title: A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images.
abstract: Deformable image registration (DIR) of fractional high-dose-rate (HDR) CT images is challenging due to the presence of applicators in the brachytherapy image. Point-to-point correspondence fails because of the undesired deformation vector fields (DVF) propagated from the applicator region (AR) to the surrounding tissues, which can potentially introduce significant DIR errors in dose mapping. This paper proposes a novel segmentation and point-matching enhanced efficient DIR (named SPEED) scheme to facilitate dose accumulation among HDR treatment fractions. In SPEED, a semi-automatic seed point generation approach is developed to obtain the incremented fore/background point sets to feed the random walks algorithm, which is used to segment and remove the AR, leaving empty AR cavities in the HDR CT images. A feature-based 'thin-plate-spline robust point matching' algorithm is then employed for AR cavity surface points matching. With the resulting mapping, a DVF defining on each voxel is estimated by B-spline approximation, which serves as the initial DVF for the subsequent Demons-based DIR between the AR-free HDR CT images. The calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative analysis and visual inspection of the DIR results indicate that SPEED can suppress the impact of applicator on DIR, and accurately register HDR CT images as well as deform and add interfractional HDR doses.
authors: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People's Republic of China.
authors: Xin
authors: X
authors: Zhen
authors: Haibin
authors: H
authors: Chen
authors: Hao
authors: H
authors: Yan
authors: Linghong
authors: L
authors: Zhou
authors: Loren K
authors: LK
authors: Mell
authors: Catheryn M
authors: CM
authors: Yashar
authors: Steve
authors: S
authors: Jiang
authors: Xun
authors: X
authors: Jia
authors: Xuejun
authors: X
authors: Gu
authors: Laura
authors: L
authors: Cervino
mesh_terms: D000465:Algorithms; D001918:Brachytherapy / Q000379:methods; D005260:Female; D005833:Genital Neoplasms, Female / Q000532:radiotherapy; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D009369:Neoplasms / Q000000981:diagnostic imaging*; D009959:Oropharyngeal Neoplasms / Q000532:radiotherapy; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*