Literature DB >> 25471959

Deformable image registration based automatic CT-to-CT contour propagation for head and neck adaptive radiotherapy in the routine clinical setting.

Akila Kumarasiri1, Farzan Siddiqui1, Chang Liu1, Raphael Yechieli1, Mira Shah1, Deepak Pradhan1, Hualiang Zhong1, Indrin J Chetty1, Jinkoo Kim1.   

Abstract

PURPOSE: To evaluate the clinical potential of deformable image registration (DIR)-based automatic propagation of physician-drawn contours from a planning CT to midtreatment CT images for head and neck (H&N) adaptive radiotherapy.
METHODS: Ten H&N patients, each with a planning CT (CT1) and a subsequent CT (CT2) taken approximately 3-4 week into treatment, were considered retrospectively. Clinically relevant organs and targets were manually delineated by a radiation oncologist on both sets of images. Four commercial DIR algorithms, two B-spline-based and two Demons-based, were used to deform CT1 and the relevant contour sets onto corresponding CT2 images. Agreement of the propagated contours with manually drawn contours on CT2 was visually rated by four radiation oncologists in a scale from 1 to 5, the volume overlap was quantified using Dice coefficients, and a distance analysis was done using center of mass (CoM) displacements and Hausdorff distances (HDs). Performance of these four commercial algorithms was validated using a parameter-optimized Elastix DIR algorithm.
RESULTS: All algorithms attained Dice coefficients of >0.85 for organs with clear boundaries and those with volumes >9 cm(3). Organs with volumes <3 cm(3) and/or those with poorly defined boundaries showed Dice coefficients of ∼ 0.5-0.6. For the propagation of small organs (<3 cm(3)), the B-spline-based algorithms showed higher mean Dice values (Dice = 0.60) than the Demons-based algorithms (Dice = 0.54). For the gross and planning target volumes, the respective mean Dice coefficients were 0.8 and 0.9. There was no statistically significant difference in the Dice coefficients, CoM, or HD among investigated DIR algorithms. The mean radiation oncologist visual scores of the four algorithms ranged from 3.2 to 3.8, which indicated that the quality of transferred contours was "clinically acceptable with minor modification or major modification in a small number of contours."
CONCLUSIONS: Use of DIR-based contour propagation in the routine clinical setting is expected to increase the efficiency of H&amp;N replanning, reducing the amount of time needed for manual target and organ delineations.

Entities:  

Mesh:

Year:  2014        PMID: 25471959     DOI: 10.1118/1.4901409

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  20 in total

1.  Cardiac Substructure Segmentation and Dosimetry Using a Novel Hybrid Magnetic Resonance and Computed Tomography Cardiac Atlas.

Authors:  Eric D Morris; Ahmed I Ghanem; Milan V Pantelic; Eleanor M Walker; Xiaoxia Han; Carri K Glide-Hurst
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-11-22       Impact factor: 7.038

2.  Validation of biomechanical deformable image registration in the abdomen, thorax, and pelvis in a commercial radiotherapy treatment planning system.

Authors:  Michael Velec; Joanne L Moseley; Stina Svensson; Björn Hårdemark; David A Jaffray; Kristy K Brock
Journal:  Med Phys       Date:  2017-06-01       Impact factor: 4.071

3.  Dosimetric study of Hounsfield number correction effect in areas influenced by contrast product in lungs case.

Authors:  Yassine Oulhouq; Dikra Bakari; Deae-Eddine Krim; Mustapha Zerfaoui; Abdeslem Rrhioua; Soufiane Berhili; Loubna Mezouar
Journal:  Rep Pract Oncol Radiother       Date:  2021-08-12

4.  Contour propagation using non-uniform cubic B-splines for lung tumor delineation in 4D-CT.

Authors:  Yongchuan Liu; Renchao Jin; Mi Chen; Enmin Song; Xiangyang Xu; Sheng Zhang; Chih-Cheng Hung
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-07-16       Impact factor: 2.924

5.  Cardiac substructure segmentation with deep learning for improved cardiac sparing.

Authors:  Eric D Morris; Ahmed I Ghanem; Ming Dong; Milan V Pantelic; Eleanor M Walker; Carri K Glide-Hurst
Journal:  Med Phys       Date:  2019-12-29       Impact factor: 4.071

6.  Clinical use, challenges, and barriers to implementation of deformable image registration in radiotherapy - the need for guidance and QA tools.

Authors:  Mohammad Hussein; Adeyemi Akintonde; Jamie McClelland; Richard Speight; Catharine H Clark
Journal:  Br J Radiol       Date:  2021-04-29       Impact factor: 3.039

7.  Evaluation of Deformable Image Registration-Based Contour Propagation From Planning CT to Cone-Beam CT.

Authors:  Andrew J Woerner; Mehee Choi; Matthew M Harkenrider; John C Roeske; Murat Surucu
Journal:  Technol Cancer Res Treat       Date:  2017-03-10

8.  The impact of robustness of deformable image registration on contour propagation and dose accumulation for head and neck adaptive radiotherapy.

Authors:  Lian Zhang; Zhi Wang; Chengyu Shi; Tengfei Long; X George Xu
Journal:  J Appl Clin Med Phys       Date:  2018-05-30       Impact factor: 2.102

9.  Replanning Criteria and Timing Definition for Parotid Protection-Based Adaptive Radiation Therapy in Nasopharyngeal Carcinoma.

Authors:  Wei-Rong Yao; Shou-Ping Xu; Bo Liu; Xiu-Tang Cao; Gang Ren; Lei Du; Fu-Gen Zhou; Lin-Chun Feng; Bao-Lin Qu; Chuan-Bin Xie; Lin Ma
Journal:  Biomed Res Int       Date:  2015-12-17       Impact factor: 3.411

10.  Performance variations among clinically available deformable image registration tools in adaptive radiotherapy - how should we evaluate and interpret the result?

Authors:  Ke Nie; Jean Pouliot; Eric Smith; Cynthia Chuang
Journal:  J Appl Clin Med Phys       Date:  2016-03-08       Impact factor: 2.102

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.