Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image

Computed tomography (CT) and structural magnetic resonance (MR) images are both important and widely applied in the treatment planning of radiotherapy (Balter et  al., 1998; Chen et al., 2004; Khoo et al., 1997; Schad et al., 1987). Recently, it has become desirable to synthesize CT image from the corresponding MR scan. For example, quantitative positron emission tomography (PET) requires CT image for attenuation correction (Carney et al., 2006; Kinahan et al., 1998; Pan et al., 2005). The approach for CT-based attenuation correction is to transform the CT image, which is expressed in Hounsfield units, into an estimate of the linear attenuation map.

Source: Medical Image Analysis - March 29, 2018 Category: Radiology Authors: Lei Xiang, Qian Wang, Dong Nie, Lichi Zhang, Xiyao Jin, Yu Qiao, Dinggang Shen Source Type: research