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Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER)
PURPOSE: Image acceleration provides multiple benefits to diffusion MRI, with in‐plane acceleration reducing distortion and slice‐wise acceleration increasing the number of directions that can be acquired in a given scan time. However, as acceleration factors increase, the reconstruction problem bec...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6492188/ https://www.ncbi.nlm.nih.gov/pubmed/30825243 http://dx.doi.org/10.1002/mrm.27699 |
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author | Wu, Wenchuan Koopmans, Peter J. Andersson, Jesper L.R. Miller, Karla L. |
author_facet | Wu, Wenchuan Koopmans, Peter J. Andersson, Jesper L.R. Miller, Karla L. |
author_sort | Wu, Wenchuan |
collection | PubMed |
description | PURPOSE: Image acceleration provides multiple benefits to diffusion MRI, with in‐plane acceleration reducing distortion and slice‐wise acceleration increasing the number of directions that can be acquired in a given scan time. However, as acceleration factors increase, the reconstruction problem becomes ill‐conditioned, particularly when using both in‐plane acceleration and simultaneous multislice imaging. In this work, we develop a novel reconstruction method for in vivo MRI acquisition that provides acceleration beyond what conventional techniques can achieve. THEORY AND METHODS: We propose to constrain the reconstruction in the spatial (k) domain by incorporating information from the angular (q) domain. This approach exploits smoothness of the signal in q‐space using Gaussian processes, as has previously been exploited in post‐reconstruction analysis. We demonstrate in‐plane undersampling exceeding the theoretical parallel imaging limits, and simultaneous multislice combined with in‐plane undersampling at a total factor of 12. This reconstruction is cast within a Bayesian framework that incorporates estimation of smoothness hyper‐parameters, with no need for manual tuning. RESULTS: Simulations and in vivo results demonstrate superior performance of the proposed method compared with conventional parallel imaging methods. These improvements are achieved without loss of spatial or angular resolution and require only a minor modification to standard pulse sequences. CONCLUSION: The proposed method provides improvements over existing methods for diffusion acceleration, particularly for high simultaneous multislice acceleration with in‐plane undersampling. |
format | Online Article Text |
id | pubmed-6492188 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64921882019-05-07 Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER) Wu, Wenchuan Koopmans, Peter J. Andersson, Jesper L.R. Miller, Karla L. Magn Reson Med Full Papers—Imaging Methodology PURPOSE: Image acceleration provides multiple benefits to diffusion MRI, with in‐plane acceleration reducing distortion and slice‐wise acceleration increasing the number of directions that can be acquired in a given scan time. However, as acceleration factors increase, the reconstruction problem becomes ill‐conditioned, particularly when using both in‐plane acceleration and simultaneous multislice imaging. In this work, we develop a novel reconstruction method for in vivo MRI acquisition that provides acceleration beyond what conventional techniques can achieve. THEORY AND METHODS: We propose to constrain the reconstruction in the spatial (k) domain by incorporating information from the angular (q) domain. This approach exploits smoothness of the signal in q‐space using Gaussian processes, as has previously been exploited in post‐reconstruction analysis. We demonstrate in‐plane undersampling exceeding the theoretical parallel imaging limits, and simultaneous multislice combined with in‐plane undersampling at a total factor of 12. This reconstruction is cast within a Bayesian framework that incorporates estimation of smoothness hyper‐parameters, with no need for manual tuning. RESULTS: Simulations and in vivo results demonstrate superior performance of the proposed method compared with conventional parallel imaging methods. These improvements are achieved without loss of spatial or angular resolution and require only a minor modification to standard pulse sequences. CONCLUSION: The proposed method provides improvements over existing methods for diffusion acceleration, particularly for high simultaneous multislice acceleration with in‐plane undersampling. John Wiley and Sons Inc. 2019-03-01 2019-07 /pmc/articles/PMC6492188/ /pubmed/30825243 http://dx.doi.org/10.1002/mrm.27699 Text en © 2019 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 Wu, Wenchuan Koopmans, Peter J. Andersson, Jesper L.R. Miller, Karla L. Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER) |
title | Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER) |
title_full | Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER) |
title_fullStr | Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER) |
title_full_unstemmed | Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER) |
title_short | Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER) |
title_sort | diffusion acceleration with gaussian process estimated reconstruction (dager) |
topic | Full Papers—Imaging Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6492188/ https://www.ncbi.nlm.nih.gov/pubmed/30825243 http://dx.doi.org/10.1002/mrm.27699 |
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