Optimizing early cancer diagnosis and detection using a temporal subtraction technique

• Published in: Technological forecasting & social change Vol. 167; p. 120745
• Main Authors: Miyake, Noriaki, Lu, Huinmin, Kamiya, Tohru, Aoki, Takatoshi, Kido, Shoji
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.06.2021; Elsevier B.V; Elsevier Science Ltd
• Subjects: Analysis; Cancer; Computed tomography; Computer aided diagnosis; CT imaging; Diagnosis; Digital health; Finite Element Method; Lung cancer; Medical analytics; Medical colleges; Medical diagnosis; Medical imaging; Medical imaging equipment; Medicine; Methods; Non-Rigid Image Registration; Optimization; Registration; Subtraction; Temporal subtraction technique; Time; Digital health; Medical analytics; Computer aided diagnosis; Temporal subtraction technique; Finite Element Method; Non-Rigid Image Registration
• ISSN: 0040-1625; 1873-5509
• DOI: 10.1016/j.techfore.2021.120745

•Finite element method approach is proposed to achieve highly accurate registration from a healthcare perspective.•CAD construction is proposed by generating CT temporal subtraction images using high-precision registration.•A solution to the problem of artifacts on temporal images is proposed by using FEM in selected local regions.•FEM in selected local regions is proposed that can control increase of the processing cost. To optimize the early diagnosis and detection of lung cancer, computer-aided diagnostic (CAD) systems have been a useful tool for analyzing medical images. The temporal subtraction technique, which is a CAD system, performs the subtraction operation between the current image and the previous image on the same patient, and supports observation by emphasizing the temporal changes. However, the temporal subtraction technique for 3D images, such as thoracic CT images, has not yet been established. There is a need to develop efficient and highly accurate 3D nonrigid registration techniques to reduce subtraction artifacts. This study aims to develop a 3D nonrigid registration technique to establish a 3D temporal subtraction technique. In particular, we focus on the Finite Element Method, which is versatile, applicable to a wide range of fields, and capable of handling any shape. Our new method was examined on 46 clinical cases with multidetector row computed tomography images. As a result, the proposed method improved by 6.93% (p = 3.0 × 10−6) compared to the conventional methods in terms of the rate of reduction of artifacts, and the effectiveness was verified. Therefore, this study contributes to the literature on early detection and treatment.