We present a fast inversion algorithm for quantitative two- and three-dimensional optoacoustic tomography. The
algorithm is based on an accurate and efficient forward model, which eliminates the need for regularization in the
inversion and can achieve real-time performance. The reconstruction speed and other algorithmic performances are
demonstrated using numerical simulation studies and experimentally on tissue-mimicking optically heterogeneous
phantoms and small animals. In the experimental examples, the model-based reconstructions manifested correctly the
effect of light attenuation through the objects and did not suffer from the artifacts which usually afflict the commonly
used filtered backprojection algorithms, such as negative absorption values.
Obtaining quantified optoacoustic reconstructions is an important and longstanding challenge, mainly caused by the
complex heterogeneous structure of biological tissues as well as the lack of accurate and robust reconstruction
algorithms. The recently introduced model-based inversion approaches were shown to eliminate some of reconstruction
artifacts associated with the commonly used back-projection schemes, while providing an excellent platform for
obtaining quantified maps of optical energy deposition in experimental configurations of various complexity. In this
work, we introduce a weighted model-based approach, capable of overcoming reconstruction challenges caused by perprojection
variations of object's illumination and other partial illumination effects. The universal weighting procedure is
equally shown to reduce reconstruction artifacts associated with other experimental imperfections, such as non-uniform
transducer sensitivity fields. Significant improvements in image fidelity and quantification are showcased both
numerically and experimentally on tissue phantoms.
We describe an improved optoacoustic tomography method, that utilizes a diffusion-based photon propagation model in
order to obtain quantified reconstruction of targets embedded deep in heterogeneous scattering and absorbing tissue. For
the correction we utilize an iterative finite-element solution of the light diffusion equation to build a photon propagation
model. We demonstrate image improvements achieved by this method by using tissue-mimicking phantom
measurements. The particular strength of the method is its ability to achieve quantified reconstructions in non-uniform
illumination configurations resembling whole-body small animal imaging scenarios.