Journal
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
Volume 748, Issue -, Pages 26-32Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.nima.2014.02.026
Keywords
Compressive sampling (CS); Computed tomography (CT); Sparse representation; Incoherent sampling; Reconstruction algorithm
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Funding
- Special Fund Project for Development of Strategic Emerging Industry of Shenzhen in China [JCYJ20130401170306796]
- Basic Research Program of Shenzhen in China [JC201005280581A, JC201105190923A]
- National Natural Science Foundation of China [61102161, 61102043]
- National Science & Technology Pillar Program of China [2012BAI13B04]
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Since Donoho and Candes et al. published their groundbreaking work on compressive sampling or compressive sensing (CS), CS theory has attracted a lot of attention and become a hot topic, especially in biomedical imaging. Specifically, some CS based methods have been developed to enable accurate reconstruction from sparse data in computed tomography (CT) imaging. In this paper, we will review the progress in CS based CT from aspects of three fundamental requirements of CS: sparse representation, incoherent sampling and reconstruction algorithm. In addition, some potential applications of compressive sampling in CT are introduced. (C) 2014 Elsevier B.V. All rights reserved.
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