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Full-view photoacoustic tomography using asymmetric distributed sensors optimized with compressed sensing method.pdf

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Full-view photoacoustic tomography using asymmetric distributed sensors optimized with compressed sensing method.pdf

Biomedical Signal Processing and Control 21 (2015) 19–25 Contents lists available at ScienceDirect Biomedical Signal Processing and Control journal homepage: /locate/bspc Full-view photoacoustic tomography using asymmetric distributed sensors optimized with compressed sensing method Meng Cao a, Jie Yuan a,?, Sidan Du a, Guan Xu b, Xueding Wang b, Paul L. Carson b, Xiaojun Liu c a School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China b Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA c School of Physics, Nanjing University, Nanjing 210093, China article info Article history: Received 3 February 2015 Received in revised form 14 April 2015 Accepted 6 May 2015 Available online 29 May 2015 Keywords: Photoacoustic tomography Compressed sensing Asymmetric sensor distribution Full-view imaging abstract Photoacoustic tomography (PAT) is an effective optical biomedical imaging method characterized with non-ionizing and noninvasive, presenting good soft tissue contrast and excellent spatial resolution. To build a full-view photoacoustic (PA) image, numbers of ultrasound sensors are needed to assure the image quality, which will bring dif?culties to data acquisition and real-time image display. Compressed sensing (CS) theory breaks the restriction of Nyquist sampling theorem and is capable to rebuild signals with less measurements. In this contribution, we proposed an optimization method to increase image quality of full-view PAT with less ultrasound sensors, which combines the theory of CS and a circularly distributing asymmetric data acquisition frame. The hardware cost can be saved and time ef?ciency of PAT can be raised without sacri?cing the image quality. The feasibility of our proposed method is veri?ed in simulation experiments which yield expected results. Our study might be helpful for clinical medical imaging such as early stage breast cancer detection, endoscopic imaging and in vivo monitoring. ? 2015 Elsevi