Model-based optoacoustic inversions with incomplete projection data

Purpose: Optoacoustic imaging is an emerging noninvasive imaging modality that can resolve optical contrast through several millimeters to centimeters of tissue with diffraction-limited resolution of ultrasound. Yet, quantified reconstruction of tissue absorption maps requires optoacoustic signals to be collected from as many locations around the object as possible. In many tomographic imaging scenarios, however, only limited-view or partial projection data are available, which has been shown to generate image artifacts and overall loss of quantification accuracy. Methods: In this article, the recently introduced interpolated-matrix-model optoacoustic inversion method is tested in different limited-view scenarios and compared to the standard backprojection algorithm. Both direct (TGSVD) and iterative (PLSQR) regularization methods are proposed to improve the accuracy of image reconstructions with their performance tested on simulated and experimental data. Results: While for full-view tomographic data the model-based inversion has been generally shown to attain higher reconstruction accuracy compared to backprojection algorithms, the incomplete tomographic datasets lead to ill-conditioned forward matrices and, consequently, to error-prone inversions, with strong artifacts following a distinct ripple-type pattern. The proposed regularization techniques are shown to stabilize the inversion and eliminate the artifacts. Conclusions: Overall, it has been determined that the regularized interpolated-matrix-model-based optoacoustic inversions show higher accuracy than reconstructions with the standard backprojection algorithm. Finally, the combination of model-based inversion with PLSQR or TGSVD regularization methods can lead to an accurate reconstruction of limited-projection angle optoacoustic data and practical systems for optoacoustic imaging in many realistic cases where the full-view dataset is unavailable.