METHODS AND SYSTEMS FOR SELECTIVE REMOVAL OF STREAK ARTIFACTS AND NOISE FROM IMAGES USING DEEP NEURAL NETWORKS

• Main Authors: WANG XINZENG, LITWILLER DANIEL VANCE, BAYRAM ERSIN, MCKINNON GRAEME COLIN, LEBEL ROBERT MARC, MANDAVA SAGAR
• Format: Patent
• Language: Chinese; English
• Published: 24.09.2021
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; PHYSICS

The invention relates to methods and systems for selective removal of streak artifacts and noise from images using deep neural networks. Methods and systems are provided for independently removing streak artifacts and noise from medical images, using trained deep neural networks. In one embodiment, streak artifacts and noise may be selectively and independently removed from a medical image by receiving the medical image comprising streak artifacts and noise, mapping the medical image to a streak residual and a noise residual using the trained deep neural network, subtracting the streak residual from the medical image to a first extent, and subtracting the noise residual from the medical image to a second extent, to produce a de-noised medical image, and displaying the de-noised medical image via a display device. 本发明题为"用于使用深度神经网络从图像中选择性地去除条纹伪影和噪声的方法和系统"。提供了用于使用受过训练的深度神经网络独立地从医学图像中去除条纹伪影和噪声的方法和系统。在一个实施方案中，可通过以下方式选择性地且独立地从医学图像中去除条纹伪影和噪声：接收包括条纹伪影和噪声的医学图像，使用受过训练的深度神经网络将医学图像映射到条纹残余和噪声残余，在第一程度上从医学图像中减去条纹残余，并且在第二程度上