Low-dose CT (LDCT) imaging is preferred in many applications to reduce the object's exposure to X-ray radiation. In recent years, one promising approach to image reconstruction in LDCT is the so-called optimization-unrolling-based deep learning approach, which replaces pre-defined image prior by learnable adaptive prior in some model-based iterative image reconstruction scheme (MBIR). While it is known that setting appropriate hyper-parameters in MBIR is challenging yet important to the reconstruction quality, it does not receive enough attention in the development of deep learning methods. This paper proposed a deep learning method for LDCT reconstruction that unrolls a half-quadratic splitting scheme. The proposed method not only introduces learnable image prior built on framelet filter bank, but also learns a network that automatically adjusts the hyper-parameters to fit noise level and the data for processing. As a result, only one universal model needs to be trained in our method to process the data taken under different dose levels. Experimental evaluation on clinical patient dataset showed that the proposed method outperformed both conventional and deep-learning-based solutions by a large margin.

中文翻译：

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利用自适应超参数进行深度学习，实现低剂量 CT 图像重建


在许多应用中，低剂量 CT (LDCT) 成像是首选，可减少物体对 X 射线辐射的暴露。近年来，一种有前途的 LDCT 图像重建方法是所谓的基于优化展开的深度学习方法，该方法在一些基于模型的迭代图像重建方案（MBIR）中用可学习的自适应先验代替预定义的图像先验。虽然众所周知，在 MBIR 中设置适当的超参数具有挑战性，但对重建质量很重要，但它在深度学习方法的开发中没有得到足够的重视。本文提出了一种用于 LDCT 重建的深度学习方法，该方法展开了半二次分割方案。该方法不仅引入了基于框架滤波器组的可学习图像先验，而且还学习了一个自动调整超参数以适应噪声水平和处理数据的网络。因此，在我们的方法中只需要训练一个通用模型来处理不同剂量水平下采集的数据。对临床患者数据集的实验评估表明，所提出的方法大大优于传统和基于深度学习的解决方案。