Title: Impact of deep learning-based image super-resolution on binary signal detection Show Chinese title

Title: 基于深度学习的图像超分辨率对二进制信号检测的影响

Abstract: 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. Show Chinese abstract

Abstract: 基于深度学习的图像超分辨率（DL-SR）在医学成像应用中显示出巨大的潜力。到目前为止，大多数提出的DL-SR方法仅通过计算机视觉领域常用的图像质量（IQ）传统指标进行评估。然而，这些方法对与医学成像任务相关的客观图像质量指标的影响仍 largely 未被探索。在本研究中，我们研究了DL-SR方法对二进制信号检测性能的影响。两种流行的DL-SR方法，超分辨率卷积神经网络（SRCNN）和超分辨率生成对抗网络（SRGAN），使用模拟的医学图像数据进行训练。制定了二进制信号已知确切且背景已知统计（SKE/BKS）和信号已知统计且背景已知统计（SKS/BKS）检测任务。采用了包括神经网络近似理想观察者和常见线性数值观察者在内的数值观察者来评估DL-SR对任务性能的影响。量化了DL-SR网络架构的复杂性对任务性能的影响。此外，还研究了DL-SR在提高次优观察者的任务性能方面的效用。我们的数值实验证实，正如预期的那样，DL-SR可以改善传统的IQ度量。然而，对于许多考虑的研究设计，DL-SR方法在任务性能方面几乎没有或没有改进，甚至可能使其退化。观察到在某些条件下，DL-SR可以提高次优观察者的任务性能。所提出的研究强调了对DL-SR方法进行客观评估的紧迫性，并提出了改进其在医学成像应用中有效性的途径。