# Temporal Subtraction Technique for Thoracic MDCT Based on Residual VoxelMorph
## 5 Conclusions  
In this paper, we proposed a new temporal subtraction method with reduced subtraction image artifacts using a nonrigid image registration method based on a deep neural network modeled on ResidualVoxelMorph. The results showed that the proposed method reduced the RMSE by 11.3% on average compared with the conventional method with thoracic MDCT images as real data. By comparing the proposed method with the conventional method, high-speed processing of a temporal subtraction technique was possible, and it was confirmed that the image quality of the temporal subtraction image was improved because subtraction image artifacts have been removed from the temporal subtraction image. In the future, we plan to conduct semi-quantitative visual assessments by expert radiologists to assess the clinical relevance of this technique. Furthermore, as an improvement of the method, we plan to improve the accuracy by devising a deep neural network model that can be applied to new lesions for which no alignment support exists. In addition, we would like to consider introducing deep neural networks to select local regions in signal-processing-based registration, as this is expected to reduce costs and improve accuracy. Therefore, our preliminary results indicate that temporal subtraction images generated based on our proposed method are useful for radiologists in detecting interval changes on MDCT images.  
In short, the proposed new temporal subtraction method contributes to the construction of a 3D temporal subtraction technique that can be used for thoracic CT screening of lung cancer and that can rapidly generate subtraction images that can be interpreted more efficiently for early lesions that are difficult to detect with 2D X-ray images. Finally, the proposed technique to improve the performance of the observer can be expected and can contribute to the early case detection and treatment of low contrast lesions.  
Conceptualization, N.M.; methodology, N.M.; software, N.M.; validation, N.M.; formal analysis, N.M.; investigation, N.M.; resources, N.M.; data curation, N.M.; writing--original draft preparation, N.M.; writing--review and editing, H.L., T.K., T.A., S.K.; visualization, N.M.; supervision, T.K.; project administration, N.M. All authors have read and agreed to the published version of the manuscript.  
This research has been partially supported by the Kayamori Foundation of Informational Science Advancement and Grants-in-Aid for Scientific Research of JSPS KAKENHI (21H03840).  
Not applicable.  
The authors declare there is no conflict of interest.  
\begin{table}
\begin{tabular}{c|c c c|c} \hline  & **Group A** & **Group B** & **Group C** & **Mean** \\ \hline
**ResidualVoxelMorph + Proposed Method** & 102.2 (23.3) & 110.9 (29) & 105 (24.3) & 106 (25.5) \\
**VoxelMorph + Proposed Method** & 110.3 (23.7) & 120.1 (34.8) & 114 (25.3) & 114.8 (27.9) \\
**VoxelMorph + Conventional Method** & 111.7 (24.9) & 121.7 (30.9) & 120 (25.4) & 117.8 (27.1) \\ \hline \end{tabular}
\end{table}
Table 4: RMSE average values in the abnormal cases of all evaluated methods. Values in parentheses represent standard deviation of RMSE.  
\begin{table}
\begin{tabular}{c|c c c|c} \hline  & **Group A** & **Group B** & **Group C** & **Mean** \\ \hline
**ResidualVoxelMorph + Proposed Method** & 92.2 (10.4) & 111 (20.3) & 89.3 (9.2) & 97.5 (13.3) \\
**VoxelMorph + Proposed Method** & 101.4 (12) & 117.1 (18.7) & 101.8 (11.2) & 106.8 (14) \\
**VoxelMorph + Conventional Method** & 108.4 (15) & 130.1 (20.7) & 104.1 (16.2) & 114.2 (17.3) \\ \hline \end{tabular}
\end{table}
Table 5: RMSE average values in the normal cases of all evaluated methods. Values in parentheses represent standard deviation of RMSE.