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Title: SU-F-J-58: Evaluation of RayStation Hybrid Deformable Image Registration for Accurate Contour Propagation in Adaptive Planning

Abstract

Purpose: Adaptive radiotherapy requires complete new sets of regions of interests (ROIs) delineation on the mid-treatment CT images. This work aims at evaluating the accuracy of the RayStation hybrid deformable image registration (DIR) algorithm for its overall integrity and accuracy in contour propagation for adaptive planning. Methods: The hybrid DIR is based on the combination of intensity-based algorithm and anatomical information provided by contours. Patients who received mid-treatment CT scans were identified for the study, including six lung patients (two mid-treatment CTs) and six head-and-neck (HN) patients (one mid-treatment CT). DIRpropagated ROIs were compared with physician-drawn ROIs for 8 ITVs and 7 critical organs (lungs, heart, esophagus, and etc.) for the lung patients, as well as 14 GTVs and 20 critical organs (mandible, eyes, parotids, and etc.) for the HN patients. Volume difference, center of mass (COM) difference, and Dice index were used for evaluation. Clinical-relevance of each propagated ROI was scored by two physicians, and correlated with the Dice index. Results: For critical organs, good agreement (Dice>0.9) were seen on all 7 for lung patients and 13 out of 20 for HN patients, with the rest requiring minimal edits. For targets, COM differences were within 5 mm on averagemore » for all patients. For Lung, 5 out of 8 ITVs required minimal edits (Dice 0.8–0.9), with the rest 2 needed re-drawn due to their small volumes (<10 cc). However, the propagated HN GTVs resulted in relatively low Dice values (0.5–0.8) due to their small volumes (3–40 cc) and high variability, among which 2 required re-drawn due to new nodal target identified on the mid-treatment CT scans. Conclusion: The hybrid DIR algorithm was found to be clinically useful and efficient for lung and HN patients, especially for propagated critical organ ROIs. It has potential to significantly improve the workflow in adaptive planning.« less

Authors:
; ; ; ; ;  [1]
  1. University of California Davis Medical Center, Sacramento, CA (United States)
Publication Date:
OSTI Identifier:
22632190
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ACCURACY; ALGORITHMS; COMPARATIVE EVALUATIONS; COMPUTERIZED TOMOGRAPHY; CRITICAL ORGANS; ESOPHAGUS; EYES; HEART; IMAGE PROCESSING; IMAGES; JAW; LUNGS; NECK; PATIENTS; PLANNING; RADIOTHERAPY

Citation Formats

Rong, Y, Rao, S, Daly, M, Wright, C, Benedict, S, and Yamamoto, T. SU-F-J-58: Evaluation of RayStation Hybrid Deformable Image Registration for Accurate Contour Propagation in Adaptive Planning. United States: N. p., 2016. Web. doi:10.1118/1.4955966.
Rong, Y, Rao, S, Daly, M, Wright, C, Benedict, S, & Yamamoto, T. SU-F-J-58: Evaluation of RayStation Hybrid Deformable Image Registration for Accurate Contour Propagation in Adaptive Planning. United States. doi:10.1118/1.4955966.
Rong, Y, Rao, S, Daly, M, Wright, C, Benedict, S, and Yamamoto, T. Wed . "SU-F-J-58: Evaluation of RayStation Hybrid Deformable Image Registration for Accurate Contour Propagation in Adaptive Planning". United States. doi:10.1118/1.4955966.
@article{osti_22632190,
title = {SU-F-J-58: Evaluation of RayStation Hybrid Deformable Image Registration for Accurate Contour Propagation in Adaptive Planning},
author = {Rong, Y and Rao, S and Daly, M and Wright, C and Benedict, S and Yamamoto, T},
abstractNote = {Purpose: Adaptive radiotherapy requires complete new sets of regions of interests (ROIs) delineation on the mid-treatment CT images. This work aims at evaluating the accuracy of the RayStation hybrid deformable image registration (DIR) algorithm for its overall integrity and accuracy in contour propagation for adaptive planning. Methods: The hybrid DIR is based on the combination of intensity-based algorithm and anatomical information provided by contours. Patients who received mid-treatment CT scans were identified for the study, including six lung patients (two mid-treatment CTs) and six head-and-neck (HN) patients (one mid-treatment CT). DIRpropagated ROIs were compared with physician-drawn ROIs for 8 ITVs and 7 critical organs (lungs, heart, esophagus, and etc.) for the lung patients, as well as 14 GTVs and 20 critical organs (mandible, eyes, parotids, and etc.) for the HN patients. Volume difference, center of mass (COM) difference, and Dice index were used for evaluation. Clinical-relevance of each propagated ROI was scored by two physicians, and correlated with the Dice index. Results: For critical organs, good agreement (Dice>0.9) were seen on all 7 for lung patients and 13 out of 20 for HN patients, with the rest requiring minimal edits. For targets, COM differences were within 5 mm on average for all patients. For Lung, 5 out of 8 ITVs required minimal edits (Dice 0.8–0.9), with the rest 2 needed re-drawn due to their small volumes (<10 cc). However, the propagated HN GTVs resulted in relatively low Dice values (0.5–0.8) due to their small volumes (3–40 cc) and high variability, among which 2 required re-drawn due to new nodal target identified on the mid-treatment CT scans. Conclusion: The hybrid DIR algorithm was found to be clinically useful and efficient for lung and HN patients, especially for propagated critical organ ROIs. It has potential to significantly improve the workflow in adaptive planning.},
doi = {10.1118/1.4955966},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}