Non-rigid CT/CBCT to CBCT registration for online external beam radiotherapy guidance

Abstract

Image-guided external beam radiotherapy (EBRT) allows radiation dose deposition with a high degree of accuracy and precision. Guidance is usually achieved by estimating the displacements, via image registration, between cone beam computed tomography (CBCT) and computed tomography (CT) images acquired at different stages of the therapy. The resulting displacements are then used to reposition the patient such that the location of the tumor at the time of treatment matches its position during planning. Moreover, ongoing research aims to use CBCT-CT image registration for online plan adaptation. However, CBCT images are usually acquired using a small number of x-ray projections and/or low beam intensities. This often leads to the images being subject to low contrast, low signal-to-noise ratio and artifacts, which ends-up hampering the image registration process. Previous studies addressed this by integrating additional image processing steps into the registration procedure. However, these steps are usually designed for particular image acquisition schemes, therefore limiting their use on a case-by-case basis. In the current study we address CT to CBCT and CBCT to CBCT registration by the means of the recently proposed EVolution registration algorithm. Contrary to previous approaches, EVolution does not require the integration of additional image processing steps in the registration scheme. Moreover, the algorithm requires a low number of input parameters, is easily parallelizable and provides an elastic deformation on a point-by-point basis. Results have shown that relative to a pure CT-based registration, the intrinsic artifacts present in typical CBCT images only have a sub-millimeter impact on the accuracy and precision of the estimated deformation. In addition, the algorithm has low computational requirements, which are compatible with online image-based guidance of EBRT treatments.

Anatomically plausible models and quality assurance criteria for online mono- and multi-modal medical image registration

“…Therefore the results indicate that, in practice, as long as the acquisition sequence parameters do not change considerably, the algorithm parameters do not require re-calibration. This observation is also in good correspondence with previous studies (Zachiu et al 2015, Zachiu et al 2015b, Denis de Senneville et al 2016, Zachiu et al 2017a, Zachiu et al 2017b).…”

“…However, the transition towards the contrast-devoid area is resolved with high divergences in the estimated deformations, leading to strong misregistrations in the ventral part of the liver. Nevertheless, if sufficient contrast is available within the images, the EVolution algorithm was demonstrated to be capable of high accuracy and precision in several independent studies (Denis de Senneville et al 2016, Zachiu et al 2017a, Zachiu et al 2017b. Concerning the required computational time, the EVO algorithm introduces a rather large relative penalty, compared to the HSO and HSI.…”

Evaluating the benefit of PBS vs. VMAT dose distributions in terms of dosimetric sparing and robustness against inter-fraction anatomical changes for pediatric abdominal tumors

“…For the estimation of the HUs from the CBCTs, the planning-CT was deformably registered to each CBCT using Evolution [32]. This algorithm was previously validated for CT-CBCT registrations for kidney and lung cancer patients [33]. The performance of the registration algorithm was evaluated by visual inspection of tissue landmarks (e.g surgical clips).…”

CBCT-based synthetic CT generation using generative adversarial networks with disentangled representation

“…However, issues of huge artifacts and inaccurate Hounsfield Unit (HU) values preclude CBCT images from various clinical applications, such as the high-precision adaptive treatment planning which involves contour deformation, plan optimization, and dose calculation etc. (4)(5)(6)(7). For the sake of broadening the scope of clinical application for CBCT images, for instance in adaptive radiotherapy, highquality CBCT-to-CT image synthesis is highly demanded.…”