The development of efficient and accurate image reconstruction algorithms is one of the cornerstones of computed tomography. Existing algorithms for quantitative photoacoustic tomography currently operate in a two-stage procedure: First an inverse source problem for the acoustic wave propagation is solved, whereas in a second step the optical parameters are estimated from the result of the first step. Such an approach has several drawbacks. In this paper we therefore propose the use of single-stage reconstruction algorithms for quantitative photoacoustic tomography, where the optical parameters are directly reconstructed from the observed acoustical data. In that context we formulate the image reconstruction problem of quantitative photoacoustic tomography as a single nonlinear inverse problem by coupling the radiative transfer equation with the acoustic wave equation. The inverse problem is approached by Tikhonov regularization with a convex penalty in combination with the proximal gradient iteration for minimizing the Tikhonov functional. We present numerical results, where the proposed single-stage algorithm shows an improved reconstruction quality at a similar computational cost.

Quantitative image reconstruction in photoacoustic tomography requires the solution of a coupled physics inverse problem involving light transport and acoustic wave propagation. In this paper we address this issue employing the radiative transfer equation as accurate model for light transport. As main theoretical results, we derive several stability and uniqueness results for the linearized inverse problem. We consider the case of single illumination as well as the case of multiple illuminations assuming full or partial data. The numerical solution of the linearized problem is much less costly than the solution of the non-linear problem. We present numerical simulations supporting the stability results for the linearized problem and demonstrate that the linearized problem already gives accurate quantitative results.

Abstract:The development of accurate and efficient image reconstruction algorithms is a central aspect of quantitative photoacoustic tomography (QPAT). In this paper, we address this issues for multi-source QPAT using the radiative transfer equation (RTE) as accurate model for light transport. The tissue parameters are jointly reconstructed from the acoustical data measured for each of the applied sources. We develop stochastic proximal gradient methods for multi-source QPAT, which are more efficient than standard proximal gradient methods in which a single iterative update has complexity proportional to the number applies sources. Additionally, we introduce a completely new formulation of QPAT as multilinear (MULL) inverse problem which avoids explicitly solving the RTE. The MULL formulation of QPAT is again addressed with stochastic proximal gradient methods. Numerical results for both approaches are presented. Besides the introduction of stochastic proximal gradient algorithms to QPAT, we consider the new MULL formulation of QPAT as main contribution of this paper.

Block coordinate descent (BCD) methods approach optimization problems by performing gradient steps along alternating subgroups of coordinates. This is in contrast to full gradient descent, where a gradient step updates all coordinates simultaneously. BCD has been demonstrated to accelerate the gradient method in many practical large-scale applications. Despite its success no convergence analysis for inverse problems is known so far. In this paper, we investigate the BCD method for solving linear inverse problems. As main theoretical result, we show that for operators having a particular tensor product form, the BCD method combined with an appropriate stopping criterion yields a convergent regularization method. To illustrate the theory, we perform numerical experiments comparing the BCD and the full gradient descent method for a system of integral equations. We also present numerical tests for a nonlinear inverse problem not covered by our theory, namely one-step inversion in multi-spectral X-ray tomography.Other step size rules yield the steepest descent and the minimal error method [20] or a more recent variant analyzed in [19]. Kaczmarz type variants of (1.2) for systems of ill-posed equations have been analyzed in [5,7,8,13,15,16]. Kaczmarz methods make use of a product structure of the image space , and are in this sense dual to BCD methods which exploit the product structure of the pre-image space .We consider the product form a X 1 ¢ ¡ ¡ ¡ ¢ X B , where the forward operator can be written as e a e 1 ; : : : ; e B . As a consequence, the Landweber iteration takes the form

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