Three-dimensional photoacoustic imaging and inversion for accurate quantification of chromophore distributions

Photoacoustic tomography can, in principle, provide quantitatively accurate, high-resolution, images of chromophore distributions in 3D in vivo. However, achieving this goal requires not only dealing with the optical fluence-related spatial and spectral distortion but also having access to high quality, calibrated, measurements and using image reconstruction algorithms free from inaccurate assumptions. Furthermore, accurate knowledge of experimental parameters, such as the positions of the ultrasound detectors and the illumination pattern, is necessary for the reconstruction step. A meticulous and rigorous experimental phantom study was conducted to show that highly-resolved 3D estimation of chromophore distributions can be achieved: a crucial step towards in vivo implementation. The phantom consisted of four 580 μm diameter tubes with different ratios of copper sulphate and nickel sulphate as hemoglobin analogues, submersed in a background medium of intralipid and india ink. The optical absorption, scattering, photostability, and Grüneisen parameter were characterised for all components independently. A V-shaped imaging scanner enabled 3D imaging with the high resolution, high sensitivity, and wide bandwidth characteristic of Fabry-Pérot ultrasound sensors, but without the limited-view disadvantage of single-plane scanners. The optical beam profile and position were determined experimentally. Nine wavelengths between 750 and 1110 nm were used. The images of the chromophore concentrations were obtained using a model-based, two-step, procedure, that did not require image segmentation. First, the acoustic reconstruction was solved with an iterative time-reversal algorithm to obtain images of the initial acoustic pressure at each of the nine wavelengths for an 18×17×13 mm3 volume with 50μm voxels. Then, 3D high resolution estimates of the chromophore concentrations were obtained by using a diffusion model of light transport in an iterative nonlinear optimisation scheme. Among the lessons to be drawn from this study, one is fundamental: in order to obtain accurate estimates of chromophores (or their ratios) it is not only necessary to model the light fluence accurately, but it is just as crucial to obtain accurate estimates of the initial acoustic pressure distributions, and to account for variations in the thermoelastic efficiency (Grüneisen parameter).

[1]  Dudley A. Williams,et al.  Optical properties of water in the near infrared. , 1974 .

[2]  G. Uhlmann,et al.  Thermoacoustic tomography with variable sound speed , 2009, 0902.1973.

[3]  Jan Laufer,et al.  Quantitative determination of chromophore concentrations from 2D photoacoustic images using a nonlinear model-based inversion scheme. , 2010, Applied optics.

[4]  Lihong V. Wang,et al.  Reconstructions in limited-view thermoacoustic tomography. , 2004, Medical physics.

[5]  Da-Kang Yao,et al.  Photoacoustic measurement of the Grüneisen parameter of tissue , 2014, Journal of biomedical optics.

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

[7]  T. Stahl,et al.  Characterization of the thermalisation efficiency and photostability of photoacoustic contrast agents , 2014, Photonics West - Biomedical Optics.

[8]  Simon Arridge,et al.  Reconstruction-classification method for quantitative photoacoustic tomography , 2015, Journal of biomedical optics.

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

[10]  Sarah E Bohndiek,et al.  Contrast agents for molecular photoacoustic imaging , 2016, Nature Methods.

[11]  Markus Haltmeier,et al.  Experimental evaluation of reconstruction algorithms for limited view photoacoustic tomography with line detectors , 2007 .

[12]  Paul C. Beard,et al.  Sensitivity of quantitative photoacoustic tomography inversion schemes to experimental uncertainty , 2016, SPIE BiOS.

[13]  Edward Z. Zhang,et al.  Orthogonal Fabry-Pérot sensors for photoacoustic tomography , 2016, SPIE BiOS.

[14]  Robert Ellwood,et al.  Photoacoustic tomography using orthogonal Fabry–Pérot sensors , 2016, Journal of biomedical optics.

[15]  Jan Laufer,et al.  Quantitative spatially resolved measurement of tissue chromophore concentrations using photoacoustic spectroscopy: application to the measurement of blood oxygenation and haemoglobin concentration , 2007, Physics in medicine and biology.

[16]  Jan Laufer,et al.  Evaluation of Absorbing Chromophores Used in Tissue Phantoms for Quantitative Photoacoustic Spectroscopy and Imaging , 2010, IEEE Journal of Selected Topics in Quantum Electronics.

[17]  Martin Schweiger,et al.  The Toast++ software suite for forward and inverse modeling in optical tomography , 2014, Journal of biomedical optics.

[18]  S. Jacques,et al.  Iterative reconstruction algorithm for optoacoustic imaging. , 2002, The Journal of the Acoustical Society of America.

[19]  T. Soonthornsaratoon,et al.  Gradient-based methods for quantitative photoacoustic tomography , 2014 .

[20]  George S. K. Wong,et al.  Speed of sound in pure water as a function of temperature , 1993 .

[21]  Jan Laufer,et al.  Backward-mode multiwavelength photoacoustic scanner using a planar Fabry-Perot polymer film ultrasound sensor for high-resolution three-dimensional imaging of biological tissues. , 2008, Applied optics.

[22]  A. Welch,et al.  Determining the optical properties of turbid mediaby using the adding-doubling method. , 1993, Applied optics.

[23]  Paul C. Beard,et al.  Gradient-based quantitative photoacoustic image reconstruction for molecular imaging , 2007, SPIE BiOS.