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• W2333182544 abstract "Purpose/Objective(s)Adaptive radiation therapy for head and neck cancer (HNC) treatment requires significant amounts of organ and target delineations. The aim of this study is to evaluate a commercially available deformable image registration (DIR) software, as a tool to propagate physician contours from planning CT to subsequent CT images in a fully automatic fashion.Materials/MethodsTen HNC patients, each with a planning CT (CT1) and a second CT (CT2) taken approximately 3 weeks into treatment, were selected. Clinically relevant normal organs and treatment volumes were manually delineated by a physician on both sets of CT scans. The contour sets of CT1 were automatically deformed onto to the corresponding CT2 images using the DIR algorithm. For each patient, rigid-body registration was performed, followed by B-spline based DIR. The volume of interest encompassed the entire patient contour. The intensity similarity metric used was Mutual Information. Agreement between automatically transferred contours and physician's manual contours were evaluated visually and quantitatively. DICE coefficients, 2x(V1∩V2)/(V1+V2), were recorded for overlap quantification.ResultsGood agreement was found between the auto-propagation and manual segmentation based on physician's visual inspection. Table presents the mean and standard deviations of organ DICE indices. Well differentiated and relatively large organs showed a high degree of correlation (DICE > 0.8), while small and thin organs with unclear boundaries on CT images showed less correlation (DICE <0.5), possibly due to relatively large CT slice thickness (3 mm). GTVs and PTVs generally showed a significant amount of overlap with DICE indices even with the expected shrinkage a few weeks into treatment. When averaged across the 10 patients with different tumor sites and significantly different volumes, the DICE indices were 0.72 ± 0.16 and 0.85 ± 0.07 for all GTVs and PTVs, respectively.ConclusionsPoster Viewing Abstract 3305; TableOrgan DICE indices [mean ± std.]Brainstem0.85 ± 0.04Optic nerve R0.46 ± 0.24Chiasm0.65 ± 0.14Parotid gland L0.78 ± 0.07Cochlea L0.64 ± 0.17Parotid gland R0.82 ± 0.04Cochlea R0.65 ± 0.16Pituitary0.65 ± 0.19Eye L0.85 ± 0.05Spinal cord0.75 ± 0.09Eye R0.83 ± 0.04Temporal lobe0.78 ± 0.12Optic nerve L0.56 ± 0.14 Open table in a new tab Purpose/Objective(s)Adaptive radiation therapy for head and neck cancer (HNC) treatment requires significant amounts of organ and target delineations. The aim of this study is to evaluate a commercially available deformable image registration (DIR) software, as a tool to propagate physician contours from planning CT to subsequent CT images in a fully automatic fashion. Adaptive radiation therapy for head and neck cancer (HNC) treatment requires significant amounts of organ and target delineations. The aim of this study is to evaluate a commercially available deformable image registration (DIR) software, as a tool to propagate physician contours from planning CT to subsequent CT images in a fully automatic fashion. Materials/MethodsTen HNC patients, each with a planning CT (CT1) and a second CT (CT2) taken approximately 3 weeks into treatment, were selected. Clinically relevant normal organs and treatment volumes were manually delineated by a physician on both sets of CT scans. The contour sets of CT1 were automatically deformed onto to the corresponding CT2 images using the DIR algorithm. For each patient, rigid-body registration was performed, followed by B-spline based DIR. The volume of interest encompassed the entire patient contour. The intensity similarity metric used was Mutual Information. Agreement between automatically transferred contours and physician's manual contours were evaluated visually and quantitatively. DICE coefficients, 2x(V1∩V2)/(V1+V2), were recorded for overlap quantification. Ten HNC patients, each with a planning CT (CT1) and a second CT (CT2) taken approximately 3 weeks into treatment, were selected. Clinically relevant normal organs and treatment volumes were manually delineated by a physician on both sets of CT scans. The contour sets of CT1 were automatically deformed onto to the corresponding CT2 images using the DIR algorithm. For each patient, rigid-body registration was performed, followed by B-spline based DIR. The volume of interest encompassed the entire patient contour. The intensity similarity metric used was Mutual Information. Agreement between automatically transferred contours and physician's manual contours were evaluated visually and quantitatively. DICE coefficients, 2x(V1∩V2)/(V1+V2), were recorded for overlap quantification. ResultsGood agreement was found between the auto-propagation and manual segmentation based on physician's visual inspection. Table presents the mean and standard deviations of organ DICE indices. Well differentiated and relatively large organs showed a high degree of correlation (DICE > 0.8), while small and thin organs with unclear boundaries on CT images showed less correlation (DICE <0.5), possibly due to relatively large CT slice thickness (3 mm). GTVs and PTVs generally showed a significant amount of overlap with DICE indices even with the expected shrinkage a few weeks into treatment. When averaged across the 10 patients with different tumor sites and significantly different volumes, the DICE indices were 0.72 ± 0.16 and 0.85 ± 0.07 for all GTVs and PTVs, respectively. Good agreement was found between the auto-propagation and manual segmentation based on physician's visual inspection. Table presents the mean and standard deviations of organ DICE indices. Well differentiated and relatively large organs showed a high degree of correlation (DICE > 0.8), while small and thin organs with unclear boundaries on CT images showed less correlation (DICE <0.5), possibly due to relatively large CT slice thickness (3 mm). GTVs and PTVs generally showed a significant amount of overlap with DICE indices even with the expected shrinkage a few weeks into treatment. When averaged across the 10 patients with different tumor sites and significantly different volumes, the DICE indices were 0.72 ± 0.16 and 0.85 ± 0.07 for all GTVs and PTVs, respectively. ConclusionsPoster Viewing Abstract 3305; TableOrgan DICE indices [mean ± std.]Brainstem0.85 ± 0.04Optic nerve R0.46 ± 0.24Chiasm0.65 ± 0.14Parotid gland L0.78 ± 0.07Cochlea L0.64 ± 0.17Parotid gland R0.82 ± 0.04Cochlea R0.65 ± 0.16Pituitary0.65 ± 0.19Eye L0.85 ± 0.05Spinal cord0.75 ± 0.09Eye R0.83 ± 0.04Temporal lobe0.78 ± 0.12Optic nerve L0.56 ± 0.14 Open table in a new tab" @default.
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• W2333182544 date "2013-10-01" @default.
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• W2333182544 title "CT-to-CT Contour Propagation Using Deformable Image Registration for Head and Neck Cancer Radiation Therapy: Investigation for Clinical Application" @default.
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