Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction

Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction

As an implementation of compressive sensing (CS), dual-dictionary learning (DDL) method provides an ideal access to restore signals of two related dictionaries and sparse representation. It has been proven that this method performs well in medical image reconstruction with highly undersampled data,...

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Main Authors: Bigong Wang, Liang Li
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Computational and Mathematical Methods in Medicine
Online Access:http://dx.doi.org/10.1155/2015/152693
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spelling doaj-ea07abdc93f644358cb1a3c7069bfa212020-11-24T22:43:48ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182015-01-01201510.1155/2015/152693152693Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and ReconstructionBigong Wang0Liang Li1Department of Engineering Physics, Tsinghua University, Beijing 100084, ChinaDepartment of Engineering Physics, Tsinghua University, Beijing 100084, ChinaAs an implementation of compressive sensing (CS), dual-dictionary learning (DDL) method provides an ideal access to restore signals of two related dictionaries and sparse representation. It has been proven that this method performs well in medical image reconstruction with highly undersampled data, especially for multimodality imaging like CT-MRI hybrid reconstruction. Because of its outstanding strength, short signal acquisition time, and low radiation dose, DDL has allured a broad interest in both academic and industrial fields. Here in this review article, we summarize DDL’s development history, conclude the latest advance, and also discuss its role in the future directions and potential applications in medical imaging. Meanwhile, this paper points out that DDL is still in the initial stage, and it is necessary to make further studies to improve this method, especially in dictionary training.http://dx.doi.org/10.1155/2015/152693
collection DOAJ
language English
format Article
sources DOAJ
author Bigong Wang
Liang Li
spellingShingle Bigong Wang
Liang Li
Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction
Computational and Mathematical Methods in Medicine
author_facet Bigong Wang
Liang Li
author_sort Bigong Wang
title Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction
title_short Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction
title_full Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction
title_fullStr Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction
title_full_unstemmed Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction
title_sort recent development of dual-dictionary learning approach in medical image analysis and reconstruction
publisher Hindawi Limited
series Computational and Mathematical Methods in Medicine
issn 1748-670X
1748-6718
publishDate 2015-01-01
description As an implementation of compressive sensing (CS), dual-dictionary learning (DDL) method provides an ideal access to restore signals of two related dictionaries and sparse representation. It has been proven that this method performs well in medical image reconstruction with highly undersampled data, especially for multimodality imaging like CT-MRI hybrid reconstruction. Because of its outstanding strength, short signal acquisition time, and low radiation dose, DDL has allured a broad interest in both academic and industrial fields. Here in this review article, we summarize DDL’s development history, conclude the latest advance, and also discuss its role in the future directions and potential applications in medical imaging. Meanwhile, this paper points out that DDL is still in the initial stage, and it is necessary to make further studies to improve this method, especially in dictionary training.
url http://dx.doi.org/10.1155/2015/152693
work_keys_str_mv AT bigongwang recentdevelopmentofdualdictionarylearningapproachinmedicalimageanalysisandreconstruction
AT liangli recentdevelopmentofdualdictionarylearningapproachinmedicalimageanalysisandreconstruction
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