Automated 2D-3D registration of portal images and CT data using line-segment enhancement Academic Article Article uri icon

Overview

MeSH Major

  • Brain Neoplasms
  • Magnetic Resonance Imaging
  • Radiosurgery

abstract

  • In prostate radiotherapy, setup errors with respect to the patient's bony anatomy can be reduced by aligning 2D megavoltage (MV) portal images acquired during treatment to a reference 3D kilovoltage (kV) CT acquired for treatment planning purposes. The purpose of this study was to evaluate a fully automated 2D-3D registration algorithm to quantify setup errors in 3D through the alignment of line-enhanced portal images and digitally reconstructed radiographs computed from the CT. The line-enhanced images were obtained by correlating the images with a filter bank of short line segments, or "sticks" at different orientations. The proposed methods were validated on (1) accurately collected gold-standard data consisting of a 3D kV cone-beam CT scan of an anthropomorphic phantom of the pelvis and 2D MV portal images in the anterior-posterior (AP) view acquired at 15 different poses and (2) a conventional 3D kV CT scan and weekly 2D MV AP portal images of a patient over 8weeks. The mean (and standard deviation) of the absolute registration error for rotations around the right-lateral (RL), inferior-superior (IS), and posterior-anterior (PA) axes were 0.212° (0.214°), 0.055° (0.033°) and 0.041° (0.039°), respectively. The corresponding registration errors for translations along the RL, IS, and PA axes were 0.161(0.131)mm, 0.096(0.033)mm, and 0.612(0.485)mm. The mean (and standard deviation) of the total registration error was 0.778(0.543)mm. Registration on the patient images was successful in all eight cases as determined visually. The results indicate that it is feasible to automatically enhance features in MV portal images of the pelvis for use within a completely automated 2D-3D registration framework for the accurate determination of patient setup errors. They also indicate that it is feasible to estimate all six transformation parameters from a 3D CT of the pelvis and a single portal image in the AP view.

publication date

  • October 6, 2008

Research

keywords

  • Academic Article

Identity

Digital Object Identifier (DOI)

  • 10.1118/1.2975143

PubMed ID

  • 18975681

Additional Document Info

start page

  • 4352

end page

  • 4361

volume

  • 35

number

  • 10