# Temporal Subtraction Technique for Thoracic MDCT Based on Residual VoxelMorph
## 1 Introduction  
Highly accurate images can be obtained in a short amount of time, and diagnostic imaging has become an indispensable part of medical care because of the development of medical imaging equipment such as computed tomography (CT) and magnetic resonance imaging (MRI). CT and MRI are three-dimensional tomographic images that provide detailed information on the inside of the body, contributing to more accurate diagnoses. However, the number of images per patient is huge, increasing the burden on physicians, who are responsible for reading the images for many patients. This burden may increase the possibility of misdiagnosis, such as overlooking a lesion. Therefore, computer-aided diagnosis (CAD) systems have been developed to reduce the burden on physicians and improve diagnostic accuracy and efficiency. CAD systems use computers to perform various image analyses, such as segmentation of organ areas, image registration, detection of lesions, and differentiation of detected lesions from benign to malignant, and provide analysis results as a \"second opinion\" to physicians [1]. Using the analysis results for diagnosis, radiologists, who must evaluate many images to identify lesions and determine a diagnosis, can increase their decision-making resources, improve the accuracy of diagnosis, and reduce the time required for diagnosis; and the results have become a useful tool for visual screening. In particular, there is a CAD system to streamline comparative reading, which is called a temporal subtraction technique. In the temporal subtraction technique, temporal subtraction images are obtained by subtracting theprevious image from the current image. It can highlight temporal differences, such as the appearance of new lesions or changes in existing abnormalities in the previous medical image, by removing most of the regions of the normal object. In actual interpretation, in reading diagnostic images of lung cancer, the interpreter reads comparing the previously taken images with the current images in order to detect new lesions and record temporal changes in existing lesions. Thus, the temporal subtraction technique can be used to present visually effective information to the physician. Furthermore, lung cancer is the leading cause of cancer-related deaths worldwide, and early diagnosis and detection is critical for survival [2], but subtle lesions, such as early lung nodules, tend to have low contrast. Since diagnosticians have a difficult task of interpreting in a limited amount of time, it is hoped that a development of a temporal subtraction method for the early diagnosis of lung cancer can also be realized. In reality, the usefulness of the temporal subtraction method for 2D X-ray images has been proven by several experiments for clinical evaluation, and its practical introduction is in progress [3]. Additionally, with screening using low-dose CT, a 20% reduction in the incidence of lung cancer-related deaths among smokers was reported by the National Lung Screening Trial [4]. It is expected that several types of CT scans will be further employed in lung screening in the future. In addition, according to the results of Aoki, the use of temporal subtraction images for chest CT images was reported to improve the detection accuracy of lesions for physicians [5]. Hence, there is a need for the practical application of a temporal subtraction technique for chest CT images. However, chest CT images often show misalignment between the current image and the previous image due to differences in the examination environment and imaging equipment. If the images are subtracted with image misalignment, the normal structure remains as an image artifact on the temporal subtraction image, because it cannot be not removed. These image artifacts can affect the interpretation of temporal subtraction images. To reduce these image artifacts, you need to generate a subtraction image by converting the previous image to a structure similar to the current image and subtracting it.