Literature DB >> 27259084

Evaluation of deformable image registration for contour propagation between CT and cone-beam CT images in adaptive head and neck radiotherapy.

X Li1, Y Y Zhang2, Y H Shi2, L H Zhou1, X Zhen1.   

Abstract

Deformable image registration (DIR) is a critical technic in adaptive radiotherapy (ART) to propagate contours between planning computerized tomography (CT) images and treatment CT/Cone-beam CT (CBCT) image to account for organ deformation for treatment re-planning. To validate the ability and accuracy of DIR algorithms in organ at risk (OAR) contours mapping, seven intensity-based DIR strategies are tested on the planning CT and weekly CBCT images from six Head & Neck cancer patients who underwent a 6 ∼ 7 weeks intensity-modulated radiation therapy (IMRT). Three similarity metrics, i.e. the Dice similarity coefficient (DSC), the percentage error (PE) and the Hausdorff distance (HD), are employed to measure the agreement between the propagated contours and the physician delineated ground truths. It is found that the performance of all the evaluated DIR algorithms declines as the treatment proceeds. No statistically significant performance difference is observed between different DIR algorithms (p> 0.05), except for the double force demons (DFD) which yields the worst result in terms of DSC and PE. For the metric HD, all the DIR algorithms behaved unsatisfactorily with no statistically significant performance difference (p= 0.273). These findings suggested that special care should be taken when utilizing the intensity-based DIR algorithms involved in this study to deform OAR contours between CT and CBCT, especially for those organs with low contrast.

Entities:  

Keywords:  Adaptive radiotherapy; CBCT; contour propagation; deformable image registration; head & neck cancer

Mesh:

Year:  2016        PMID: 27259084     DOI: 10.3233/THC-161204

Source DB:  PubMed          Journal:  Technol Health Care        ISSN: 0928-7329            Impact factor:   1.285


  3 in total

1.  Automatic evaluation of contours in radiotherapy planning utilising conformity indices and machine learning.

Authors:  Samsara Terparia; Romaana Mir; Yat Tsang; Catharine H Clark; Rushil Patel
Journal:  Phys Imaging Radiat Oncol       Date:  2020-12-01

2.  Clinical Enhancement in AI-Based Post-processed Fast-Scan Low-Dose CBCT for Head and Neck Adaptive Radiotherapy.

Authors:  Wen Chen; Yimin Li; Nimu Yuan; Jinyi Qi; Brandon A Dyer; Levent Sensoy; Stanley H Benedict; Lu Shang; Shyam Rao; Yi Rong
Journal:  Front Artif Intell       Date:  2021-02-11

3.  Comparison of an in-house hybrid DIR method to NiftyReg on CBCT and CT images for head and neck cancer.

Authors:  Chunling Jiang; Yuling Huang; Shenggou Ding; Xiaochang Gong; Xingxing Yuan; Shaobin Wang; Jingao Li; Yun Zhang
Journal:  J Appl Clin Med Phys       Date:  2022-01-27       Impact factor: 2.102

  3 in total

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