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A variety of deep neural network (DNN)-based image denoising methods havebeen proposed for use with medical images. Traditional measures of imagequality (IQ) have been employed to optimize and evaluate these methods.However, the objective evaluation of IQ for the DNN-based denoising methodsremains largely lacking. In this work, we evaluate the performance of DNN-baseddenoising methods by use of task-based IQ measures. Specifically, binary signaldetection tasks under signal-known-exactly (SKE) withbackground-known-statistically (BKS) conditions are considered. The performanceof the ideal observer (IO) and common linear numerical observers are quantifiedand detection efficiencies are computed to assess the impact of the denoisingoperation on task performance. The numerical results indicate that, in thecases considered, the application of a denoising network can result in a lossof task-relevant information in the image. The impact of the depth of thedenoising networks on task performance is also assessed. The presented resultshighlight the need for the objective evaluation of IQ for DNN-based denoisingtechnologies and may suggest future avenues for improving their effectivenessin medical imaging applications.

Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks