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Among all tissue imaging modalities, photo-acoustic tomography (PAT) has beengetting increasing attention in the recent past due to the fact that it hashigh contrast, high penetrability, and has capability of retrieving highresolution. Model-based regularized reconstruction methods used in PAT plays acrucial role in the applicability of PAT because such methods can yield highquality images from measurements obtained using fewer number of transducers. Acrucial factor that determines the quality of reconstruction in such methods isthe choice of regularization weight. Unfortunately, an appropriately tunedvalue of regularization weight varies significantly with variation in the noiselevel, as well as, with the variation in the high resolution contents of theimage, in a way that has not been well understood. There has been attempts todetermine optimum regularization weight from the measured data in the contextof using elementary and general purpose regularizations. In this paper, wedevelop a practicable method for semi-automated tuning of the regularizationweight in the context of using a modern type of regularization that isspecifically designed for PAT image reconstruction. As a first step, weintroduce a relative smoothness constraint with a parameter; this parametercomputationally maps into the actual regularization weight, but, its tuningdoes not vary significantly with variation in the noise level, and with thevariation in the high resolution contents of the image. Next, we construct analgorithm that integrates the task of determining this mapping along withobtaining the reconstruction. Finally we demonstrate experimentally that we canrun this algorithm with a nominal value of the relative smoothness parameter --a value independent of the noise level and the structure of the underlyingimage -- to obtain good quality reconstructions.

PAT image reconstruction using augmented sparsity regularization with practicable tuning of regularization weight