla L., Clare, Stuart
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5900895/
https://www.ncbi.nlm.nih.gov/pubmed/29193340
http://dx.doi.org/10.1002/mrm.27020
_version_ 1783314501748129792
author Shi, Yuhang
Vannesjo, S. Johanna
Miller, Karla L.
Clare, Stuart
author_facet Shi, Yuhang
Vannesjo, S. Johanna
Miller, Karla L.
Clare, Stuart
author_sort Shi, Yuhang
collection PubMed
description PURPOSE: In typical MRI protocols, time is spent acquiring a field map to calculate the shim settings for best image quality. We propose a fast template‐based field map prediction method that yields near‐optimal shims without measuring the field. METHODS: The template‐based prediction method uses prior knowledge of the B(0) distribution in the human brain, based on a large database of field maps acquired from different subjects, together with subject‐specific structural information from a quick localizer scan. The shimming performance of using the template‐based prediction is evaluated in comparison to a range of potential fast shimming methods. RESULTS: Static B(0) shimming based on predicted field maps performed almost as well as shimming based on individually measured field maps. In experimental evaluations at 7 T, the proposed approach yielded a residual field standard deviation in the brain of on average 59 Hz, compared with 50 Hz using measured field maps and 176 Hz using no subject‐specific shim. CONCLUSIONS: This work demonstrates that shimming based on predicted field maps is feasible. The field map prediction accuracy could potentially be further improved by generating the template from a subset of subjects, based on parameters such as head rotation and body mass index. Magn Reson Med 80:171–180, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
format Online
Article
Text
id pubmed-5900895
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-59008952018-04-23 Template‐based field map prediction for rapid whole brain B(0) shimming Shi, Yuhang Vannesjo, S. Johanna Miller, Karla L. Clare, Stuart Magn Reson Med Full Papers—Imaging Methodology PURPOSE: In typical MRI protocols, time is spent acquiring a field map to calculate the shim settings for best image quality. We propose a fast template‐based field map prediction method that yields near‐optimal shims without measuring the field. METHODS: The template‐based prediction method uses prior knowledge of the B(0) distribution in the human brain, based on a large database of field maps acquired from different subjects, together with subject‐specific structural information from a quick localizer scan. The shimming performance of using the template‐based prediction is evaluated in comparison to a range of potential fast shimming methods. RESULTS: Static B(0) shimming based on predicted field maps performed almost as well as shimming based on individually measured field maps. In experimental evaluations at 7 T, the proposed approach yielded a residual field standard deviation in the brain of on average 59 Hz, compared with 50 Hz using measured field maps and 176 Hz using no subject‐specific shim. CONCLUSIONS: This work demonstrates that shimming based on predicted field maps is feasible. The field map prediction accuracy could potentially be further improved by generating the template from a subset of subjects, based on parameters such as head rotation and body mass index. Magn Reson Med 80:171–180, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. John Wiley and Sons Inc. 2017-11-28 2018-07 /pmc/articles/PMC5900895/ /pubmed/29193340 http://dx.doi.org/10.1002/mrm.27020 Text en © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Full Papers—Imaging Methodology
Shi, Yuhang
Vannesjo, S. Johanna
Miller, Karla L.
Clare, Stuart
Template‐based field map prediction for rapid whole brain B(0) shimming
title Template‐based field map prediction for rapid whole brain B(0) shimming
title_full Template‐based field map prediction for rapid whole brain B(0) shimming
title_fullStr Template‐based field map prediction for rapid whole brain B(0) shimming
title_full_unstemmed Template‐based field map prediction for rapid whole brain B(0) shimming
title_short Template‐based field map prediction for rapid whole brain B(0) shimming
title_sort template‐based field map prediction for rapid whole brain b(0) shimming
topic Full Papers—Imaging Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5900895/
https://www.ncbi.nlm.nih.gov/pubmed/29193340
http://dx.doi.org/10.1002/mrm.27020
work_keys_str_mv AT shiyuhang templatebasedfieldmappredictionforrapidwholebrainb0shimming
AT vannesjosjohanna templatebasedfieldmappredictionforrapidwholebrainb0shimming
AT millerkarlal templatebasedfieldmappredictionforrapidwholebrainb0shimming
AT clarestuart templatebasedfieldmappredictionforrapidwholebrainb0shimming