Abstract: In optical tomography, there exist certain spatial frequency
components that cannot be measured due to the limited projection angles
imposed by the numerical aperture of objective lenses. This limitation,
often called as the missing cone problem, causes the under-estimation of
refractive index (RI) values in tomograms and results in severe elongations
of RI distributions along the optical axis. To address this missing cone
problem, several iterative reconstruction algorithms have been introduced
exploiting prior knowledge such as positivity in RI differences or edges of
samples. In this paper, various existing iterative reconstruction algorithms
are systematically compared for mitigating the missing cone problem in
optical diffraction tomography. In particular, three representative regu-
larization schemes, edge preserving, total variation regularization, and
the Gerchberg-Papoulis algorithm, were numerically and experimentally
evaluated using spherical beads as well as real biological samples; human
red blood cells and hepatocyte cells. Our work will provide important
guidelines for choosing the appropriate regularization in ODT