Deep learning approaches have shown promising performance for compressed
sensing-based Magnetic Resonance Imaging. While deep neural networks trained
with mean squared error (MSE) loss functions can achieve high peak signal to
noise ratio, the reconstructed images are often blurry and lack sharp details,
especially for higher undersampling rates. Recently, adversarial and perceptual
loss functions have been shown to achieve more visually appealing results.
However, it remains an open question how to (1) optimally combine these loss
functions with the MSE loss function and (2) evaluate such a perceptual
enhancement. In this work, we propose a hybrid method, in which a visual
refinement component is learnt on top of an MSE loss-based reconstruction
network. In addition, we introduce a semantic interpretability score, measuring
the visibility of the region of interest in both ground truth and reconstructed
images, which allows us to objectively quantify the usefulness of the image
quality for image post-processing and analysis. Applied on a large cardiac MRI
dataset simulated with 8-fold undersampling, we demonstrate significant
improvements ($p<0.01$) over the state-of-the-art in both a human observer
study and the semantic interpretability score.

该论文提出了一种基于 MSE 损失重建网络的可视改善组件，通过引入感知损失函数和对抗性损失函数，可以更好地平衡图像清晰度和对比度。同时，提出了一种语义可解释性评分，可对象地定量评估图像质量。在基于 8 倍下采样的大型心脏 MRI 数据集上的应用表明，该方法相比最新技术，在人类观察者研究和语义可解释性得分上均有显著提高。