# Temporal Subtraction Technique for Thoracic MDCT Based on Residual VoxelMorph
## 2 Literature Review
### Deep Neural Networks-Based Methods for Medical Image Registration  
Recently, research on deep neural networks have been actively conducted to achieve medical image registration. For example, [PERSON] proposed the use of 3D CNNs to achieve deformable registration of inhalation-exhalation 3D lung CT image volumes [14]. This technique provides deformable registration that preserves the appearance of a realistic image through a series of multiscale and random transformations of aligned image pairs, in a way that does not require manual annotation of ground truth data. In addition, like other methods of generating ground-truth data, CNNs can be trained in a supervised way using a relatively small number of medical images. The processing is real-time across applications, and it can enable robust registration with supervised transformation estimation. However, as a limitation to consider, first of all, it should be noted that the quality of registration using this framework depends on the quality of grand-truth, and this technique depends on the practitioner's expertise in the quality of these labels. Additionally, these labels are complicated due to the relatively small number of individuals who have the necessary expertise to perform such registrations. Although these limitations can be addressed by transforming training data and generating composite ground-truth data, it is essential to make sure that the synthesized data closely resembles clinical data. This motivated several groups to explore unsupervised approaches [15; 16]. One important innovation that has been useful in these studies is the spatial transformation network (STN) [17]. Several methods use STN to perform transformations relevant to their registration applications [18; 19]. [PERSON] used a CNN- and STN-inspired framework to perform a deformable registration of T1-weighted brain MR volumes [19]. The loss function consists of an NCC term and a regularization term. They compared their method to VoxelMorph [6] and uTllzReg GeoShoot [20] using the LBPA40 and Mindboggle 101 datasets, and demonstrated superior performance compared to both methods. Furthermore, these image registration methods do not explicitly enforce criteria that guarantee topology preservation, which often results in loss of structural information and inaccurate registration. To overcome the potential degeneracy problem of registration fields, [PERSON] presented a deformed image registration method called CycleMorph [21]. CycleMorph forces the deformed image back into the original image, thereby providing cycle consistency between image pairs. Experiments on various image datasets have confirmed that CycleMorph provides topology-preserving image deformations for arbitrary image pairs, resulting in significant performance gains. However, existing deep neural network-based registration methods employ networks such as CNNs, which have a limited ability to extract global image features and thus, cannot effectively capture long-range dependencies in video and fixed images. Therefore, [PERSON] proposed a network called Transformer-UNet (TUNet) to effectively capture long-range dependence in video and fixed images [22]. This network introduces a vision transformer (VIT) into the UNet framework to extract global and local features from moving and fixed images, thereby generating a deformation field. To validate its performance, experiments were conducted using LPBA40 and OASIS-1, and qualitative and quantitative evaluations demonstrated that it provides a high registration accuracy.