CT temporal subtraction: techniques and clinical applications.

Quant Imaging Med Surg 2021;11(6):2214-2223 | http://dx.doi.org/10.21037/qims-20-1367 Computed tomography (CT) has been the main noninvasive diagnostic technique for evaluating lung lesions. Although multidetector CT (MDCT) enables simultaneous increased z-axis coverage and thinner slice collimation, large numbers of axial images are generated by MDCT, which leads to reviewer fatigue during interpretation (1). Human perceptual errors, therefore, currently seem to be one of the most significant limiting factors in the detection of small lung lesions (2). Bone is one of the most common sites for metastasis in cancer along with lung. Although CT is a routine imaging modality to survey many types of cancer, bone metastases are often missed at CT because of their subtle findings. Other modalities, such as bone scintigraphy and positron emission tomography (PET), are useful to detect bone metastasis, but they still must be identified at CT anatomically. In order to solve these problems, computer aided diagnosis (CAD) systems have attracted attention in recent years (3). Improvement of image interpretation speed and image analysis accuracy is expected by using the CAD system because it can reduce burden on interpreting physicians and reduce variation in diagnostic accuracy. Temporal subtraction (TS), which is one of the computeraided detection (CADe) techniques, can remove most of the normal structures, such as blood vessels, ribs, muscles, by performing subtraction calculation processing between the current and the previous images of the same subject (4). CTTS technique can enhance the subtle change between the CT images. Therefore, it has been developed for CADe of small or ground-glass lung nodules and small or faint bone metastases. In available CT-CAD system lesion candidates are usually indicated by symbol/mark on a computeroutput image, however, in the TS system observers refer to the subtraction image without symbol/mark and then judge whether a “lesion” exist or not. Therefore, diagnostic performance is heavily affected by the image quality of the subtraction image. To obtain a high-quality subtraction image without subtraction artifacts, image registration is a key technology. Up to date, there are many rigid and nonrigid image registration techniques. If the image warping is incorrect, normal structures remain as the artifacts on the subtraction image, and as a result the image quality can be degraded. Many image warping techniques have been developed based on 2-dimensional (2D) images using a chest radiography (5-7), and TS on plain radiograph have already been commercially available. In general, CT images which is obtained different time series have temporal changes such as shape, size and location. A TS image on thoracic CT, which is obtained by subtraction of a previous image from a current one, has a 3-dimensional (3D) structure and the deformation of the subject in the axial direction needs to be considered 3D registration technique is necessary. Since early 2000, several attempts have been made to develop an image warping technique based on 3D images using thoracic MDCT images (8,9). Although the quality of the subtraction images based on 3D was relatively good in general, misregistration still appeared as artifacts on the subtraction images. It is, however, necessary to employ a more complex 3D registration, since the TS image obtained from two successive CT scans can have noticeable Editorial