Cargando…
Ultrafast 3D Bloch–Siegert B [Formula: see text] ‐mapping using variational modeling
PURPOSE: Highly accelerated B [Formula: see text] ‐mapping based on the Bloch–Siegert shift to allow 3D acquisitions even within a brief period of a single breath‐hold. THEORY AND METHODS: The B [Formula: see text] dependent Bloch–Siegert phase shift is measured within a highly subsampled 3D‐volume...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6491998/ https://www.ncbi.nlm.nih.gov/pubmed/30444294 http://dx.doi.org/10.1002/mrm.27434 |
_version_ | 1783415061981691904 |
---|---|
author | Lesch, Andreas Schlöegl, Matthias Holler, Martin Bredies, Kristian Stollberger, Rudolf |
author_facet | Lesch, Andreas Schlöegl, Matthias Holler, Martin Bredies, Kristian Stollberger, Rudolf |
author_sort | Lesch, Andreas |
collection | PubMed |
description | PURPOSE: Highly accelerated B [Formula: see text] ‐mapping based on the Bloch–Siegert shift to allow 3D acquisitions even within a brief period of a single breath‐hold. THEORY AND METHODS: The B [Formula: see text] dependent Bloch–Siegert phase shift is measured within a highly subsampled 3D‐volume and reconstructed using a two‐step variational approach, exploiting the different spatial distribution of morphology and B [Formula: see text] ‐field. By appropriate variable substitution the basic non‐convex optimization problem is transformed in a sequential solution of two convex optimization problems with a total generalized variation (TGV) regularization for the morphology part and a smoothness constraint for the B [Formula: see text] ‐field. The method is evaluated on 3D in vivo data with retro‐ and prospective subsampling. The reconstructed B [Formula: see text] ‐maps are compared to a zero‐padded low resolution reconstruction and a fully sampled reference. RESULTS: The reconstructed B [Formula: see text] ‐field maps are in high accordance to the reference for all measurements with a mean error below 1% and a maximum of about 4% for acceleration factors up to 100. The minimal error for different sampling patterns was achieved by sampling a dense region in k‐space center with acquisition times of around 10–12 s for 3D‐acquistions. CONCLUSIONS: The proposed variational approach enables highly accelerated 3D acquisitions of Bloch–Siegert data and thus full liver coverage in a single breath hold. |
format | Online Article Text |
id | pubmed-6491998 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64919982019-05-06 Ultrafast 3D Bloch–Siegert B [Formula: see text] ‐mapping using variational modeling Lesch, Andreas Schlöegl, Matthias Holler, Martin Bredies, Kristian Stollberger, Rudolf Magn Reson Med Full Papers—Imaging Methodology PURPOSE: Highly accelerated B [Formula: see text] ‐mapping based on the Bloch–Siegert shift to allow 3D acquisitions even within a brief period of a single breath‐hold. THEORY AND METHODS: The B [Formula: see text] dependent Bloch–Siegert phase shift is measured within a highly subsampled 3D‐volume and reconstructed using a two‐step variational approach, exploiting the different spatial distribution of morphology and B [Formula: see text] ‐field. By appropriate variable substitution the basic non‐convex optimization problem is transformed in a sequential solution of two convex optimization problems with a total generalized variation (TGV) regularization for the morphology part and a smoothness constraint for the B [Formula: see text] ‐field. The method is evaluated on 3D in vivo data with retro‐ and prospective subsampling. The reconstructed B [Formula: see text] ‐maps are compared to a zero‐padded low resolution reconstruction and a fully sampled reference. RESULTS: The reconstructed B [Formula: see text] ‐field maps are in high accordance to the reference for all measurements with a mean error below 1% and a maximum of about 4% for acceleration factors up to 100. The minimal error for different sampling patterns was achieved by sampling a dense region in k‐space center with acquisition times of around 10–12 s for 3D‐acquistions. CONCLUSIONS: The proposed variational approach enables highly accelerated 3D acquisitions of Bloch–Siegert data and thus full liver coverage in a single breath hold. John Wiley and Sons Inc. 2018-10-12 2019-02 /pmc/articles/PMC6491998/ /pubmed/30444294 http://dx.doi.org/10.1002/mrm.27434 Text en © 2018 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 Lesch, Andreas Schlöegl, Matthias Holler, Martin Bredies, Kristian Stollberger, Rudolf Ultrafast 3D Bloch–Siegert B [Formula: see text] ‐mapping using variational modeling |
title | Ultrafast 3D Bloch–Siegert B [Formula: see text] ‐mapping using variational modeling |
title_full | Ultrafast 3D Bloch–Siegert B [Formula: see text] ‐mapping using variational modeling |
title_fullStr | Ultrafast 3D Bloch–Siegert B [Formula: see text] ‐mapping using variational modeling |
title_full_unstemmed | Ultrafast 3D Bloch–Siegert B [Formula: see text] ‐mapping using variational modeling |
title_short | Ultrafast 3D Bloch–Siegert B [Formula: see text] ‐mapping using variational modeling |
title_sort | ultrafast 3d bloch–siegert b [formula: see text] ‐mapping using variational modeling |
topic | Full Papers—Imaging Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6491998/ https://www.ncbi.nlm.nih.gov/pubmed/30444294 http://dx.doi.org/10.1002/mrm.27434 |
work_keys_str_mv | AT leschandreas ultrafast3dblochsiegertbformulaseetextmappingusingvariationalmodeling AT schloeglmatthias ultrafast3dblochsiegertbformulaseetextmappingusingvariationalmodeling AT hollermartin ultrafast3dblochsiegertbformulaseetextmappingusingvariationalmodeling AT bredieskristian ultrafast3dblochsiegertbformulaseetextmappingusingvariationalmodeling AT stollbergerrudolf ultrafast3dblochsiegertbformulaseetextmappingusingvariationalmodeling |