Abstract Model reduction is often required in several applications, typically due to limited
available time, computer memory or other restrictions. In problems that are related to partial …

Model reduction is often required in diffuse optical tomography (DOT), typically because of
limited available computation time or computer memory. In practice, this means that one is …

In this paper, we investigate the applicability of the Bayesian approximation error approach
to compensate for the discrepancy of the diffusion approximation in diffuse optical …

Diffuse optical tomography (DOT) is a non-invasive functional imaging modality that aims to
image the optical properties of biological organs. The forward problem of the light …

Optical tomography attempts to determine a spatially variable coefficient in the interior of a
body from measurements of light fluxes at the boundary. Like in many other applications in …

We present a review of methods for the forward and inverse problems in optical tomography.
We limit ourselves to the highly scattering case found in applications in medical imaging …

We present an image reconstruction method for diffuse optical tomography (DOT) by using
the sparsity regularization and expectation-maximization (EM) algorithm. Typical image …

Diffuse optical tomography (DOT) involves estimation of tissue optical properties using
noninvasive boundary measurements. The image reconstruction procedure is a nonlinear, ill …

A prominent result of Arridge and Lionheart (1998 Opt. Lett. 23 882–4) demonstrates that it is
in general not possible to simultaneously recover both the diffusion (aka scattering) and the …

Optical tomography consists of reconstructing the spatial distribution of absorption and
scattering properties of a medium from surface measurements of transmitted light intensities …