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Fast Compressed Sensing of 3D Radial T(1) Mapping with Different Sparse and Low-Rank Models

Knowledge of the relative performance of the well-known sparse and low-rank compressed sensing models with 3D radial quantitative magnetic resonance imaging acquisitions is limited. We use 3D radial T(1) relaxation time mapping data to compare the total variation, low-rank, and Huber penalty functio...

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Autores principales: Paajanen, Antti, Hanhela, Matti, Hänninen, Nina, Nykänen, Olli, Kolehmainen, Ville, Nissi, Mikko J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10455972/
https://www.ncbi.nlm.nih.gov/pubmed/37623683
http://dx.doi.org/10.3390/jimaging9080151
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author Paajanen, Antti
Hanhela, Matti
Hänninen, Nina
Nykänen, Olli
Kolehmainen, Ville
Nissi, Mikko J.
author_facet Paajanen, Antti
Hanhela, Matti
Hänninen, Nina
Nykänen, Olli
Kolehmainen, Ville
Nissi, Mikko J.
author_sort Paajanen, Antti
collection PubMed
description Knowledge of the relative performance of the well-known sparse and low-rank compressed sensing models with 3D radial quantitative magnetic resonance imaging acquisitions is limited. We use 3D radial T(1) relaxation time mapping data to compare the total variation, low-rank, and Huber penalty function approaches to regularization to provide insights into the relative performance of these image reconstruction models. Simulation and ex vivo specimen data were used to determine the best compressed sensing model as measured by normalized root mean squared error and structural similarity index. The large-scale compressed sensing models were solved by combining a GPU implementation of a preconditioned primal-dual proximal splitting algorithm to provide high-quality T(1) maps within a feasible computation time. The model combining spatial total variation and locally low-rank regularization yielded the best performance, followed closely by the model combining spatial and contrast dimension total variation. Computation times ranged from 2 to 113 min, with the low-rank approaches taking the most time. The differences between the compressed sensing models are not necessarily large, but the overall performance is heavily dependent on the imaged object.
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spelling pubmed-104559722023-08-26 Fast Compressed Sensing of 3D Radial T(1) Mapping with Different Sparse and Low-Rank Models Paajanen, Antti Hanhela, Matti Hänninen, Nina Nykänen, Olli Kolehmainen, Ville Nissi, Mikko J. J Imaging Article Knowledge of the relative performance of the well-known sparse and low-rank compressed sensing models with 3D radial quantitative magnetic resonance imaging acquisitions is limited. We use 3D radial T(1) relaxation time mapping data to compare the total variation, low-rank, and Huber penalty function approaches to regularization to provide insights into the relative performance of these image reconstruction models. Simulation and ex vivo specimen data were used to determine the best compressed sensing model as measured by normalized root mean squared error and structural similarity index. The large-scale compressed sensing models were solved by combining a GPU implementation of a preconditioned primal-dual proximal splitting algorithm to provide high-quality T(1) maps within a feasible computation time. The model combining spatial total variation and locally low-rank regularization yielded the best performance, followed closely by the model combining spatial and contrast dimension total variation. Computation times ranged from 2 to 113 min, with the low-rank approaches taking the most time. The differences between the compressed sensing models are not necessarily large, but the overall performance is heavily dependent on the imaged object. MDPI 2023-07-26 /pmc/articles/PMC10455972/ /pubmed/37623683 http://dx.doi.org/10.3390/jimaging9080151 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Paajanen, Antti
Hanhela, Matti
Hänninen, Nina
Nykänen, Olli
Kolehmainen, Ville
Nissi, Mikko J.
Fast Compressed Sensing of 3D Radial T(1) Mapping with Different Sparse and Low-Rank Models
title Fast Compressed Sensing of 3D Radial T(1) Mapping with Different Sparse and Low-Rank Models
title_full Fast Compressed Sensing of 3D Radial T(1) Mapping with Different Sparse and Low-Rank Models
title_fullStr Fast Compressed Sensing of 3D Radial T(1) Mapping with Different Sparse and Low-Rank Models
title_full_unstemmed Fast Compressed Sensing of 3D Radial T(1) Mapping with Different Sparse and Low-Rank Models
title_short Fast Compressed Sensing of 3D Radial T(1) Mapping with Different Sparse and Low-Rank Models
title_sort fast compressed sensing of 3d radial t(1) mapping with different sparse and low-rank models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10455972/
https://www.ncbi.nlm.nih.gov/pubmed/37623683
http://dx.doi.org/10.3390/jimaging9080151
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