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Proceedings Paper

Sparse-view photoacoustic tomography using virtual parallel-projections and spatially adaptive filtering
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Paper Abstract

To fully realize the potential of photoacoustic tomography (PAT) in preclinical and clinical applications, rapid measurements and robust reconstructions are needed. Sparse-view measurements have been adopted effectively to accelerate the data acquisition. However, since the reconstruction from the sparse-view sampling data is challenging, both of the effective measurement and the appropriate reconstruction should be taken into account. In this study, we present an iterative sparse-view PAT reconstruction scheme where a virtual parallel-projection concept matching for the proposed measurement condition is introduced to help to achieve the “compressive sensing” procedure of the reconstruction, and meanwhile the spatially adaptive filtering fully considering the a priori information of the mutually similar blocks existing in natural images is introduced to effectively recover the partial unknown coefficients in the transformed domain. Therefore, the sparse-view PAT images can be reconstructed with higher quality compared with the results obtained by the universal back-projection (UBP) algorithm in the same sparse-view cases. The proposed approach has been validated by simulation experiments, which exhibits desirable performances in image fidelity even from a small number of measuring positions.

Paper Details

Date Published: 19 February 2018
PDF: 7 pages
Proc. SPIE 10494, Photons Plus Ultrasound: Imaging and Sensing 2018, 104944T (19 February 2018); doi: 10.1117/12.2291371
Show Author Affiliations
Yihan Wang, Tianjin Univ. (China)
Tong Lu, Tianjin Univ. (China)
Wenbo Wan, Tianjin Univ. (China)
Lingling Liu, Tianjin Univ. (China)
Songhe Zhang, Tianjin Univ. (China)
Jiao Li, Tianjin Univ. (China)
Huijuan Zhao, Tianjin Univ. (China)
Feng Gao, Tianjin Univ. (China)

Published in SPIE Proceedings Vol. 10494:
Photons Plus Ultrasound: Imaging and Sensing 2018
Alexander A. Oraevsky; Lihong V. Wang, Editor(s)

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