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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 152693, 9 pages
Review Article

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

Bigong Wang1,2 and Liang Li1,2

1Department of Engineering Physics, Tsinghua University, Beijing 100084, China
2Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing 100084, China

Received 5 October 2014; Revised 12 January 2015; Accepted 6 April 2015

Academic Editor: Valeri Makarov

Copyright © 2015 Bigong Wang and Liang Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.