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Validation of an Improved 'Diffeomorphic Demons' Algorithm for Deformable Image Registration in Image-Guided Radiation Therapy
图像引导放射治疗中用于可变形图像配准的改进的“区别伪魔法”算法的验证作者机构：Guangzhou Med Univ Affiliated Tumor Hosp Radiotherapy Ctr Guangzhou 510095 Guangdong Peoples R China Southern Med Univ Dept Biomed Engn Guangzhou 510095 Guangdong Peoples R China
出 版 物：《BIO-MEDICAL MATERIALS AND ENGINEERING》
年 卷 期：2014年第24卷第1期
页 面：373-382页
核心收录：
学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 1001[医学-基础医学(可授医学、理学学位)] 0805[工学-材料科学与工程（可授工学、理学学位）] 10[医学]
主 题：Demons algorithm deformable registration gradient constancy assumption efficient second-order minimization L-BFGS algorithm
摘 要：Deformable image registration (DIR) was widely used in radiation therapy, such as in automatic contour generation, dose accumulation, tumor growth or regression analysis. To achieve higher registration accuracy and faster convergence, an improved diffeomorphic demons registration algorithm was proposed and validated. Based on Brox et al. s gradient constancy assumption and Malis s efficient second-order minimization (ESM) algorithm, a grey value gradient similarity term and a transformation error term were added into the demons energy function, and a formula was derived to calculate the update of transformation field. The limited Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm was used to optimize the energy function so that the iteration number could be determined automatically. The proposed algorithm was validated using mathematically deformed images and physically deformed phantom images. Compared with the original diffeomorphic demons algorithm, the registration method proposed achieve a higher precision and a faster convergence speed. Due to the influence of different scanning conditions in fractionated radiation, the density range of the treatment image and the planning image may be different. In such a case, the improved demons algorithm can achieve faster and more accurate radiotherapy.
