  Deep learning-based image super-resolution (DL-SR) has shown great promise in
medical imaging applications. To date, most of the proposed methods for DL-SR
have only been assessed by use of traditional measures of image quality (IQ)
that are commonly employed in the field of computer vision. However, the impact
of these methods on objective measures of image quality that are relevant to
medical imaging tasks remains largely unexplored. In this study, we investigate
the impact of DL-SR methods on binary signal detection performance. Two popular
DL-SR methods, the super-resolution convolutional neural network (SRCNN) and
the super-resolution generative adversarial network (SRGAN), were trained by
use of simulated medical image data. Binary signal-known-exactly with
background-known-statistically (SKE/BKS) and signal-known-statistically with
background-known-statistically (SKS/BKS) detection tasks were formulated.
Numerical observers, which included a neural network-approximated ideal
observer and common linear numerical observers, were employed to assess the
impact of DL-SR on task performance. The impact of the complexity of the DL-SR
network architectures on task-performance was quantified. In addition, the
utility of DL-SR for improving the task-performance of sub-optimal observers
was investigated. Our numerical experiments confirmed that, as expected, DL-SR
could improve traditional measures of IQ. However, for many of the study
designs considered, the DL-SR methods provided little or no improvement in task
performance and could even degrade it. It was observed that DL-SR could improve
the task-performance of sub-optimal observers under certain conditions. The
presented study highlights the urgent need for the objective assessment of
DL-SR methods and suggests avenues for improving their efficacy in medical
imaging applications.
