基于深度学习的低剂量CT成像算法研究进展

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

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,已成为低剂量CT成像技术的研究热点。近年来,在人工智能领域深度学习方法快速发展,已广泛应用于图像处理、模式识别、信号处理等领域。与此同时,大数据驱动下的深度学习方法在LDCT成像领域的应用也有了长足的发展。本文从CT成像的过程、低剂量CT噪声建模以及成像算法的设计3方面,介绍近年来国内外低剂量CT成像算法的发展,尤其对深度学习领域的成像算法进行阐述与分析,并对LDCT图像成像领域未来的发展进行展望。

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 lowdose 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.