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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...

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Autores principales: Wu, Wenchuan, Koopmans, Peter J., Andersson, Jesper L.R., Miller, Karla L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
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.
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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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AT anderssonjesperlr diffusionaccelerationwithgaussianprocessestimatedreconstructiondager
AT millerkarlal diffusionaccelerationwithgaussianprocessestimatedreconstructiondager