Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables
spatially resolved imaging of optical tissue properties up to several centimeters deep in …

Photoacoustic tomography (PAT) regularly operates in limited-view cases owing to data
acquisition limitations. The results using traditional methods in limited-view PAT exhibit …

Non-uniqueness and instability are characteristic features of image reconstruction methods.
As a result, it is necessary to develop regularization methods that can be used to compute …

We propose a non-stationary iterated network Tikhonov (iNETT) method for the solution of ill-
posed inverse problems. The iNETT employs deep neural networks to build a data-driven …

Photoacoustic tomography (PAT) is an innovative biomedical imaging technology, which
has the capacity to obtain high-resolution images of biological tissue. In the extremely …

We propose aNETT (augmented NETwork Tikhonov) regularization as a novel data-driven
reconstruction framework for solving inverse problems. An encoder-decoder type network …

One of the key assumptions in the stability and convergence analysis of variational
regularization is the ability of finding global minimizers. However, such an assumption is …

We present a deep learning-based computational algorithm for inversion of circular Radon
transforms in the partial radial setup, arising in photoacoustic tomography. We first …

Photoacoustic imaging (PAI) holds great promise as a novel, non-ionizing imaging modality,
allowing insight into both morphological and physiological tissue properties, which are of …

One of the key tools in an organization's performance management is the goal tree, which is
used for solving both direct and inverse problems. This research deals with goal setting …