基于深度学习的低剂量CT成像算法研究进展

计算机断层扫描成像(CT)技术具有成像速度快分辨率高的优点,广泛应用于医学临床诊断中.然而,提高剂量辐射会引发人体组织器官受损,降低剂量又会造成成像质量严重下降.为解决上述矛盾,在确保成像质量满足临床诊断需求的条件下,研究如何最大程度地降低X射线辐射对人体造成的伤害,己成为低剂量CT成像技术的研究热点.近年来,在人工智...

Advances in Research on Low-dose CT Imaging Algorithm Based on Deep Learning

Computed tomography (CT) is widely used in clinical diagnosis because of its fast imaging speed and high resolution. However, higher doses of radiation will cause damages to human tissues and organs, while lower doses will lead to serious deterioration of imaging quality. In order to solve the above contradiction, researchers have focused on the low-dose CT imaging technology to study how to reduce the harm caused by radiation to the human body to the greatest extent under the condition of ensuring the imaging quality to meet the needs of clinical diagnosis. In recent years, deep learning has developed rapidly in the field of artificial intelligence, and has been widely used in image processing, pattern recognition, signal processing fields. Driven by big data, LDCT imaging algorithms based on deep learning have made great progress. This paper studies the development of low-dose CT imaging algorithms in recent years in terms of three aspects: the process of CT imaging, the noise modeling of low-dose CT, and the design of imaging algorithms. In particular, the imaging algorithms in the field of deep learning are systematically elaborated and analyzed. Finally, future developments in the field of LDCT image artifact suppression are also prospected.