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In Bioengineering (Basel, Switzerland) ; h5-index 0.0

X-ray computed tomography (CT) is widely used in clinical practice. The involved ionizingX-ray radiation, however, could increase cancer risk. Hence, the reduction of the radiation dosehas been an important topic in recent years. Few-view CT image reconstruction is one of the mainways to minimize radiation dose and potentially allow a stationary CT architecture. In this paper,we propose a deep encoder-decoder adversarial reconstruction (DEAR) network for 3D CT imagereconstruction from few-view data. Since the artifacts caused by few-view reconstruction appear in3D instead of 2D geometry, a 3D deep network has a great potential for improving the image qualityin a data driven fashion. More specifically, our proposed DEAR-3D network aims at reconstructing3D volume directly from clinical 3D spiral cone-beam image data. DEAR is validated on a publiclyavailable abdominal CT dataset prepared and authorized by Mayo Clinic. Compared with other2D deep learning methods, the proposed DEAR-3D network can utilize 3D information to producepromising reconstruction results.

Xie Huidong, Shan Hongming, Wang Ge


deep encoder-decoder adversarial network (DEAR), deep learning, few-view CT, generative adversarial network (GAN), machine learning, sparse-view CT