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1283 poster ACCURACY OF DEFORMABLE IMAGE REGISTRATION FOR CONTOUR PROPAGATION IN ADAPTIVE LUNG RADIOTHERAPY
I MAGE REGISTRATION , SEGMENTATION ALGORITHMS AND DELINEATION
Purpose: Deformable image registration, such as the intensity based BSpline Registration Method (BSRM), can help to track changes in shape and position of anatomical structures obtained during the course of radiotherapy. Such a registration is essential for dose accumulation in adaptive radiotherapy. The aim of this work is to evaluate the accuracy of the restored voxel displacements of the BSRM in the head and neck (H&N) area. In particular the effects on the registration result of parameter settings of the used algorithm, image contrast, and image mottle were studied. Materials: Sixty CT images in the H&N area of a patient and of a synthetic H&N phantom were utilized. The phantom contained spherical and elliptical structures simulating a tumour and several H&N organs. From the patient and the phantom set, additional CT sets were constructed by imposing Gaussian shaped deformations up to 20 mm. The phantom set was further modified by 1) addition of mottle of 1 up to 200 HU (1SD) and 2) addition of contrast discs, simulating structures of various densities between -375 and 375 HU. The BSRM of the Insight Toolkit 1 combined with Elastix 2 was used to register the various deformed patient and phantom CT sets back to their original, undeformed set. The effect of the number of iterations and the B-Spline grid spacing on the registration was investigated. Displacement vectors calculated in the deformable registration were compared with the imposed (known) deformations. Average and maximal Residual Voxel Displacements (RVD) in a volume of interest (e.g. parotid gland) were calculated and used as a measure for the (Local) positional accuracy (LRVD). Results: As a result of BSRMthe average LRVD decreased from 12 mm to less than 1 mm, and the maximal LRVD from 20 mm to 3 mm for about 100 iterations. The best result for the imposed 20 mm deformations was found for a B-spline gridsize of 15 mm. With increased mottle variation for the phantom up to 50 HU (1SD), the LRVD remained < 1.0 mm. To obtain LRVD values between 1.0 and 1.5 mm for mottle variations of 100 and 200 HU (1SD), 6 times more iterations were needed. Similarly, for low and zero contrast of the discs, 2.5 times more iterations were needed to obtain average LRVD values < 1.5 mm. An average tracking accuracy after registration of better than 1.0 mm for parotid gland was found in this study. In some areas, however, LRVD values of 3-4 mm were observed. Conclusions: We presented a method to evaluate the accuracy of a deformable image registration application. The BSRM seems to be suitable for structures like the parotid and submandibular glands in the H&N region. Careful tuning of parameter values like B-spline grid size and number of iterations is important to obtain an average accuracy of about 1-2 mm in acceptable computation time. References: 1. ITK: www.itk.org 2. Elastix: www.isi.uu.nl/Elastix 1283 poster ACCURACY OF DEFORMABLE IMAGE REGISTRATION FOR CONTOUR PROPAGATION IN ADAPTIVE LUNG RADIOTHERAPY N. Hardcastle1 , W. van Elmpt2 , M. Oechsner3 , M. Guckenberger3 , D. De Ruysscher2 , K. Bzdusek4 , W. Tomé1 1 U NIVERSITY OF W ISCONSIN S CHOOL OF M EDICINE AND P UBLIC H EALTH, Human Oncology, Madison, USA 2 MAASTRO C LINIC, Maastricht, Netherlands 3 U NIVERSITY H OSPITAL W UERZBURG, Radiation Oncology, Wuerzburg, Germany 4 P HILIPS H EALTHCARE, Fitchburg, WI, USA Purpose: Anatomical changes are often observed during radiotherapy treatment of lung cancer. Both normal tissues and tumor volumes can deform over time. Obtaining volumetric images during treatment fractions allows tracking of any anatomical changes for adaptive protocols. This approach however requires each volumetric image to be re-contoured. Currently, re-contouring of images during a radiotherapy course can be very time-intensive. Automatic propagation of target and OAR contours between two image sets is thus an attractive approach to reducing adaptive radiotherapy resource requirements. The aim of this study was to evaluate two deformable image registration (DIR) algorithms for the purpose of propagating contours from pre-treatment to midtreatment CT scans. Materials: Pre-treatment and mid-treatment kVCT scans were obtained for 15 patients. The primary tumor (GTV), nodal tumor (GTV-nodes), lungs, esophagus and spinal cord were delineated by a physician on each of the pre and mid-treatment scans. DIR was performed by warping the pre-treatment to the mid-treatment scans and applying the deformation map to the pretreatment scan ROIs to obtain ROIs on the mid-treatment scan. Two DIR algorithms were used Demons (Thirion) and Salient Feature Based Registration (SFBR), as implemented in a research version of the Pinnacle RTPs (v9.1, Philips Medical Systems, Fitchburg, WI). The DIR-propagated ROIs were compared with the physician-drawn ROIs on the mid-treatment scan using the Dice score and mean of the slicewise Hausdorff distance . The difference in the Centre of Mass (COM) position of the GTVs was also measured. Results: The average metric scores for each of the five ROIs are shown in Figure 1. The DICE scores for normal structures were greater than for GTV volumes. The mean Dice score was greater for SFBR than for Demons for all organs with the exception of right lung. The average Hausdorff distance was lower with SFBR with all ROIs with the exception of the right lung. The
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average COM difference for the GTV-tumor was 0.42 cm and 0.36 cm and GTV-nodes was 0.40 cm and 0.35 cm for demons and SFBR respectively.
Figure 1: Dice score (top row) and mean DTA (cm) (bottom row) for the five structures investigated. The black circle is the mean, the grey bar is ± 1 SD and the black horizontal bars are the minimum and maximum values. Conclusions: Two DIR algorithms were successfully used to automatically propagate both normal tissue and target volumes in repeat lung scans. Reasonably good agreement with physician drawn contours was observed for normal tissues. DIR-propagated nodal contours were not as accurate most probably due to less soft tissue contrast for these structures. 1284 poster AUTOMATED 2D/3D REGISTRATION FOR LUNG BRACHYTHERAPY P. Zvonarev1 , T. Farrell1 2 , R. Hunter2 , J. Hayward2 , M. Wierzbicki2 , R. Sur3 M C M ASTER U NIVERSITY, Medical Physics Applied Radiation Sciences, Hamilton, Canada 2 J URAVINSKI C ANCER C ENTRE, Medical Physics, Hamilton, Ontario, Canada 3 J URAVINSKI C ANCER C ENTRE, Oncology, Hamilton, Ontario, Canada
1
Purpose: High dose rate brachytherapy is an effective modality of treatment for obstructive lung cancer. For treatment planning, orthogonal X-ray images are used to reconstruct the treatment catheters. However, the poor soft tissue contrast of X-ray images does not allow visualization of the tumor or organs at risk. Diagnostic CT images are available for each patient prior to treatment; however these images cannot be directly fused with the orthogonal x-rays due to differences in patient positioning. To account for this, a 2D/3D registration algorithm was developed to register the orthogonal images to the CT volume, thereby allowing for reconstruction of the treatment catheters in the CT data set. Materials: An image-based, rigid body registration algorithm was developed. The best-neighbour search optimization was smoothed by parabolic interpolation. Image similarity was quantified using a combination of normalized mutual information (NMI) and the sum of intensity differences. Evaluation of the algorithm was performed using an anthropomorphic thorax phantom. Two narrow channels were drilled in the phantom allowing for treatment catheters to be inserted into the lungs. The phantom was placed in a custom body mould to achieve reproducible positioning.CT images of the phantom were acquired with and without the inserted treatment catheters (CT+, CT-). Orthogonal X-ray images were collected with the catheters in place and with the phantom shifted and rotated in 3D for eight unique positions. The position variation range was ±50 mm and ±15◦ for translations and rotations respectively. ECG leads were attached to the phantom at their normal anatomical locations to mimic an actual lung treatment procedure. The orthogonal images were registered with the CT- data using the registration algorithm. The catheter coordinates from the CT+ data and the X-ray images were digitized using BrachyVision software. Results: The coordinates from the orthogonal pair reconstruction were transformed and were fused into the CT+ data. The transformed data were compared with the positions directly digitized in the CT+ images. Registration error was quantified by computing the distance between the fused and true catheter positions and varied from 0.5 mm to 2.5 mm for both catheters in eight different data sets. The mean error was 1.9 mm. Conclusions: The proposed registration algorithm showed robustness to various degrees of initial displacement and to external objects introduced during orthogonal image acquisition. The registration error was acceptable for clinical applications.This work was supported by Varian Research Grant #07-583.
Purpose: Deformable image registration, such as the intensity based BSpline Registration Method (BSRM), can help to track changes in shape and position of anatomical structures obtained during the course of radiotherapy. Such a registration is essential for dose accumulation in adaptive radiotherapy. The aim of this work is to evaluate the accuracy of the restored voxel displacements of the BSRM in the head and neck (H&N) area. In particular the effects on the registration result of parameter settings of the used algorithm, image contrast, and image mottle were studied. Materials: Sixty CT images in the H&N area of a patient and of a synthetic H&N phantom were utilized. The phantom contained spherical and elliptical structures simulating a tumour and several H&N organs. From the patient and the phantom set, additional CT sets were constructed by imposing Gaussian shaped deformations up to 20 mm. The phantom set was further modified by 1) addition of mottle of 1 up to 200 HU (1SD) and 2) addition of contrast discs, simulating structures of various densities between -375 and 375 HU. The BSRM of the Insight Toolkit 1 combined with Elastix 2 was used to register the various deformed patient and phantom CT sets back to their original, undeformed set. The effect of the number of iterations and the B-Spline grid spacing on the registration was investigated. Displacement vectors calculated in the deformable registration were compared with the imposed (known) deformations. Average and maximal Residual Voxel Displacements (RVD) in a volume of interest (e.g. parotid gland) were calculated and used as a measure for the (Local) positional accuracy (LRVD). Results: As a result of BSRMthe average LRVD decreased from 12 mm to less than 1 mm, and the maximal LRVD from 20 mm to 3 mm for about 100 iterations. The best result for the imposed 20 mm deformations was found for a B-spline gridsize of 15 mm. With increased mottle variation for the phantom up to 50 HU (1SD), the LRVD remained < 1.0 mm. To obtain LRVD values between 1.0 and 1.5 mm for mottle variations of 100 and 200 HU (1SD), 6 times more iterations were needed. Similarly, for low and zero contrast of the discs, 2.5 times more iterations were needed to obtain average LRVD values < 1.5 mm. An average tracking accuracy after registration of better than 1.0 mm for parotid gland was found in this study. In some areas, however, LRVD values of 3-4 mm were observed. Conclusions: We presented a method to evaluate the accuracy of a deformable image registration application. The BSRM seems to be suitable for structures like the parotid and submandibular glands in the H&N region. Careful tuning of parameter values like B-spline grid size and number of iterations is important to obtain an average accuracy of about 1-2 mm in acceptable computation time. References: 1. ITK: www.itk.org 2. Elastix: www.isi.uu.nl/Elastix 1283 poster ACCURACY OF DEFORMABLE IMAGE REGISTRATION FOR CONTOUR PROPAGATION IN ADAPTIVE LUNG RADIOTHERAPY N. Hardcastle1 , W. van Elmpt2 , M. Oechsner3 , M. Guckenberger3 , D. De Ruysscher2 , K. Bzdusek4 , W. Tomé1 1 U NIVERSITY OF W ISCONSIN S CHOOL OF M EDICINE AND P UBLIC H EALTH, Human Oncology, Madison, USA 2 MAASTRO C LINIC, Maastricht, Netherlands 3 U NIVERSITY H OSPITAL W UERZBURG, Radiation Oncology, Wuerzburg, Germany 4 P HILIPS H EALTHCARE, Fitchburg, WI, USA Purpose: Anatomical changes are often observed during radiotherapy treatment of lung cancer. Both normal tissues and tumor volumes can deform over time. Obtaining volumetric images during treatment fractions allows tracking of any anatomical changes for adaptive protocols. This approach however requires each volumetric image to be re-contoured. Currently, re-contouring of images during a radiotherapy course can be very time-intensive. Automatic propagation of target and OAR contours between two image sets is thus an attractive approach to reducing adaptive radiotherapy resource requirements. The aim of this study was to evaluate two deformable image registration (DIR) algorithms for the purpose of propagating contours from pre-treatment to midtreatment CT scans. Materials: Pre-treatment and mid-treatment kVCT scans were obtained for 15 patients. The primary tumor (GTV), nodal tumor (GTV-nodes), lungs, esophagus and spinal cord were delineated by a physician on each of the pre and mid-treatment scans. DIR was performed by warping the pre-treatment to the mid-treatment scans and applying the deformation map to the pretreatment scan ROIs to obtain ROIs on the mid-treatment scan. Two DIR algorithms were used Demons (Thirion) and Salient Feature Based Registration (SFBR), as implemented in a research version of the Pinnacle RTPs (v9.1, Philips Medical Systems, Fitchburg, WI). The DIR-propagated ROIs were compared with the physician-drawn ROIs on the mid-treatment scan using the Dice score and mean of the slicewise Hausdorff distance . The difference in the Centre of Mass (COM) position of the GTVs was also measured. Results: The average metric scores for each of the five ROIs are shown in Figure 1. The DICE scores for normal structures were greater than for GTV volumes. The mean Dice score was greater for SFBR than for Demons for all organs with the exception of right lung. The average Hausdorff distance was lower with SFBR with all ROIs with the exception of the right lung. The
S 479
average COM difference for the GTV-tumor was 0.42 cm and 0.36 cm and GTV-nodes was 0.40 cm and 0.35 cm for demons and SFBR respectively.
Figure 1: Dice score (top row) and mean DTA (cm) (bottom row) for the five structures investigated. The black circle is the mean, the grey bar is ± 1 SD and the black horizontal bars are the minimum and maximum values. Conclusions: Two DIR algorithms were successfully used to automatically propagate both normal tissue and target volumes in repeat lung scans. Reasonably good agreement with physician drawn contours was observed for normal tissues. DIR-propagated nodal contours were not as accurate most probably due to less soft tissue contrast for these structures. 1284 poster AUTOMATED 2D/3D REGISTRATION FOR LUNG BRACHYTHERAPY P. Zvonarev1 , T. Farrell1 2 , R. Hunter2 , J. Hayward2 , M. Wierzbicki2 , R. Sur3 M C M ASTER U NIVERSITY, Medical Physics Applied Radiation Sciences, Hamilton, Canada 2 J URAVINSKI C ANCER C ENTRE, Medical Physics, Hamilton, Ontario, Canada 3 J URAVINSKI C ANCER C ENTRE, Oncology, Hamilton, Ontario, Canada
1
Purpose: High dose rate brachytherapy is an effective modality of treatment for obstructive lung cancer. For treatment planning, orthogonal X-ray images are used to reconstruct the treatment catheters. However, the poor soft tissue contrast of X-ray images does not allow visualization of the tumor or organs at risk. Diagnostic CT images are available for each patient prior to treatment; however these images cannot be directly fused with the orthogonal x-rays due to differences in patient positioning. To account for this, a 2D/3D registration algorithm was developed to register the orthogonal images to the CT volume, thereby allowing for reconstruction of the treatment catheters in the CT data set. Materials: An image-based, rigid body registration algorithm was developed. The best-neighbour search optimization was smoothed by parabolic interpolation. Image similarity was quantified using a combination of normalized mutual information (NMI) and the sum of intensity differences. Evaluation of the algorithm was performed using an anthropomorphic thorax phantom. Two narrow channels were drilled in the phantom allowing for treatment catheters to be inserted into the lungs. The phantom was placed in a custom body mould to achieve reproducible positioning.CT images of the phantom were acquired with and without the inserted treatment catheters (CT+, CT-). Orthogonal X-ray images were collected with the catheters in place and with the phantom shifted and rotated in 3D for eight unique positions. The position variation range was ±50 mm and ±15◦ for translations and rotations respectively. ECG leads were attached to the phantom at their normal anatomical locations to mimic an actual lung treatment procedure. The orthogonal images were registered with the CT- data using the registration algorithm. The catheter coordinates from the CT+ data and the X-ray images were digitized using BrachyVision software. Results: The coordinates from the orthogonal pair reconstruction were transformed and were fused into the CT+ data. The transformed data were compared with the positions directly digitized in the CT+ images. Registration error was quantified by computing the distance between the fused and true catheter positions and varied from 0.5 mm to 2.5 mm for both catheters in eight different data sets. The mean error was 1.9 mm. Conclusions: The proposed registration algorithm showed robustness to various degrees of initial displacement and to external objects introduced during orthogonal image acquisition. The registration error was acceptable for clinical applications.This work was supported by Varian Research Grant #07-583.