Jinzhong Yang1, Yongbin Zhang2, Zijian Zhang1,3, Lifei Zhang1, Peter Balter1, Laurence Court1. 1. Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 2. Proton Therapy Center, University of Cincinnati Medical Center, Liberty Township, OH, USA. 3. Central South University Xiangya Hospital, Changsha, Hunan, China.
Abstract
PURPOSE: To improve the accuracy of computed tomography (CT) number mapping inside the lung in deformable image registration with large differences in lung volume for applications in vertical CT imaging and adaptive radiotherapy. METHODS: The deep inspiration breath hold (DIBH) CT image and the end of exhalation (EE) phase image in four-dimensional CT of 14 thoracic cancer patients were used in this study. Lung volumes were manually delineated. A Demons-based deformable registration was first applied to register the EE CT to the DIBH CT for each patient, and the resulting deformation vector field deformed the EE CT image to the DIBH CT space. Given that the mass of the lung remains the same during respiration, we created a mass-preserving model to correlate lung density variations with volumetric changes, which were characterized by the Jacobian derived from the deformation field. The Jacobian determinant was used to correct the lung CT numbers transferred from the EE CT image. The absolute intensity differences created by subtracting the deformed EE CT from the DIBH CT with and without density correction were compared. RESULTS: The ratio of DIBH CT to EE CT lung volumes was 1.6 on average. The deformable registration registered the lung shape well, but the appearance of voxel intensities inside the lung was different, demonstrating the need for density correction. Without density correction, the mean and standard deviation of the absolute intensity difference between the deformed EE CT and the DIBH CT inside the lung were 54.5 ± 45.5 for all cases. After density correction, these numbers decreased to 18.1 ± 34.9, demonstrating greater accuracy. The cumulative histogram of the intensity difference also showed that density correction improved CT number mapping greatly. CONCLUSION: Density correction improves CT number mapping inside the lung in deformable image registration for difficult cases with large lung volume differences.
PURPOSE: To improve the accuracy of computed tomography (CT) number mapping inside the lung in deformable image registration with large differences in lung volume for applications in vertical CT imaging and adaptive radiotherapy. METHODS: The deep inspiration breath hold (DIBH) CT image and the end of exhalation (EE) phase image in four-dimensional CT of 14 thoracic cancerpatients were used in this study. Lung volumes were manually delineated. A Demons-based deformable registration was first applied to register the EE CT to the DIBH CT for each patient, and the resulting deformation vector field deformed the EE CT image to the DIBH CT space. Given that the mass of the lung remains the same during respiration, we created a mass-preserving model to correlate lung density variations with volumetric changes, which were characterized by the Jacobian derived from the deformation field. The Jacobian determinant was used to correct the lung CT numbers transferred from the EE CT image. The absolute intensity differences created by subtracting the deformed EE CT from the DIBH CT with and without density correction were compared. RESULTS: The ratio of DIBH CT to EE CT lung volumes was 1.6 on average. The deformable registration registered the lung shape well, but the appearance of voxel intensities inside the lung was different, demonstrating the need for density correction. Without density correction, the mean and standard deviation of the absolute intensity difference between the deformed EE CT and the DIBH CT inside the lung were 54.5 ± 45.5 for all cases. After density correction, these numbers decreased to 18.1 ± 34.9, demonstrating greater accuracy. The cumulative histogram of the intensity difference also showed that density correction improved CT number mapping greatly. CONCLUSION: Density correction improves CT number mapping inside the lung in deformable image registration for difficult cases with large lung volume differences.
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