  The current deep learning approaches for low-dose CT denoising can be divided
into paired and unpaired methods. The former involves the use of well-paired
datasets, whilst the latter relaxes this constraint. The large availability of
unpaired datasets has raised the interest in deepening unpaired denoising
strategies that, in turn, need for robust evaluation techniques going beyond
the qualitative evaluation. To this end, we can use quantitative image quality
assessment scores that we divided into two categories, i.e., paired and
unpaired measures. However, the interpretation of unpaired metrics is not
straightforward, also because the consistency with paired metrics has not been
fully investigated. To cope with this limitation, in this work we consider 15
paired and unpaired scores, which we applied to assess the performance of
low-dose CT denoising. We perform an in-depth statistical analysis that not
only studies the correlation between paired and unpaired metrics but also
within each category. This brings out useful guidelines that can help
researchers and practitioners select the right measure for their applications.
