  Among all tissue imaging modalities, photo-acoustic tomography (PAT) has been
getting increasing attention in the recent past due to the fact that it has
high contrast, high penetrability, and has capability of retrieving high
resolution. The reconstruction methods used in PAT plays a crucial role in the
applicability of PAT, and PAT finds particularly a wider applicability if a
model-based regularized reconstruction method is used. A crucial factor that
determines the quality of reconstruction in such methods is the choice of
regularization weight. Unfortunately, an appropriately tuned value of
regularization weight varies significantly with variation in the noise level,
as well as, with the variation in the high resolution contents of the image, in
a way that has not been well understood. There has been attempts to determine
optimum regularization weight from the measured data in the context of using
elementary and general purpose regularizations. In this paper, we develop a
method for semi-automated tuning of the regularization weight in the context of
using a modern type of regularization that was specifically designed for PAT
image reconstruction. As a first step, we introduce a relative smoothness
constraint with a parameter; this parameter computationally maps into the
actual regularization weight, but, its tuning does not vary significantly with
variation in the noise level, and with the variation in the high resolution
contents of the image. Next, we construct an algorithm that integrates the task
of determining this mapping along with obtaining the reconstruction. Finally we
demonstrate experimentally that we can run this algorithm with a nominal value
of the relative smoothness parameter -- a value independent of the noise level
and the structure of the underlying image -- to obtain good quality
reconstructions.
