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Proceedings Paper

Deep-learning-based breast CT for radiation dose reduction
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Paper 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.

Paper Details

Date Published: 10 September 2019
PDF: 7 pages
Proc. SPIE 11113, Developments in X-Ray Tomography XII, 111131L (10 September 2019); doi: 10.1117/12.2530234
Show Author Affiliations
Wenxiang Cong, Rensselaer Polytechnic Institute (United States)
Hongming Shan, Rensselaer Polytechnic Institute (United States)
Xiaohua Zhang, Koning Corp. (United States)
Shaohua Liu, Koning Corp. (United States)
Ruola Ning, Koning Corp. (United States)
Ge Wang, Rensselaer Polytechnic Institute (United States)

Published in SPIE Proceedings Vol. 11113:
Developments in X-Ray Tomography XII
Bert Müller; Ge Wang, Editor(s)

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