Optoacoustic Tomography is a fast developing imaging modality, combining the high resolution and penetration depth of ultrasound detection with the high contrast available from optical absorption in tissue. The spectral profile of near infrared excitation light used in optoacoustic tomography instruments is modified by absorption and scattering as it propagates deep into biological tissue. The resulting images therefore provide only qualitative insight into the distribution of tissue chromophores. Knowledge of the spectral profile of excitation light across the mouse is needed for accurate determination of the absorption coefficient in vivo. Under the conditions of constant Grueneisen parameter and accurate knowledge of the light fluence, a linear relationship should exist between the initial optoacoustic pressure amplitude and the tissue absorption coefficient. Using data from a commercial optoacoustic tomography system, we implemented an iterative optimization based on the σ-Eddington approximation to the Radiative Transfer Equation to derive a light fluence map within a given object. We segmented the images based on the positions of phantom inclusions, or mouse organs, and used known scattering coefficients for initialization. Performing the fluence correction in simple phantoms allowed the expected linear relationship between recorded and independently measured absorption coefficients to be retrieved and spectral coloring to be compensated. For in vivo data, the correction resulted in an enhancement of signal intensities in deep tissues. This improved our ability to visualize organs at depth (> 5mm). Future work will aim to perform the optimization without data normalization and explore the need for methodology that enables routine implementation for in vivo imaging.
Frederic M. Brochu, James Joseph, Michal R. Tomaszewski, and Sarah E. Bohndiek, "In vivo light fluence correction for determination of tissue absorption coefficient using Multispectral Optoacoustic Tomography," Proc. SPIE 9708, Photons Plus Ultrasound: Imaging and Sensing 2016, 970829 (Presented at SPIE BiOS: February 17, 2016; Published: 15 March 2016); https://doi.org/10.1117/12.2210958.
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