Open AccessEnhancing sparse-view photoacoustic tomography with combined virtually parallel projecting and spatially adaptive filtering
Author(s) -
Yihan Wang,
Tong Lü,
Jiao Li,
Wenbo Wan,
Wenjuan Ma,
Limin Zhang,
Zhongxing Zhou,
Jingying Jiang,
Huijuan Zhao,
Feng Gao
Publication year - 2018
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.9.004569
Subject(s) - computer science , compressed sensing , iterative reconstruction , a priori and a posteriori , projection (relational algebra) , artificial intelligence , computer vision , sparse matrix , photoacoustic tomography , algorithm , philosophy , physics , epistemology , quantum mechanics , gaussian
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 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 concept of virtual parallel-projection matching the measurement condition is introduced to aid the "compressive sensing" in the reconstruction procedure, and meanwhile, the non-local spatially adaptive filtering exploring the a priori information of the mutual similarities in natural images is adopted to recover the unknowns in the transformed sparse domain. Consequently, the reconstructed images with the proposed sparse-view scheme can be evidently improved in comparison to those with the universal back-projection method, for the cases of same sparse views. The proposed approach has been validated by the simulations and ex vivo experiments, which exhibits desirable performances in image fidelity even from a small number of measuring positions.