19 February 2018 Sparse-view photoacoustic tomography using virtual parallel-projections and spatially adaptive filtering
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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.
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Yihan Wang, Tong Lu, Wenbo Wan, Lingling Liu, Songhe Zhang, Jiao Li, Huijuan Zhao, Feng Gao, "Sparse-view photoacoustic tomography using virtual parallel-projections and spatially adaptive filtering", Proc. SPIE 10494, Photons Plus Ultrasound: Imaging and Sensing 2018, 104944T (19 February 2018); doi: 10.1117/12.2291371; https://doi.org/10.1117/12.2291371
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