Deep-learning-based breast CT for radiation dose reduction
Wenxiang Cong, Hongming Shan, Xiaohua Zhang, Shaohua Liu, Ruola Ning, Ge Wang, "Deep-learning-based breast CT for radiation dose reduction," Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131L (10 September 2019)
Abstract
Cone-beam breast computed tomography (CT) provides true 3D breast images with isotropic resolution and highcontrast information, detecting calcifications as small as a few hundred microns and revealing subtle tissue differences. However, breast is highly sensitive to x-ray radiation. It is critically important for healthcare to reduce radiation dose. Few-view cone-beam CT only uses a fraction of x-ray projection data acquired by standard cone-beam breast CT, enabling significant reduction of the radiation dose. However, insufficient sampling data would cause severe streak artifacts in images reconstructed using conventional methods. We propose a deep-learning-based method for the image reconstruction to establish a residual neural network model, which is applied for few-view breast CT to produce high quality breast CT images.