Abstract：
Adversarial training has attracted much attention in enhancing the visual realism of images, but its efficacy in clinical imaging has not yet been explored. This work investigated adversarial training in a clinical context, by training 206 networks on the OASIS-1 dataset for improving low-resolution and low signal-to-noise ratio (SNR) magnetic resonance images. Each network corresponded to a different combination of perceptual and adversarial loss weights and distinct learning rate values. For each perceptual loss weighting, we identified its corresponding adversarial loss weighting that minimized structural disparity. Each optimally weighted adversarial loss yielded an average SSIM reduction of 1.5%. We further introduced a set of new metrics to assess other clinically relevant image features: Gradient Error (GE) to measure structural disparities; Sharpness to compute edge clarity; and Edge-Contrast Error (ECE) to quantify any distortion of the pixel distribution around edges. Including adversarial loss increased structural enhancement in visual inspection, which correlated with statistically consistent GE reductions (p-value << 0.05). This also resulted in increased Sharpness; however, the level of statistical significance was dependent on the perceptual loss weighting. Additionally, adversarial loss yielded ECE reductions for smaller perceptual loss weightings, while showing non-significant increases (p-value >> 0.05) when these weightings were higher, demonstrating that the increased Sharpness does not adversely distort the pixel distribution around the edges in the image. These studies clearly suggest that adversarial training significantly improves the performance of an MRI enhancement pipeline, and highlights the need for systematic studies of hyperparameter optimization and investigation of alternative image quality metrics.
摘要：
对抗性训练在增强图像的视觉真实感方面备受关注,但其在临床影像学中的疗效尚未被探讨。这项工作调查了临床背景下的对抗训练，通过在OASIS-1数据集上训练206网络来改善低分辨率和低信噪比（SNR）磁共振图像。每个网络都对应于感知和对抗性损失权重以及不同的学习率值的不同组合。对于每个感知损失加权，我们确定了其相应的对抗性损失权重，将结构差异降至最低。每个最佳加权对抗损失产生平均1.5%的SSIM减少。我们进一步引入了一组新的度量标准来评估其他临床相关的图像特征：梯度误差（GE），用于测量结构差异；清晰度计算边缘清晰度；边缘对比度误差（ECE），用于量化边缘周围像素分布的任何失真。包括对抗性损失增加了视觉检查中的结构增强，这与统计学上一致的GE减少相关(p值<<0.05)。这也导致了锐度的增加；然而，统计学显著性水平取决于感知损失权重.此外,对抗性损失导致ECE减少，感知损失权重较小，当这些权重较高时，显示出不显著的增加(p值>>0.05)，证明增加的清晰度不会不利地扭曲图像边缘周围的像素分布。这些研究清楚地表明，对抗训练显着提高了MRI增强管道的性能，并强调需要对超参数优化进行系统研究，并研究替代图像质量度量。
