Registration of clinical volumes to beams‐eye‐view images for real‐time tracking

Abstract

Purpose: The authors combine the registration of 2D beam's eye view (BEV) images and 3D planning computed tomography (CT) images, with relative, markerless tumor tracking to provide automatic absolute tracking of physician defined volumes such as the gross tumor volume (GTV). Methods: During treatment of lung SBRT cases, BEV images were continuously acquired with an electronic portal imaging device (EPID) operating in cine mode. For absolute registration of physician-defined volumes, an intensity based 2D/3D registration to the planning CT was performed using the end-of-exhale (EoE) phase of the four dimensional computed tomography (4DCT). The volume was converted from Hounsfield units into electron density by a calibration curve and digitally reconstructed radiographs (DRRs) were generated for each beam geometry. Using normalized cross correlation between the DRR and an EoE BEV image, the best in-plane rigid transformation was found. The transformation was applied to physician-defined contours in the planning CT, mapping them into the EPID image domain. A robust multiregion method of relative markerless lung tumor tracking quantified deviations from the EoE position. Results: The success of 2D/3D registration was demonstrated at the EoE breathing phase. By registering at this phase and then employing a separate technique for relative tracking, the authors are able to successfully track target volumes in the BEV images throughout the entire treatment delivery. Conclusions: Through the combination of EPID/4DCT registration and relative tracking, a necessary step toward the clinical implementation of BEV tracking has been completed. The knowledge of tumor volumes relative to the treatment field is important for future applications like real-time motion management, adaptive radiotherapy, and delivered dose calculations. C 2014 American Association of Physicists in Medicine. [http://dx

Technical Note: Combination of multiple EPID imager layers improves image quality and tracking performance of low contrast‐to‐noise objects

“…For example, a tracking algorithm developed by Rottmann et al has been used to estimate tumor movement by automatically detected imaging features on sequential EPID frames . This tracking algorithm has found many potential applications in the clinic, including the estimation of delivered radiation dose, tumor localization, and real‐time dynamic multileaf collimator (DMLC) tracking …”

“…[3][4][5] For example, a tracking algorithm developed by Rottmann et al has been used to estimate tumor movement by automatically detected imaging features on sequential EPID frames. 4 This tracking algorithm has found many potential applications in the clinic, including the estimation of delivered radiation dose, 6 tumor localization, 7,8 and real-time dynamic multileaf collimator (DMLC) tracking. 9 Despite the promise of auto-tracking in adaptive radiotherapy and motion management, the performance can be greatly hindered by the EPID image quality, especially compared to kilovoltage x-ray systems.…”

Markerless tracking of small lung tumors for stereotactic radiotherapy

“…If a tumor happens to be temporarily out of the planned position, the dosimetric differences may be exacerbated compared to treatment using longer beam on times. 6 The feasibility of markerless lung tumor tracking in 2D has been reported by a number of groups using the MV treatment beam [7][8][9][10][11][12] or kV imaging systems. [13][14][15] Using EPID movie-based tumor tracking, Richter et al 7 showed in 40 patients treated using static beams that the difference between automatic and manual tracking was a maximum of 2 mm for 98.3% in the X-direction and 89.4% in the Y -direction.…”

Markerless tumor tracking using short kilovoltage imaging arcs for lung image-guided radiotherapy

“…Several markerless lung tumor tracking methods have been proposed using MV electronic portal imaging devices (EPID) (Richter et al , 2010; Rottmann et al , 2013; Bryant et al , 2014; Serpa et al , 2014) or kV imaging systems (Hugo et al , 2010; Lewis et al , 2010; Gendrin et al , 2012; Yang et al , 2012; van Sornsen de Koste et al , 2015). MV EPID based methods are limited by the treatment field size and poor contrast.…”