# Temporal Subtraction Technique for Thoracic MDCT Based on Residual VoxelMorph
## 4 Experiment
### Results and Evaluation  
Figure 6 shows examples of the results applied the proposed method to current and previous image pairs. Figure 6a shows a section of a previous image, (b) a section of current image, (c) a section of the temporal subtraction image, and (d) a volume-rendering capture image of the temporal subtraction images. There is a nodular shadow with a diameter of 6.8 mm in the circular mark on the current image and the temporal subtraction image. As shown in the cross-section of the temporal subtraction image in (c), we can see that the newly appearing nodular shadow is emphasized in the current image. Additionally, as observed in (d), although some residual artifacts are still present, lesions with temporal changes are sufficiently emphasized such that identifying their location is easy and effective for interpretation. Table 1 shows the range of the loss function values for the training sets, and Table 2 shows the range of the loss function values for the validation sets. These tables show that the loss function values are sufficiently small. However, the minimum loss function value of the training set is smaller than that of the validation set, which raises concerns about overlearning. Since the data set used in this experiment is small, future validation with a large amount of data is needed. The average prediction processing time of the proposed method was 84.4 s by using GPU. Cases where only the CPU and the proposed method were used took 277.7 s, which is faster than the conventional temporal subtraction technique [7], which requires several tens of minutes. Figure 6: Result images applied the proposal method to the current and previous CT images pair. (**a**) Shows a section of previous CT images, (**b**) a section of current CT images, (**c**) a section of temporal subtraction images, and (**d**) a volume rendering capture image of temporal subtraction images. The lesion is circled. The performance of the proposed method can be evaluated by comparing the temporal subtraction images generated using ResidualVoxelMorph as deep neural network models of the proposal method, those generated using the VoxelMorph as deep neural network models of the proposal method, and those generated by subtracting from deformed images using conventional VoxelMorph [6]. Figure 7 shows the (a) previous image, (b) current image, (c) temporal subtraction image from ResidualVoxelMorph with the proposed method, (d) temporal subtraction image from VoxelMorph with the proposed method, and (e) temporal subtraction image from conventional VoxelMorph. The current images show a nodular shadow with a diameter of 6 mm. As shown in Figure 7c-e, compared to conventional VoxelMorph, the proposed method suppressed the appearance of artifacts and highlighted the lesion. This is largely owed to the difference in the computed region of the NCC within the loss function during training. This is because conventional VoxelMorph uses the NCC of the entire image as the image similarity in the loss function, whereas the proposed method calculates the NCC only within the lung field, thereby allowing the construction of a network that can preferentially register structures within the lung field. Furthermore, the temporal subtraction images were blurred in conventional VoxelMorph. This is because of the GPU device memory size during training, which is reduced to 160 \(\times\) 192 \(\times\) 224 pixels in conventional VoxelMorph, whereas the proposed method divides the image into patches without reduction. Therefore, the proposed method suppresses blurring. Furthermore, comparing (c) and (e) in Figure 7, the artifacts were suppressed when ResidualVoxelMorph was used in the deep neural network model. This is because the residual blocks enable more efficient training. However, some lesions in the temporal difference images obtained by applying ResidualVoxelMorph with the proposed method were depicted as missing, although they were not present in the previous images, such as in the lower part of the lesion in Figure 7c.