Paper
22 March 2017 Experimental validation of a Monte-Carlo-based inversion scheme for 3D quantitative photoacoustic tomography
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Abstract
Quantitative photoacoustic tomography (qPAT) aims to extract physiological parameters, such as blood oxygen saturation (sO2), from measured multi-wavelength image data sets. The challenge of this approach lies in the inherently nonlinear fluence distribution in the tissue, which has to be accounted for by using an appropriate model, and the large scale of the inverse problem. In addition, the accuracy of experimental and scanner-specific parameters, such as the wavelength dependence of the incident fluence, the acoustic detector response, the beam profile and divergence, needs to be considered. This study aims at quantitative imaging of blood sO2, as it has been shown to be a more robust parameter compared to absolute concentrations. We propose a Monte-Carlo–based inversion scheme in conjunction with a reduction in the number of variables achieved using image segmentation. The inversion scheme is experimentally validated in tissue-mimicking phantoms consisting of polymer tubes suspended in a scattering liquid. The tubes were filled with chromophore solutions at different concentration ratios. 3-D multi-spectral image data sets were acquired using a Fabry-Perot based PA scanner. A quantitative comparison of the measured data with the output of the forward model is presented. Parameter estimates of chromophore concentration ratios were found to be within 5 % of the true values.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jens Buchmann, Bernhard A. Kaplan, Steffen Prohaska, and Jan Laufer "Experimental validation of a Monte-Carlo-based inversion scheme for 3D quantitative photoacoustic tomography", Proc. SPIE 10064, Photons Plus Ultrasound: Imaging and Sensing 2017, 1006416 (22 March 2017); https://doi.org/10.1117/12.2252359
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Cited by 2 scholarly publications.
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KEYWORDS
Data modeling

3D modeling

Sensors

Image segmentation

Absorption

Tissue optics

Chromophores

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