Cargando…
Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints
Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t)-space measurements,...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375795/ https://www.ncbi.nlm.nih.gov/pubmed/28273827 http://dx.doi.org/10.3390/s17030509 |
_version_ | 1782519058438029312 |
---|---|
author | Liu, Ryan Wen Shi, Lin Yu, Simon Chun Ho Xiong, Naixue Wang, Defeng |
author_facet | Liu, Ryan Wen Shi, Lin Yu, Simon Chun Ho Xiong, Naixue Wang, Defeng |
author_sort | Liu, Ryan Wen |
collection | PubMed |
description | Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t)-space measurements, commonly referred to as big data. The purpose of this work is to accelerate big medical data acquisition in dynamic MRI by developing a non-convex minimization framework. In particular, to overcome the inherent speed limitation, both non-convex low-rank and sparsity constraints were combined to accelerate the dynamic imaging. However, the non-convex constraints make the dynamic reconstruction problem difficult to directly solve through the commonly-used numerical methods. To guarantee solution efficiency and stability, a numerical algorithm based on Alternating Direction Method of Multipliers (ADMM) is proposed to solve the resulting non-convex optimization problem. ADMM decomposes the original complex optimization problem into several simple sub-problems. Each sub-problem has a closed-form solution or could be efficiently solved using existing numerical methods. It has been proven that the quality of images reconstructed from fewer measurements can be significantly improved using non-convex minimization. Numerous experiments have been conducted on two in vivo cardiac datasets to compare the proposed method with several state-of-the-art imaging methods. Experimental results illustrated that the proposed method could guarantee the superior imaging performance in terms of quantitative and visual image quality assessments. |
format | Online Article Text |
id | pubmed-5375795 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-53757952017-04-10 Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints Liu, Ryan Wen Shi, Lin Yu, Simon Chun Ho Xiong, Naixue Wang, Defeng Sensors (Basel) Article Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t)-space measurements, commonly referred to as big data. The purpose of this work is to accelerate big medical data acquisition in dynamic MRI by developing a non-convex minimization framework. In particular, to overcome the inherent speed limitation, both non-convex low-rank and sparsity constraints were combined to accelerate the dynamic imaging. However, the non-convex constraints make the dynamic reconstruction problem difficult to directly solve through the commonly-used numerical methods. To guarantee solution efficiency and stability, a numerical algorithm based on Alternating Direction Method of Multipliers (ADMM) is proposed to solve the resulting non-convex optimization problem. ADMM decomposes the original complex optimization problem into several simple sub-problems. Each sub-problem has a closed-form solution or could be efficiently solved using existing numerical methods. It has been proven that the quality of images reconstructed from fewer measurements can be significantly improved using non-convex minimization. Numerous experiments have been conducted on two in vivo cardiac datasets to compare the proposed method with several state-of-the-art imaging methods. Experimental results illustrated that the proposed method could guarantee the superior imaging performance in terms of quantitative and visual image quality assessments. MDPI 2017-03-03 /pmc/articles/PMC5375795/ /pubmed/28273827 http://dx.doi.org/10.3390/s17030509 Text en © 2017 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Ryan Wen Shi, Lin Yu, Simon Chun Ho Xiong, Naixue Wang, Defeng Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints |
title | Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints |
title_full | Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints |
title_fullStr | Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints |
title_full_unstemmed | Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints |
title_short | Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints |
title_sort | reconstruction of undersampled big dynamic mri data using non-convex low-rank and sparsity constraints |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375795/ https://www.ncbi.nlm.nih.gov/pubmed/28273827 http://dx.doi.org/10.3390/s17030509 |
work_keys_str_mv | AT liuryanwen reconstructionofundersampledbigdynamicmridatausingnonconvexlowrankandsparsityconstraints AT shilin reconstructionofundersampledbigdynamicmridatausingnonconvexlowrankandsparsityconstraints AT yusimonchunho reconstructionofundersampledbigdynamicmridatausingnonconvexlowrankandsparsityconstraints AT xiongnaixue reconstructionofundersampledbigdynamicmridatausingnonconvexlowrankandsparsityconstraints AT wangdefeng reconstructionofundersampledbigdynamicmridatausingnonconvexlowrankandsparsityconstraints |