Compressed Sensing Photoacoustic Imaging based on Correlation Criterion

For different tissues, the sparstiy of photoacoustic images changes greatly. In compressed sensing photoacoustic imaging, the required measurement increases as the image sparsity decreases to acquire a certain image quality. In this paper, a compressed sensing photoacoustic imaging method based on correlation criterion is proposed. The photoacoustic imaging scheme based on arc-compressed sensing and two-view observation is built firstly. Then the correlation coefficient between two-view observation images is defined and computed. As the measurement increases, the correlation coefficient rises. When the correlation coefficient reaches to a certain value, the reconstructed artifacts become slight enough and the measurement process finishes. The proposed method is testified by the subsequent simulation. Two tissue phantoms with different sparsity are built in the simulation. The corresponding photoacoustic signals are detected and reconstructed respectively. Simulation results show that the optimal measurement can be determined by the proposed correlation criterion.

[1]  A. B. Sontheimer Digital Micromirror Device (DMD) hinge memory lifetime reliability modeling , 2002, 2002 IEEE International Reliability Physics Symposium. Proceedings. 40th Annual (Cat. No.02CH37320).

[2]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[3]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[4]  Walter M. Duncan,et al.  Emerging digital micromirror device (DMD) applications , 2003, SPIE MOEMS-MEMS.

[5]  Natalie Baddour,et al.  Theory and analysis of frequency-domain photoacoustic tomography. , 2008, The Journal of the Acoustical Society of America.

[6]  Wei Zhuang,et al.  Energy-Efficient ECG Acquisition in Body Sensor Networks based on Compressive Sensing , 2011 .

[7]  Lihong V. Wang,et al.  Photoacoustic imaging in biomedicine , 2006 .

[8]  Ping Fu,et al.  Compressive Sensing Signal Detection Algorithm Based on Location Information of Sparse Coefficients , 2010, J. Digit. Content Technol. its Appl..

[9]  Lihong V. Wang,et al.  Prospects of photoacoustic tomography. , 2008, Medical physics.

[10]  Frédéric Lesage,et al.  The Application of Compressed Sensing for Photo-Acoustic Tomography , 2009, IEEE Transactions on Medical Imaging.

[11]  V. Altuzar,et al.  Atmospheric pollution profiles in Mexico City in two different seasons , 2003 .

[12]  Lihong V. Wang,et al.  Functional photoacoustic microscopy for high-resolution and noninvasive in vivo imaging , 2006, Nature Biotechnology.

[13]  Zhilie Tang,et al.  Photoacoustic tomography imaging using a 4f acoustic lens and peak-hold technology. , 2008, Optics express.

[14]  Lihong V Wang,et al.  Compressed sensing in photoacoustic tomography in vivo. , 2010, Journal of biomedical optics.