Progressive generative adversarial network for low-dose CT image noise reduction and artifact removal

• Main Authors: HAN XINGLONG, HAN ZEFANG, ZHANG XIONG, WANG ANHONG, SHANGGUAN HONG, CUI XUEYING
• Format: Patent
• Language: Chinese; English
• Published: 25.05.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

The invention belongs to the technical field of CT imaging, and particularly discloses a progressive generative adversarial network for low-dose CT image noise reduction and artifact removal, a double-generator nested sub-network is designed, each generator comprises a global feature denoiser and a local texture feature intensifier, the global feature denoiser performs feature extraction on a full-resolution input image; global features of the image are obtained; the local texture feature intensifier is used for carrying out feature extraction on an image with relatively low resolution after the input image is subjected to down-sampling, and local detail features of the image are captured; and an LDCT image noise reduction task is completed jointly. According to the invention, a shuffle discriminator network for multi-scale feature extraction is designed, the discrimination capability of a discriminator is improved and the stability and robustness of GAN adversarial training are enhanced while the complexity