基于特征学习的低剂量CT成像算法研究进展

随着CT（computed tomography）技术在临床中的大量应用，其辐射伤害问题也越来越受到人们的关注。与此同时，高性能低剂量的成像也已经成为近年来CT研究领域中的重要研究方向。随着学习型算法的提出及广泛应用，为低剂量CT成像算法的发展带来了新的方向。在影像大数据环境下，基于特征学习方法的低剂量CT成像有着更广阔的发展空间。本文将从稀疏表示和深度学习两个方面，介绍一些国内外应用于改善CT成像质量的相关技术，包括CT成像技术的发展趋势，特征学习相关算法的研究现状，提高低剂量CT扫描成像质量的相关方案等。本文对近年来在低剂量CT成像及特种学习算法等领域的研究成果进行了介绍，并进行相关总结和分析。 

Research Progress of Low Dose CT Imaging Based on Feature Learning

The continuous development and extensive use of CT in modern medical practice has raised a public concern over the associated radiation dose to the patient. Hence, extensive efforts have been made to design better image reconstruction or image processing methods for low-dose CT over the past years. The recent explosive development of learning type algorithm suggests new thinking and huge potential for the CT imaging field under the imaging big data environment. This paper summarizes the development and implementation of low dose CT scans from the following aspects: sparse learning and deep learning. The research status of low dose CT technology and feature learning models are also summarized. Finally, both the current research focus and the future research prospect are discussed and analyzed.