  Performing X-ray computed tomography (CT) examinations with less radiation
has recently received increasing interest: in medical imaging this means less
(potentially harmful) radiation for the patient; in non-destructive testing of
materials/objects such as testing jet engines, the redution of the number of
projection angles (which for large objects is in general high) leads to a
substantial decreasing of the experiment time. In the experiment, less
radiation is usually achieved by either (1) reducing the radiation dose used at
each projection angle or (2) using sparse view X-ray CT, which means
significantly less projection angles are used during the examination. In this
work, we study the performance of the recently proposed sinogram-based
iterative reconstruction algorithm in sparse view X-ray CT and show that it
provides, in some cases, reconstruction accuracy better than that obtained by
some of the Total Variation regularization techniques. The provided accuracy is
obtained with computation times comparable to other techniques. An important
feature of the sinogram-based iterative reconstruction algorithm is that it has
no parameters to be set.
