Photon counting spectral CT (PCCT) can produce reconstructed attenuation maps in different energy channels, reflecting energy properties of the scanned object. Due to the limited photon numbers and the non-ideal detector response of each energy channel, the reconstructed images usually contain much noise. With the development of Deep Learning (DL) technique, different kinds of DL-based models have been proposed for noise reduction. However, most of the models require clean data set as the training labels, which are not always available in medical imaging field. Inspiring by the similarities of each channel's reconstructed image, we proposed a self-supervised learning based PCCT image enhancement framework via multi-spectral channels (S2MS). In S2MS framework, both the input and output labels are noisy images. Specifically, one single channel image was used as output while images of other single channels and channel-sum image were used as input to train the network, which can fully use the spectral data information without extra cost. The simulation results based on the AAPM Low-dose CT Challenge database showed that the proposed S2MS model can suppress the noise and preserve details more effectively in comparison with the traditional DL models, which has potential to improve the image quality of PCCT in clinical applications.
翻译:光计光计光谱CT(PCCT)可以在不同的能源频道中绘制重建的减速图,反映扫描物体的能量特性。由于光子数量有限,每个能源频道的非理想探测器反应反应也有限,重建后的图像通常含有许多噪音。随着深层学习技术的发展,提出了不同种类的DL基模型以减少噪音。然而,大多数模型需要干净的数据集,作为培训标签,这些标签不一定在医疗成像场中提供。根据每个频道重建后的图像的相似性,我们建议通过多光谱频道(S2MS)建立一个以自我监督为基础的基于学习的PCCT图像增强框架。在S2MS框架内,输入和输出标签都是噪音图像。具体地说,使用一种单一频道图像作为输出,而其他单一频道和频道和频道合成图像作为培训网络的输入,可以不增加额外费用地充分利用光谱数据信息。基于AAPM低剂量CT挑战数据库的模拟结果显示,拟议的S2MS模型可以有效地抑制传统图像的升级质量,并在临床应用中保存详细信息。