Deep-learning-based breast CT for radiation dose reduction

Wenxiang Cong; Hongming Shan; Xiaohua Zhang; Shaohua Liu; Ruola Ning; Ge Wang

doi:10.1117/12.2530234

10 September 2019 Deep-learning-based breast CT for radiation dose reduction

Wenxiang Cong, Hongming Shan, Xiaohua Zhang, Shaohua Liu, Ruola Ning, Ge Wang

Author Affiliations +

Proceedings Volume 11113, Developments in X-Ray Tomography XII; 111131L (2019) https://doi.org/10.1117/12.2530234
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

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. In this study, we respectively evaluate the breast image reconstruction from one third and one quarter of x-ray projection views of the standard cone-beam breast CT. Based on clinical breast imaging dataset, we perform a supervised learning to train the neural network from few-view CT images to corresponding full-view CT images. Experimental results show that the deep learning-based image reconstruction method allows few-view breast CT to achieve a radiation dose <6mGy per cone-beam CT scan which is a threshold set by FDA for mammographic screening.

Citation Download Citation

Wenxiang Cong, Hongming Shan, Xiaohua Zhang, Shaohua Liu, Ruola Ning, and Ge Wang "Deep-learning-based breast CT for radiation dose reduction", Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131L (10 September 2019); https://doi.org/10.1117/12.2530234

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