Research Article
Sparse Photoacoustic Microscopy Reconstruction Based on Matrix Nuclear Norm Minimization
@INPROCEEDINGS{10.1007/978-3-319-73564-1_6, author={Ying Fu and Naizhang Feng and Yahui Shi and Ting Liu and Mingjian Sun}, title={Sparse Photoacoustic Microscopy Reconstruction Based on Matrix Nuclear Norm Minimization}, proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I}, proceedings_a={MLICOM}, year={2018}, month={2}, keywords={Sparse photoacoustic microscopy reconstruction Real-time imaging Matrix completion Nuclear norm minimization}, doi={10.1007/978-3-319-73564-1_6} }- Ying Fu
Naizhang Feng
Yahui Shi
Ting Liu
Mingjian Sun
Year: 2018
Sparse Photoacoustic Microscopy Reconstruction Based on Matrix Nuclear Norm Minimization
MLICOM
Springer
DOI: 10.1007/978-3-319-73564-1_6
Abstract
As a high-resolution deep tissue imaging technology, photoacoustic microscopy (PAM) is attracting extensive attention in biomedical studies. PAM has trouble in achieving real-time imaging with the long data acquisition time caused by point-to-point sample mode. In this paper, we propose a sparse photoacoustic microscopy reconstruction method based on matrix nuclear norm minimization. We use random sparse sampling instead of traditional full sampling and regard the sparse PAM reconstruction problem as a nuclear norm minimization problem, which is efficiently solved under alternating direction method of multiplier (ADMM) framework. Results from PAM experiments indicate the proposed method could work well in fast imaging. The proposed method is also be expected to promote the achievement of PAM real-time imaging.