Literature DB >> 24090743

Evaluation of the Block Matching deformable registration algorithm in the field of head-and-neck adaptive radiotherapy.

S Huger1, P Graff2, V Harter2, V Marchesi2, P Royer2, J C Diaz3, S Aouadi3, D Wolf4, D Peiffert5, A Noel5.   

Abstract

BACKGROUND AND
PURPOSE: To compare the accuracy of the Block Matching deformable registration (DIR) against rigid image registration (RIR) for head-and-neck multi-modal images CT to cone-beam CT (CBCT) registration.
MATERIAL AND METHODS: Planning-CT and weekly CBCT of 10 patients were used for this study. Several volumes, including medullary canal (MC), thyroid cartilage (TC), hyoid bone (HB) and submandibular gland (SMG) were transposed from CT to CBCT images using either DIR or RIR. Transposed volumes were compared with the manual delineation of these volumes on every CBCT. The parameters of similarity used for analysis were: Dice Similarity Index (DSI), 95%-Hausdorff Distance (95%-HD) and difference of volumes (cc).
RESULTS: With DIR, the major mean difference of volumes was -1.4 cc for MC, revealing limited under-segmentation. DIR limited variability of DSI and 95%-HD. It significantly improved DSI for TC and HB and 95%-HD for all structures but SMG. With DIR, mean 95%-HD (mm) was 3.01 ± 0.80, 5.33 ± 2.51, 4.99 ± 1.69, 3.07 ± 1.31 for MC, TC, HB and SMG, respectively. With RIR, it was 3.92 ± 1.86, 6.94 ± 3.98, 6.44 ± 3.37 and 3.41 ± 2.25, respectively.
CONCLUSION: Block Matching is a valid algorithm for deformable multi-modal CT to CBCT registration. Values of 95%-HD are useful for ongoing development of its application to the cumulative dose calculation.
Copyright © 2013 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  CT–CBCT registration; Deformable algorithm; Head and neck cancer

Mesh:

Year:  2013        PMID: 24090743     DOI: 10.1016/j.ejmp.2013.09.001

Source DB:  PubMed          Journal:  Phys Med        ISSN: 1120-1797            Impact factor:   2.685


  5 in total

1.  Quality assurance assessment of diagnostic and radiation therapy-simulation CT image registration for head and neck radiation therapy: anatomic region of interest-based comparison of rigid and deformable algorithms.

Authors:  Abdallah S R Mohamed; Manee-Naad Ruangskul; Musaddiq J Awan; Charles A Baron; Jayashree Kalpathy-Cramer; Richard Castillo; Edward Castillo; Thomas M Guerrero; Esengul Kocak-Uzel; Jinzhong Yang; Laurence E Court; Michael E Kantor; G Brandon Gunn; Rivka R Colen; Steven J Frank; Adam S Garden; David I Rosenthal; Clifton D Fuller
Journal:  Radiology       Date:  2014-11-07       Impact factor: 11.105

2.  Methodology for analysis and reporting patterns of failure in the Era of IMRT: head and neck cancer applications.

Authors:  Abdallah S R Mohamed; David I Rosenthal; Musaddiq J Awan; Adam S Garden; Esengul Kocak-Uzel; Abdelaziz M Belal; Ahmed G El-Gowily; Jack Phan; Beth M Beadle; G Brandon Gunn; Clifton D Fuller
Journal:  Radiat Oncol       Date:  2016-07-26       Impact factor: 3.481

3.  Comprehensive evaluation of ten deformable image registration algorithms for contour propagation between CT and cone-beam CT images in adaptive head & neck radiotherapy.

Authors:  Xin Li; Yuyu Zhang; Yinghua Shi; Shuyu Wu; Yang Xiao; Xuejun Gu; Xin Zhen; Linghong Zhou
Journal:  PLoS One       Date:  2017-04-17       Impact factor: 3.240

4.  Estimating the accumulative dose uncertainty for intracavitary and interstitial brachytherapy.

Authors:  Binbing Wang; Weibiao Hu; Guoping Shan; Xiaoxian Xu
Journal:  Biomed Eng Online       Date:  2021-10-18       Impact factor: 2.819

5.  The suitability of common metrics for assessing parotid and larynx autosegmentation accuracy.

Authors:  William J Beasley; Alan McWilliam; Adam Aitkenhead; Ranald I Mackay; Carl G Rowbottom
Journal:  J Appl Clin Med Phys       Date:  2016-03-08       Impact factor: 2.102

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.