A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT Denoising
This work addresses the need for robust evaluation techniques in low-dose CT denoising, particularly for unpaired methods, offering practical guidelines for researchers and practitioners in medical imaging.
The study tackled the problem of evaluating low-dose CT denoising methods by comparing 15 paired and unpaired image quality assessment metrics, performing an in-depth statistical analysis to reveal correlations and provide guidelines for selecting appropriate measures.
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.