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Title: Assessment of a Model-Based Deformable Image Registration Approach for Radiation Therapy Planning

Abstract

Purpose: The aim of this study is to develop a surface-based deformable image registration strategy and to assess the accuracy of the system for the integration of multimodality imaging, image-guided radiation therapy, and assessment of geometrical change during and after therapy. Methods and Materials: A surface-model-based deformable image registration system has been developed that enables quantitative description of geometrical change in multimodal images with high computational efficiency. Based on the deformation of organ surfaces, a volumetric deformation field is derived using different volumetric elasticity models as alternatives to finite-element modeling. Results: The accuracy of the system was assessed both visually and quantitatively by tracking naturally occurring landmarks (bronchial bifurcations in the lung, vessel bifurcations in the liver, implanted gold markers in the prostate). The maximum displacements for lung, liver and prostate were 5.3 cm, 3.2 cm, and 0.6 cm respectively. The largest registration error (direction, mean {+-} SD) for lung, liver and prostate were (inferior-superior, -0.21 {+-} 0.38 cm) (anterior-posterior, -0.09 {+-} 0.34 cm), and (left-right, 0.04 {+-} 0.38 cm) respectively, which was within the image resolution regardless of the deformation model. The computation time (2.7 GHz Intel Xeon) was on the order of seconds (e.g., 10 s for 2more » prostate datasets), and deformed axial images could be viewed at interactive speed (less than 1 s for 512 x 512 voxels). Conclusions: Surface-based deformable image registration enables the quantification of geometrical change in normal tissue and tumor with acceptable accuracy and speed.« less

Authors:
 [1];  [2];  [3];  [2];  [4];  [2]
  1. Philips Radiation Oncology Systems, Fitchburg, WI (United States). E-mail: Michael.kaus@philips.com
  2. Princess Margaret Hospital, Radiation Medicine Program, Toronto, ON (Canada)
  3. Philips Research Laboratories, Hamburg (Germany)
  4. BC Cancer Agency, Department of Radiation Oncology, Vancouver, BC (Canada)
Publication Date:
OSTI Identifier:
20951680
Resource Type:
Journal Article
Resource Relation:
Journal Name: International Journal of Radiation Oncology, Biology and Physics; Journal Volume: 68; Journal Issue: 2; Other Information: DOI: 10.1016/j.ijrobp.2007.01.056; PII: S0360-3016(07)00240-4; Copyright (c) 2007 Elsevier Science B.V., Amsterdam, Netherlands, All rights reserved; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; ACCURACY; BIFURCATION; CARCINOMAS; ELASTICITY; GHZ RANGE 01-100; IMAGES; LIVER; LUNGS; PLANNING; PROSTATE; RADIOTHERAPY

Citation Formats

Kaus, Michael R., Brock, Kristy K., Pekar, Vladimir, Dawson, Laura A., Nichol, Alan M., and Jaffray, David A. Assessment of a Model-Based Deformable Image Registration Approach for Radiation Therapy Planning. United States: N. p., 2007. Web. doi:10.1016/j.ijrobp.2007.01.056.
Kaus, Michael R., Brock, Kristy K., Pekar, Vladimir, Dawson, Laura A., Nichol, Alan M., & Jaffray, David A. Assessment of a Model-Based Deformable Image Registration Approach for Radiation Therapy Planning. United States. doi:10.1016/j.ijrobp.2007.01.056.
Kaus, Michael R., Brock, Kristy K., Pekar, Vladimir, Dawson, Laura A., Nichol, Alan M., and Jaffray, David A. Fri . "Assessment of a Model-Based Deformable Image Registration Approach for Radiation Therapy Planning". United States. doi:10.1016/j.ijrobp.2007.01.056.
@article{osti_20951680,
title = {Assessment of a Model-Based Deformable Image Registration Approach for Radiation Therapy Planning},
author = {Kaus, Michael R. and Brock, Kristy K. and Pekar, Vladimir and Dawson, Laura A. and Nichol, Alan M. and Jaffray, David A.},
abstractNote = {Purpose: The aim of this study is to develop a surface-based deformable image registration strategy and to assess the accuracy of the system for the integration of multimodality imaging, image-guided radiation therapy, and assessment of geometrical change during and after therapy. Methods and Materials: A surface-model-based deformable image registration system has been developed that enables quantitative description of geometrical change in multimodal images with high computational efficiency. Based on the deformation of organ surfaces, a volumetric deformation field is derived using different volumetric elasticity models as alternatives to finite-element modeling. Results: The accuracy of the system was assessed both visually and quantitatively by tracking naturally occurring landmarks (bronchial bifurcations in the lung, vessel bifurcations in the liver, implanted gold markers in the prostate). The maximum displacements for lung, liver and prostate were 5.3 cm, 3.2 cm, and 0.6 cm respectively. The largest registration error (direction, mean {+-} SD) for lung, liver and prostate were (inferior-superior, -0.21 {+-} 0.38 cm) (anterior-posterior, -0.09 {+-} 0.34 cm), and (left-right, 0.04 {+-} 0.38 cm) respectively, which was within the image resolution regardless of the deformation model. The computation time (2.7 GHz Intel Xeon) was on the order of seconds (e.g., 10 s for 2 prostate datasets), and deformed axial images could be viewed at interactive speed (less than 1 s for 512 x 512 voxels). Conclusions: Surface-based deformable image registration enables the quantification of geometrical change in normal tissue and tumor with acceptable accuracy and speed.},
doi = {10.1016/j.ijrobp.2007.01.056},
journal = {International Journal of Radiation Oncology, Biology and Physics},
number = 2,
volume = 68,
place = {United States},
year = {Fri Jun 01 00:00:00 EDT 2007},
month = {Fri Jun 01 00:00:00 EDT 2007}
}
  • Purpose: Measuring changes in lung perfusion resulting from radiation therapy dose requires registration of the functional imaging to the radiation therapy treatment planning scan. This study investigates registration accuracy and utility for positron emission tomography (PET)/computed tomography (CT) perfusion imaging in radiation therapy for non–small cell lung cancer. Methods: {sup 68}Ga 4-dimensional PET/CT ventilation-perfusion imaging was performed before, during, and after radiation therapy for 5 patients. Rigid registration and deformable image registration (DIR) using B-splines and Demons algorithms was performed with the CT data to obtain a deformation map between the functional images and planning CT. Contour propagation accuracy andmore » correspondence of anatomic features were used to assess registration accuracy. Wilcoxon signed-rank test was used to determine statistical significance. Changes in lung perfusion resulting from radiation therapy dose were calculated for each registration method for each patient and averaged over all patients. Results: With B-splines/Demons DIR, median distance to agreement between lung contours reduced modestly by 0.9/1.1 mm, 1.3/1.6 mm, and 1.3/1.6 mm for pretreatment, midtreatment, and posttreatment (P<.01 for all), and median Dice score between lung contours improved by 0.04/0.04, 0.05/0.05, and 0.05/0.05 for pretreatment, midtreatment, and posttreatment (P<.001 for all). Distance between anatomic features reduced with DIR by median 2.5 mm and 2.8 for pretreatment and midtreatment time points, respectively (P=.001) and 1.4 mm for posttreatment (P>.2). Poorer posttreatment results were likely caused by posttreatment pneumonitis and tumor regression. Up to 80% standardized uptake value loss in perfusion scans was observed. There was limited change in the loss in lung perfusion between registration methods; however, Demons resulted in larger interpatient variation compared with rigid and B-splines registration. Conclusions: DIR accuracy in the data sets studied was variable depending on anatomic changes resulting from radiation therapy; caution must be exercised when using DIR in regions of low contrast or radiation pneumonitis. Lung perfusion reduces with increasing radiation therapy dose; however, DIR did not translate into significant changes in dose–response assessment.« less
  • Purpose: To quantify adequate anisotropic clinical target volume (CTV)-to-planning target volume (PTV) margins for three different setup strategies used during prostate irradiation: (1) no setup corrections, (2) on-line corrections determined from bony anatomy, and (3) on-line corrections determined from gold markers. Method and Materials: Three radiation oncologists independently delineated the CTV on computed tomography images of 30 prostate cancer patients. Eight repeat scans were acquired to allow simulation of the delivered dose distributions in changing geometry. Different registration approaches were taken to mimic the different setup strategies. A surface model-based deformable image registration system was used to warp the deliveredmore » dose distributions back to the dose in the planning computed tomography scan. On the basis of the geometric extent of the underdosed areas, a set of anisotropic margins was derived to ensure a minimal dose to the CTV of 95% for 90% of the patients. Results: Without setup correction, margins of approximately 11 mm for the corpus of the prostate and 15 mm for the seminal vesicles were required. These margins could be reduced to 8 and 13 mm when aligning the patient to the bony anatomy and to 3 and 8 mm aligning the patient to implanted gold markers. A larger margin at the apex was required to account for the significant observer variability and steep dose gradients at this location (11 mm using skin marker registration, 9 mm using bony anatomy registration, and 6 mm using gold marker registration). Conclusions: Novel voxel tracking techniques have enabled us to calculate accumulated dose distributions and design accurate three-dimensional CTV-to-PTV margins for prostate irradiation.« less
  • Purpose: Online adaptive therapy (ART) relies on auto-contouring using deformable image registration (DIR). DIR’s inherent uncertainties require user intervention and manual edits while the patient is on the table. We investigated the dosimetric impact of DIR errors on the quality of re-optimized plans, and used the findings to establish regions for focusing manual edits to where DIR errors can Result in clinically relevant dose differences. Methods: Our clinical implementation of online adaptive MR-IGRT involves using DIR to transfer contours from CT to daily MR, followed by a physicians’ edits. The plan is then re-optimized to meet the organs at riskmore » (OARs) constraints. Re-optimized abdomen and pelvis plans generated based on physician edited OARs were selected as the baseline for evaluation. Plans were then re-optimized on auto-deformed contours with manual edits limited to pre-defined uniform rings (0 to 5cm) around the PTV. A 0cm ring indicates that the auto-deformed OARs were used without editing. The magnitude of the variations caused by the non-deterministic optimizer was quantified by repeat re-optimizations on the same geometry to determine the mean and standard deviation (STD). For each re-optimized plan, various volumetric parameters for the PTV, the OARs were extracted along with DVH and isodose evaluation. A plan was deemed acceptable if the variation from the baseline plan was within one STD. Results: Initial results show that for abdomen and pancreas cases, a minimum of 5cm margin around the PTV is required for contour corrections, while for pelvic and liver cases a 2–3 cm margin is sufficient. Conclusion: Focusing manual contour edits to regions of dosimetric relevance can reduce contouring time in the online ART process while maintaining a clinically comparable plan. Future work will further refine the contouring region by evaluating the path along the beams, dose gradients near the target and OAR dose metrics.« less
  • Purpose: To evaluate the implications of differences between contours drawn manually and contours generated automatically by deformable image registration for four-dimensional (4D) treatment planning. Methods and Materials: In 12 lung cancer patients intensity-modulated radiotherapy (IMRT) planning was performed for both manual contours and automatically generated ('auto') contours in mid and peak expiration of 4D computed tomography scans, with the manual contours in peak inspiration serving as the reference for the displacement vector fields. Manual and auto plans were analyzed with respect to their coverage of the manual contours, which were assumed to represent the anatomically correct volumes. Results: Auto contoursmore » were on average larger than manual contours by up to 9%. Objective scores, D{sub 2%} and D{sub 98%} of the planning target volume, homogeneity and conformity indices, and coverage of normal tissue structures (lungs, heart, esophagus, spinal cord) at defined dose levels were not significantly different between plans (p = 0.22-0.94). Differences were statistically insignificant for the generalized equivalent uniform dose of the planning target volume (p = 0.19-0.94) and normal tissue complication probabilities for lung and esophagus (p = 0.13-0.47). Dosimetric differences >2% or >1 Gy were more frequent in patients with auto/manual volume differences {>=}10% (p = 0.04). Conclusions: The applied deformable image registration algorithm produces clinically plausible auto contours in the majority of structures. At this stage clinical supervision of the auto contouring process is required, and manual interventions may become necessary. Before routine use, further investigations are required, particularly to reduce imaging artifacts.« less
  • Purpose: The pleural volumes will deform during surgery portion of the pleural photodynamic therapy (PDT) of lung cancer when the pleural cavity is opened. This impact the delivered dose when using highly conformal treatment techniques. In this study, a finite element-based (FEM) deformable image registration is used to quantify the anatomical variation between the contours for the pleural cavities obtained in the operating room and those determined from pre-surgery computed tomography (CT) scans. Methods: An infrared camera-based navigation system (NDI) is used during PDT to track the anatomical changes and contour the lung and chest cavity. A series of CTsmore » of the lungs, in the same patient, are also acquired before the surgery. The structure contour of lung and the CTs are processed and contoured in Matlab and MeshLab. Then, the contours are imported into COMSOL Multiphysics 5.0, where the FEM-based deformable image registration is obtained using the deformed mesh - moving mesh (ALE) model. The NDI acquired lung contour is considered as the reference contour, and the CT contour is used as the target one, which will be deformed. Results: The reconstructed three-dimensional contours from both NDI and CT can be converted to COMSOL so that a three-dimensional ALE model can be developed. The contours can be registered using COMSOL ALE moving mesh model, which takes into account the deformation along x, y and z-axes. The deformed contour has good matches to the reference contour after the dynamic matching process. The resulting 3D deformation map can be used to obtain the locations of other critical anatomic structures, e.g., heart, during surgery. Conclusion: Deformable image registration can fuse images acquired by different modalities. It provides insights into the development of phenomenon and variation in normal anatomical structures over time. The initial assessments of three-dimensional registration show good agreement.« less