title: Learning anatomy changes from patient populations to create artificial CT images for voxel-level validation of deformable image registration.
abstract: The purpose of this study was to develop an approach to generate artificial computed tomography (CT) images with known deformation by learning the anatomy changes in a patient population for voxel-level validation of deformable image registration. Using a dataset of CT images representing anatomy changes during the course of radiation therapy, we selected a reference image and registered the remaining images to it, either directly or indirectly, using deformable registration. The resulting deformation vector fields (DVFs) represented the anatomy variations in that patient population. The mean deformation, computed from the DVFs, and the most prominent variations, which were captured using principal component analysis (PCA), composed an active shape model that could generate random known deformations with realistic anatomy changes based on those learned from the patient population. This approach was applied to a set of 12 head and neck patients who received intensity-modulated radiation therapy for validation. Artificial planning CT and daily CT images were generated to simulate a patient with known anatomy changes over the course of treatment and used to validate the deformable image registration between them. These artificial CT images potentially simulated the actual patients' anatomies and also showed realistic anatomy changes between different daily CT images. They were used to successfully validate deformable image registration applied to intrapatient deformation.
authors.affiliation: The University of Texas MD Anderson Cancer Center; Christiana Health Care Systems. zyu@christianacare.org.
authors.forename: Z Henry
authors.initials: ZH
authors.lastname: Yu
authors.forename: Rajat
authors.initials: R
authors.lastname: Kudchadker
authors.forename: Lei
authors.initials: L
authors.lastname: Dong
authors.forename: Yongbin
authors.initials: Y
authors.lastname: Zhang
authors.forename: Laurence E
authors.initials: LE
authors.lastname: Court
authors.forename: Firas
authors.initials: F
authors.lastname: Mourtada
authors.forename: Adam
authors.initials: A
authors.lastname: Yock
authors.forename: Susan L
authors.initials: SL
authors.lastname: Tucker
authors.forename: Jinzhong
authors.initials: J
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006258:Head and Neck Neoplasms / Q000473:pathology* / Q000532:radiotherapy; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D019047:Phantoms, Imaging; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted; D050397:Radiotherapy, Intensity-Modulated; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*