Automated 2D-3D registration of a radiograph and a cone beam CT using line-segment enhancement. Academic Article uri icon

Overview

abstract

  • The objective of this study was to develop a fully automated two-dimensional (2D)-three-dimensional (3D) registration framework to quantify setup deviations in prostate radiation therapy from cone beam CT (CBCT) data and a single AP radiograph. A kilovoltage CBCT image and kilovoltage AP radiograph of an anthropomorphic phantom of the pelvis were acquired at 14 accurately known positions. The shifts in the phantom position were subsequently estimated by registering digitally reconstructed radiographs (DRRs) from the 3D CBCT scan to the AP radiographs through the correlation of enhanced linear image features mainly representing bony ridges. Linear features were enhanced by filtering the images with "sticks," short line segments which are varied in orientation to achieve the maximum projection value at every pixel in the image. The mean (and standard deviations) of the absolute errors in estimating translations along the three orthogonal axes in millimeters were 0.134 (0.096) AP(out-of-plane), 0.021 (0.023) ML and 0.020 (0.020) SI. The corresponding errors for rotations in degrees were 0.011 (0.009) AP, 0.029 (0.016) ML (out-of-plane), and 0.030 (0.028) SI (out-of-plane). Preliminary results with megavoltage patient data have also been reported. The results suggest that it may be possible to enhance anatomic features that are common to DRRs from a CBCT image and a single AP radiography of the pelvis for use in a completely automated and accurate 2D-3D registration framework for setup verification in prostate radiotherapy. This technique is theoretically applicable to other rigid bony structures such as the cranial vault or skull base and piecewise rigid structures such as the spine.

publication date

  • May 1, 2006

Research

keywords

  • Algorithms
  • Imaging, Three-Dimensional
  • Prostatic Neoplasms
  • Radiographic Image Enhancement
  • Radiographic Image Interpretation, Computer-Assisted
  • Subtraction Technique
  • Tomography, Spiral Computed

Identity

PubMed Central ID

  • PMC2796183

Scopus Document Identifier

  • 33646437820

Digital Object Identifier (DOI)

  • 10.1118/1.2192621

PubMed ID

  • 16752576

Additional Document Info

volume

  • 33

issue

  • 5