De‐noising of photoacoustic sensing and imaging based on combined empirical mode decomposition and independent component analysis

Photoacoustic (PA) imaging breaks the diffusion limit of conventional optical imaging by listening to the PA wave. As a new kind of functional imaging method, it has experienced tremendous growth in research community with wide range of applications. However, it is still an open and fundamental challenge that the conversion efficiency from light to sound based on PA effect is extremely low. The consequence is the poor signal-to-noise ratio (SNR) of PA signal especially in scenarios of low laser power and deep penetration. Conventional approach to enhance the SNR of PA signal in these noisy scenarios is data averaging, which is quite time-consuming. To improve the signal fidelity and imaging speed, an algorithm of using empirical mode decomposition and independent component analysis de-noising methods in PA imaging is proposed. The simulation and in vivo experimental results show obvious SNR enhancement of the PA signal and image contrast. The proposed method provides the potential to develop real-time low-cost PA imaging system with low-power laser source.

[1]  Yuanjin Zheng,et al.  Single laser pulse generates dual photoacoustic signals for differential contrast photoacoustic imaging , 2017, Scientific Reports.

[2]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  Huabei Jiang,et al.  4-D Photoacoustic Tomography , 2013, Scientific Reports.

[4]  A. Oraevsky,et al.  Laser optoacoustic imaging system for detection of breast cancer. , 2009, Journal of biomedical optics.

[5]  John A Viator,et al.  Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction , 2008, Physics in medicine and biology.

[6]  Yuanjin Zheng,et al.  Phase-domain photoacoustic sensing , 2017 .

[7]  Bernard Choi,et al.  In vivo port-wine stain depth determination with a photoacoustic probe. , 2003, Applied optics.

[8]  Lihong V. Wang,et al.  Tutorial on Photoacoustic Microscopy and Computed Tomography , 2008, IEEE Journal of Selected Topics in Quantum Electronics.

[9]  Lihong V. Wang,et al.  Simultaneous functional photoacoustic and ultrasonic endoscopy of internal organs in vivo , 2012, Nature Medicine.

[10]  A. Tam.  Applications of photoacoustic sensing techniques , 1986 .

[11]  Vasilis Ntziachristos,et al.  Blind source unmixing in multi-spectral optoacoustic tomography. , 2011, Optics express.

[12]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[13]  Lihong V. Wang,et al.  Optical-resolution photoacoustic microscopy for in vivo imaging of single capillaries. , 2008, Optics letters.

[14]  Lihong V Wang,et al.  Photoacoustic tomography and sensing in biomedicine , 2009, Physics in medicine and biology.

[15]  B T Cox,et al.  k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields. , 2010, Journal of biomedical optics.

[16]  Yuanjin Zheng,et al.  Micro-Doppler Photoacoustic Effect and Sensing by Ultrasound Radar , 2016, IEEE Journal of Selected Topics in Quantum Electronics.

[17]  J. Laufer,et al.  In vivo high-resolution 3D photoacoustic imaging of superficial vascular anatomy , 2009, Physics in medicine and biology.

[18]  Adrien E. Desjardins,et al.  Identification and removal of laser-induced noise in photoacoustic imaging using singular value decomposition , 2016, Biomedical optics express.

[19]  Yuanjin Zheng,et al.  Adaptive Photoacoustic Sensing Using Matched Filter , 2017, IEEE Sensors Letters.

[20]  Vasilis Ntziachristos,et al.  Multispectral opto-acoustic tomography of deep-seated fluorescent proteins in vivo , 2009 .

[21]  Lihong V. Wang.  Multiscale photoacoustic microscopy and computed tomography. , 2009, Nature photonics.

[22]  Norden E. Huang,et al.  Review of empirical mode decomposition , 2001, SPIE Defense + Commercial Sensing.

[23]  Minghua Xu,et al.  Time-domain reconstruction for thermoacoustic tomography in a spherical geometry , 2002, IEEE Transactions on Medical Imaging.

[24]  Danilo P. Mandic,et al.  Empirical Mode Decomposition for Trivariate Signals , 2010, IEEE Transactions on Signal Processing.

[25]  Mehmet Ali Arıcı,et al.  Variation of contrast-noise ratio (CNR) and mean glanduler dose according to breast thickness , 2016, 2016 20th National Biomedical Engineering Meeting (BIYOMUT).

[26]  Lihong V. Wang,et al.  Whole-body ring-shaped confocal photoacoustic computed tomography of small animals in vivo. , 2012, Journal of biomedical optics.

[27]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[28]  Yuanjin Zheng,et al.  Fast photoacoustic-guided depth-resolved Raman spectroscopy: a feasibility study. , 2015, Optics letters.

[29]  Yuanjin Zheng,et al.  Photoacoustic elastic oscillation and characterization. , 2015, Optics express.

[30]  Aamir Saeed Malik,et al.  Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA , 2017, Journal of Neuroscience Methods.

[31]  Fei Gao,et al.  Advanced photoacoustic and thermoacoustic sensing and imaging beyond pulsed absorption contrast , 2016 .

[32]  A. Karabutov,et al.  Time-resolved laser optoacoustic tomography of inhomogeneous media , 1996 .

[33]  Manojit Pramanik,et al.  Basis pursuit deconvolution for improving model-based reconstructed images in photoacoustic tomography. , 2014, Biomedical optics express.

[34]  Lihong V. Wang,et al.  Multicontrast photoacoustic in vivo imaging using near-infrared fluorescent proteins , 2014, Scientific Reports.

[35]  Chi Zhang,et al.  Single-cell photoacoustic thermometry , 2013, Journal of biomedical optics.

[36]  Mirjana Bonkovic,et al.  Application of EEMD-ICA algorithm to EMG signals measured in laryngeal muscles , 2013, 2013 21st International Conference on Software, Telecommunications and Computer Networks - (SoftCOM 2013).