Biomechanical deformable image registration of longitudinal lung CT images using vessel information.
Spatial correlation of lung tissue across longitudinal images, as the patient responds to treatment, is a critical step in adaptive radiotherapy. The goal of this work is to expand a biomechanical model-based deformable registration algorithm (Morfeus) to achieve accurate registration in the presence of significant anatomical changes. Six lung cancer patients previously treated with conventionally fractionated radiotherapy were retrospectively evaluated. Exhale CT scans were obtained at treatment planning and following three weeks of treatment. For each patient, the planning CT was registered to the follow-up CT using Morfeus, a biomechanical model-based deformable registration algorithm. To model the complex response of the lung, an extension to Morfeus has been developed: an initial deformation was estimated with Morfeus consisting of boundary conditions on the chest wall and incorporating a sliding interface with the lungs. It was hypothesized that the addition of boundary conditions based on vessel tree matching would provide a robust reduction of the residual registration error. To achieve this, the vessel trees were segmented on the two images by thresholding a vesselness image based on the Hessian matrix's eigenvalues. For each point on the reference vessel tree centerline, the displacement vector was estimated by applying a variant of the Demons registration algorithm between the planning CT and the deformed follow-up CT. An expert independently identified corresponding landmarks well distributed in the lung to compute target registration errors (TRE). The TRE was: [Formula: see text], [Formula: see text] and [Formula: see text] mm after rigid registration, Morfeus and Morfeus with boundary conditions on the vessel tree, respectively. In conclusion, the addition of boundary conditions on the vessels significantly improved the accuracy in modeling the response of the lung and tumor over the course of radiotherapy. Minimizing and modeling these geometrical uncertainties will enable future plan adaptation strategies.
Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48109, USA.
Guillaume
G
Cazoulat
Dawn
D
Owen
Martha M
MM
Matuszak
James M
JM
Balter
Kristy K
KK
Brock
D000465:Algorithms; D001696:Biomechanical Phenomena; D001808:Blood Vessels / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008137:Longitudinal Studies; D008168:Lung / Q000098:blood supply* / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000098:blood supply / Q000000981:diagnostic imaging / Q000628:therapy; D055595:Mechanical Phenomena; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed