  A variety of deep neural network (DNN)-based image denoising methods have
been proposed for use with medical images. Traditional measures of image
quality (IQ) have been employed to optimize and evaluate these methods.
However, the objective evaluation of IQ for the DNN-based denoising methods
remains largely lacking. In this work, we evaluate the performance of DNN-based
denoising methods by use of task-based IQ measures. Specifically, binary signal
detection tasks under signal-known-exactly (SKE) with
background-known-statistically (BKS) conditions are considered. The performance
of the ideal observer (IO) and common linear numerical observers are quantified
and detection efficiencies are computed to assess the impact of the denoising
operation on task performance. The numerical results indicate that, in the
cases considered, the application of a denoising network can result in a loss
of task-relevant information in the image. The impact of the depth of the
denoising networks on task performance is also assessed. The presented results
highlight the need for the objective evaluation of IQ for DNN-based denoising
technologies and may suggest future avenues for improving their effectiveness
in medical imaging applications.
