title: Feature-based rectal contour propagation from planning CT to cone beam CT.
abstract: The purpose of this work is to develop a novel feature-based registration strategy to automatically map the rectal contours from planning computed tomography (CT) (pCT) to cone beam CT (CBCT). The rectal contours were manually outlined on the pCT. A narrow band with the outlined contour as its interior surface was then constructed, so that we can exclude the volume inside the rectum in the registration process. The corresponding contour in the CBCT was found by using a feature-based registration algorithm, which consists of two steps: (1) automatically searching for control points in the pCT and CBCT based on the features of the surrounding tissue and matching the homologous control points using the scale invariance feature transformation; and (2) using the control points for a thin plate spline transformation to warp the narrow band and mapping the corresponding contours from pCT to CBCT. The proposed contour propagation technique is applied to digital phantoms and clinical cases and, in all cases, the contour mapping results are found to be clinically acceptable. For clinical cases, the method yielded satisfactory results even when there were significant rectal content changes between the pCT and CBCT scans. As a consequence, the accordance between the rectal volumes after deformable registration and the manually segmented rectum was found to be more than 90%. The proposed technique provides a powerful tool for adaptive radiotherapy of prostate, rectal, and gynecological cancers in the future.
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California 94305-5847, USA.
authors.forename: Yaoqin
authors.initials: Y
authors.lastname: Xie
authors.forename: Ming
authors.initials: M
authors.lastname: Chao
authors.forename: Percy
authors.initials: P
authors.lastname: Lee
authors.forename: Lei
authors.initials: L
authors.lastname: Xing
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011881:Radiotherapy, Computer-Assisted / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000379:methods; D012004:Rectal Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity