Deep neural networks used for reconstructing sparse-view CT data are typically trained by minimizing a pixel-wise mean-squared error or similar loss function over a set of training images. However, networks trained with such pixel-wise losses are prone to wipe out small, low-contrast features that are critical for screening and diagnosis. To remedy this issue, we introduce a novel training loss inspired by the model observer framework to enhance the detectability of weak signals in the reconstructions. We evaluate our approach on the reconstruction of synthetic sparse-view breast CT data, and demonstrate an improvement in signal detectability with the proposed loss.

翻译：用于稀疏视图CT重建的深度神经网络通常通过在一组训练图像上最小化逐像素均方误差或类似损失函数来进行训练。然而，采用此类逐像素损失训练的网络容易抹掉对筛查和诊断至关重要的低对比度细微特征。为解决这一问题，我们引入一种受模型观察器框架启发的全新训练损失函数，旨在增强重建图像中微弱信号的可检测性。我们在合成稀疏视图乳腺CT数据重建中评估了该方法，并证实所提出的损失函数能够提升信号可检测性。