Literature DB >> 29116054

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

Cornel Zachiu1, Baudouin Denis de Senneville, Rob H N Tijssen, Alexis N T J Kotte, Antonetta C Houweling, Linda G W Kerkmeijer, Jan J W Lagendijk, Chrit T W Moonen, Mario Ries.   

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.

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Year:  2017        PMID: 29116054     DOI: 10.1088/1361-6560/aa990e

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  4 in total

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

Authors:  Jiwei Liu; Hui Yan; Hanlin Cheng; Jianfei Liu; Pengjian Sun; Boyi Wang; Ronghu Mao; Chi Du; Shengquan Luo
Journal:  Quant Imaging Med Surg       Date:  2021-12

2.  Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep learning methods for magnetic resonance (MR)-guided radiotherapy.

Authors:  Wen Li; Yafen Li; Wenjian Qin; Xiaokun Liang; Jianyang Xu; Jing Xiong; Yaoqin Xie
Journal:  Quant Imaging Med Surg       Date:  2020-06

3.  Clinical Assessment of a Novel Ring Gantry Linear Accelerator-Mounted Helical Fan-Beam kVCT System.

Authors:  Christian Velten; Lee Goddard; Kyoungkeun Jeong; Madhur K Garg; Wolfgang A Tomé
Journal:  Adv Radiat Oncol       Date:  2021-12-01

4.  A Deep Unsupervised Learning Model for Artifact Correction of Pelvis Cone-Beam CT.

Authors:  Guoya Dong; Chenglong Zhang; Xiaokun Liang; Lei Deng; Yulin Zhu; Xuanyu Zhu; Xuanru Zhou; Liming Song; Xiang Zhao; Yaoqin Xie
Journal:  Front Oncol       Date:  2021-07-16       Impact factor: 6.244

  4 in total

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