# DC2 Anet: Generating Lumbar Spine MR Images from CT Scan Data Based on Semi-Supervised Learning
## 2 Literature Review of Medical Imaging Synthesis
This method also used adversarial loss and voxel-wise loss together. Extending the above method, Nie et al. [37] proposed a context-aware GAN that utilized a 3D CNN [38; 39] and an auto-context model [40] to generate a CT sequence from an MR sequence. The voxel-wise loss of the 3D CNN learns both the spatial and temporal information of the sequence data. In contrast, Wolterink et al. [41] applied a cycle-consistent GAN (cycleGAN) [42] with least-squares adversarial loss [43] in which the loss term leads to the stable optimization of the network when synthesizing an MR image using a CT image. A cycle-consistent loss [42; 44] produces not only a synthetic image that looks real, but also one that is similar to the input under unsupervised learning. A CT-based MR image estimation method was first proposed by Jin et al. [45]. They proposed a synthesis system referred to as MR-GAN using a dual cycle-consistent structure. Their MR-GAN is trainable with paired and unpaired data together to improve performance. In addition to dual cycle-consistent loss, their objective function includes two other loss terms: adversarial loss and the voxel-wise loss. Table 2 presents a comparison of the network architectures and objective functions of the deep-neural-network-based medical synthesis methods. Compared to the methods in Table 2, this study proposes a more general system of the cross-modality synthesis, DC\({}^{2}\)Anet, that supports both supervised and unsupervised learning and a new objective function to balance quantitative and qualitative loss terms. The voxel-wise loss function measures the difference between the synthetic and the reference images, but it cannot reflect the perceptual difference between the two images. For example, even when two identical images have the same perceptual information, they will have very different voxel-wise loss measurements if they are offset from each other by just one pixel. Recent work has shown that high-quality synthetic images can be produced using perceptual loss based on differences between high-level feature representations extracted from a pretrained CNN [48; 49]. Gatys et al. [49] conducted artistic style transfer by jointly minimizing feature reconstruction loss [50] and style reconstruction loss based on features extracted from a pretrained VGG16 network [24]. Johnson et al. [48] produced visually-pleasing results using image style transformation and single-image super-resolution, with voxel-wise loss replaced by perceptual loss. Structural similarity (SSIM) [51; 52] is another qualitative measurement approach that is based on the human visual system and is used to compare local patterns of structural information that have been normalized for luminance and contrast. In our study, a similar structural loss term was proposed to retain the structural patterns of lumbar spine CT scans in the synthesis of MR images. Additionally, to balance quantitative and qualitative performance, gradient difference loss and perceptual loss were included based on adversarial, voxel-wise, and dual cycle-consistent loss.