title: Evaluation of Demons- and FEM-Based Registration Algorithms for Lung Cancer.
abstract: We evaluated and compared the accuracy of 2 deformable image registration algorithms in 4-dimensional computed tomography images for patients with lung cancer. Ten patients with non-small cell lung cancer or small cell lung cancer were enrolled in this institutional review board-approved study. The displacement vector fields relative to a specific reference image were calculated by using the diffeomorphic demons (DD) algorithm and the finite element method (FEM)-based algorithm. The registration accuracy was evaluated by using normalized mutual information (NMI), the sum of squared intensity difference (SSD), modified Hausdorff distance (dH_M), and ratio of gross tumor volume (rGTV) difference between reference image and deformed phase image. We also compared the registration speed of the 2 algorithms. Of all patients, the FEM-based algorithm showed stronger ability in aligning 2 images than the DD algorithm. The means (±standard deviation) of NMI were 0.86 (±0.05) and 0.90 (±0.05) using the DD algorithm and the FEM-based algorithm, respectively. The means of SSD were 0.006 (±0.003) and 0.003 (±0.002) using the DD algorithm and the FEM-based algorithm, respectively. The means of dH_M were 0.04 (±0.02) and 0.03 (±0.03) using the DD algorithm and the FEM-based algorithm, respectively. The means of rGTV were 3.9% (±1.01%) and 2.9% (±1.1%) using the DD algorithm and the FEM-based algorithm, respectively. However, the FEM-based algorithm costs a longer time than the DD algorithm, with the average running time of 31.4 minutes compared to 21.9 minutes for all patients. The preliminary results showed that the FEM-based algorithm was more accurate than the DD algorithm while compromised with the registration speed.
authors.affiliation: School of Information Science and Engineering, Shandong University, Jinan, Shandong, China.
authors.forename: Juan
authors.initials: J
authors.lastname: Yang
authors.affiliation: School of Physics and Electronic Science, Shandong Normal University, East Wenhua Road, Jinan, Shandong, China.
authors.forename: Dengwang
authors.initials: D
authors.lastname: Li
authors.affiliation: Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Yin
authors.affiliation: Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Jinan, Shandong, China.
authors.forename: Fen
authors.initials: F
authors.lastname: Zhao
authors.affiliation: School of Information Science and Engineering, Shandong University, Jinan, Shandong, China bright-4030@163.com hjw@sdu.edu.cn.
authors.forename: Hongjun
authors.initials: H
authors.lastname: Wang
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging*; D005260:Female; D020342:Finite Element Analysis; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D055752:Small Cell Lung Carcinoma / Q000000981:diagnostic imaging*
keywords: 4D CT; FEM-based algorithm; deformable image registration; diffeomorphic demons algorithm; registration accuracy