Quantitative Reconstruction of Absorption Coefficients for Photoacoustic Tomography

Photoacoustic (PA) tomography (PAT) is a cutting-edge imaging modality for visualizing the internal structure and light-absorption distribution in tissue. However, reconstruction of the absorption distribution has been limited by nonuniform light fluence. This paper introduces a novel method for quantitative reconstruction of the distribution of optical absorption coefficients in tissue. In this method, we implement an iterative algorithm for recovering absorption coefficients from optical absorbed energy maps based on a 3D Monte Carlo simulation of light transport and integrated with fluence compensation to obtain the initialization parameters. In the iteration algorithm, we calculate the deviation between the detected and the computed absorbed energy distribution at each iteration. By minimizing the deviation in the absorbed energy, the recovered values converge to the true absorption distribution. The results of numerical simulation and phantom experiment theoretically and experimentally demonstrate that the proposed method performs an accurately quantitative estimate of the distribution of optical absorption coefficients. This work expects to provide accurate quantitative information for absorbers within tissues or organs, and thereby broaden the clinical applications of PAT.

[1]  Vasilis Ntziachristos,et al.  Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues , 2015, Nature Communications.

[2]  Yi Shen,et al.  Monte Carlo light transport-based blood vessel quantification using linear array photoacoustic tomography , 2017 .

[3]  Simon R Arridge,et al.  Two-dimensional quantitative photoacoustic image reconstruction of absorption distributions in scattering media by use of a simple iterative method. , 2006, Applied optics.

[4]  F. M. van den Engh,et al.  Visualizing breast cancer using the Twente photoacoustic mammoscope: what do we learn from twelve new patient measurements? , 2012, Optics express.

[5]  Malini Olivo,et al.  Multispectral Photoacoustic Imaging Artifact Removal and Denoising Using Time Series Model-Based Spectral Noise Estimation , 2016, IEEE Transactions on Medical Imaging.

[6]  Qiang Wang,et al.  Reconstruction of optical absorption coefficient maps of heterogeneous media by photoacoustic tomography coupled with diffusion equation based regularized Newton method. , 2007, Optics express.

[7]  James Joseph,et al.  Towards Quantitative Evaluation of Tissue Absorption Coefficients Using Light Fluence Correction in Optoacoustic Tomography , 2017, IEEE Transactions on Medical Imaging.

[8]  Ilya Turchin,et al.  Fluence compensation in raster-scan optoacoustic angiography , 2017, Photoacoustics.

[9]  Vasilis Ntziachristos,et al.  Maximum Entropy Based Non-Negative Optoacoustic Tomographic Image Reconstruction , 2017, IEEE Transactions on Biomedical Engineering.

[10]  Simon R. Arridge,et al.  Bayesian Image Reconstruction in Quantitative Photoacoustic Tomography , 2013, IEEE Transactions on Medical Imaging.

[11]  Hua-bei Jiang,et al.  Finite-element-based photoacoustic tomography in time domain , 2009 .

[12]  S. Arridge,et al.  Estimating chromophore distributions from multiwavelength photoacoustic images. , 2009, Journal of the Optical Society of America. A, Optics, image science, and vision.

[13]  Debasish Roy,et al.  Quantitative photoacoustic tomography from boundary pressure measurements: noniterative recovery of optical absorption coefficient from the reconstructed absorbed energy map. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.

[14]  Lihong V. Wang,et al.  Quantitative photoacoustic imaging: correcting for heterogeneous light fluence distributions using diffuse optical tomography. , 2011, Journal of biomedical optics.

[15]  R. Zemp,et al.  Estimating optical absorption, scattering, and Grueneisen distributions with multiple-illumination photoacoustic tomography. , 2011, Applied optics.

[16]  Lihong V. Wang,et al.  Noninvasive laser-induced photoacoustic tomography for structural and functional in vivo imaging of the brain , 2003, Nature Biotechnology.

[17]  Vasilis Ntziachristos,et al.  Fast Semi-Analytical Model-Based Acoustic Inversion for Quantitative Optoacoustic Tomography , 2010, IEEE Transactions on Medical Imaging.

[18]  Konstantin I Maslov,et al.  Handheld photoacoustic microscopy to detect melanoma depth in vivo. , 2014, Optics letters.

[19]  Daniel Razansky,et al.  Visual Quality Enhancement in Optoacoustic Tomography Using Active Contour Segmentation Priors , 2015, IEEE Transactions on Medical Imaging.

[20]  Vasilis Ntziachristos,et al.  Performance of iterative optoacoustic tomography with experimental data , 2009 .

[21]  Huabei Jiang,et al.  Three-dimensional finite-element-based photoacoustic tomography: reconstruction algorithm and simulations. , 2007, Medical physics.

[22]  Steven L. Jacques,et al.  Coupling 3D Monte Carlo light transport in optically heterogeneous tissues to photoacoustic signal generation , 2014, Photoacoustics.

[23]  P. C. Beard,et al.  Quantitative photoacoustic imaging: fitting a model of light transport to the initial pressure distribution , 2005, SPIE BiOS.

[24]  Yi Shen,et al.  Multiscale Hessian filter-based segmentation and quantification method for photoacoustic microangiography , 2015 .

[25]  Markus Haltmeier,et al.  Stochastic Proximal Gradient Algorithms for Multi-Source Quantitative Photoacoustic Tomography , 2018, Entropy.

[26]  Vasilis Ntziachristos,et al.  Sparse signal representation at the service of quantitative optoacoustic tomography , 2010, BiOS.

[27]  Yan Liu,et al.  Calibration-free quantification of absolute oxygen saturation based on the dynamics of photoacoustic signals. , 2013, Optics letters.

[28]  Sanjiv S Gambhir,et al.  Quantitative photoacoustic image reconstruction improves accuracy in deep tissue structures. , 2016, Biomedical optics express.

[29]  Huabei Jiang,et al.  Two schemes for quantitative photoacoustic tomography based on Monte Carlo simulation. , 2016, Medical physics.

[30]  Jin Zhang,et al.  Effects of Different Imaging Models on Least-Squares Image Reconstruction Accuracy in Photoacoustic Tomography , 2009, IEEE Transactions on Medical Imaging.

[31]  Makoto Yamakawa,et al.  Adaptive and quantitative reconstruction algorithm for photoacoustic tomography , 2011, BiOS.

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

[33]  P. Beard Biomedical photoacoustic imaging , 2011, Interface Focus.

[34]  Vasilis Ntziachristos,et al.  Volumetric real-time multispectral optoacoustic tomography of biomarkers , 2011, Nature Protocols.

[35]  H. J. van Staveren,et al.  Light scattering in Intralipid-10% in the wavelength range of 400-1100 nm. , 1991, Applied optics.

[36]  Xueding Wang,et al.  Photoacoustic tomography: a potential new tool for prostate cancer , 2010, Biomedical optics express.

[37]  Lihong V. Wang,et al.  Biomedical Optics: Principles and Imaging , 2007 .

[38]  Lihong V. Wang,et al.  Photoacoustic Tomography: In Vivo Imaging from Organelles to Organs , 2012, Science.

[39]  Zhuang Liu,et al.  Carbon nanotubes as photoacoustic molecular imaging agents in living mice. , 2008, Nature nanotechnology.

[40]  Makoto Yamakawa,et al.  Model-Based Reconstruction Integrated With Fluence Compensation for Photoacoustic Tomography , 2012, IEEE Transactions on Biomedical Engineering.

[41]  Roger J Zemp Quantitative photoacoustic tomography with multiple optical sources. , 2010, Applied optics.

[42]  S. Arridge,et al.  Quantitative spectroscopic photoacoustic imaging: a review. , 2012, Journal of biomedical optics.

[43]  Rui Li,et al.  Assessing breast tumor margin by multispectral photoacoustic tomography. , 2015, Biomedical optics express.

[44]  Vasilis Ntziachristos,et al.  Quantitative Optoacoustic Signal Extraction Using Sparse Signal Representation , 2009, IEEE Transactions on Medical Imaging.