Yabo Fu1, Shi Liu1, H Harold Li1, Hua Li1, Deshan Yang1. 1. Department of Radiation Oncology, School of Medicine, Washington University in Saint Louis, 4921 Parkview Place, St. Louis, MO, 63110, USA.
Abstract
PURPOSE: Isotropic smoothing has been conventionally used to regularize deformation vector fields (DVFs) in deformable image registration (DIR). However, the isotropic smoothing method enforces global smoothness and therefore cannot accurately model the complex tissue deformation, such as sliding motion at organ boundaries. To accurately model and estimate sliding tissue motion, an adaptive direction-dependent DVF regularization technique was developed in this study. METHODS: A DVF is computed and updated iteratively by minimizing the intensity differences between the images. In each iteration, the DVF was smoothed using an adaptive direction-dependent filter which enforces different motion propagation mechanisms along the primary normal and tangential directions of soft tissue local structures. A Gaussian isotropic filter was used along the normal direction while a bilateral filter was used along the tangential direction. To support large sliding motion, an automatic method was developed to delineate sliding surfaces, such as the chest wall and abdominal wall, where large organ sliding motion occurs. Parameters of the DVF regularization were adjusted adaptively based on a distance map to the sliding surfaces. The proposed method was tested on 14 4D-CT datasets at End-Inhalation (EI) and End-Exhalation (EE) phases of a respiratory cycle (10 public lung datasets, 3 upper abdomen datasets and 1 digital phantom dataset). TRE results of the 10 lung datasets were compared to results from six other existing DIR methods. For the three upper abdomen patient datasets, DIR accuracy was evaluated using manually defined landmarks across the lung and the abdomen. For the digital phantom dataset, DIR accuracy was evaluated using the ground truth displacement of a total 40,000 points that were evenly distributed across the phantom. RESULTS: The results showed that the sliding motion was preserved near the surface of chest wall and abdominal wall. The average target registration error (TRE) was reduced by 35.1% using the proposed method in comparison with five other methods on the 10 lung datasets. The sum of squared difference (SSD) after registration using the proposed method was 4.4% and 11.4% smaller than the SSDs obtained using isotropic smoothing and bilateral smoothing respectively. On the digital phantom, the average TRE was reduced by 59.6% near the surface of liver and by 53.7% near the surface of spleen using the proposed method. Contour propagation and Jacobian determinant analysis of DVF suggested an overall improved accuracy using the proposed method. CONCLUSION: An adaptive direction-dependent DVF regularization method has been developed to model the sliding tissue motion of the thoracic and abdominal organs. The overall motion estimation accuracy has been improved especially near the chest wall and abdominal wall where large organ sliding motion occurs.
PURPOSE: Isotropic smoothing has been conventionally used to regularize deformation vector fields (DVFs) in deformable image registration (DIR). However, the isotropic smoothing method enforces global smoothness and therefore cannot accurately model the complex tissue deformation, such as sliding motion at organ boundaries. To accurately model and estimate sliding tissue motion, an adaptive direction-dependent DVF regularization technique was developed in this study. METHODS: A DVF is computed and updated iteratively by minimizing the intensity differences between the images. In each iteration, the DVF was smoothed using an adaptive direction-dependent filter which enforces different motion propagation mechanisms along the primary normal and tangential directions of soft tissue local structures. A Gaussian isotropic filter was used along the normal direction while a bilateral filter was used along the tangential direction. To support large sliding motion, an automatic method was developed to delineate sliding surfaces, such as the chest wall and abdominal wall, where large organ sliding motion occurs. Parameters of the DVF regularization were adjusted adaptively based on a distance map to the sliding surfaces. The proposed method was tested on 14 4D-CT datasets at End-Inhalation (EI) and End-Exhalation (EE) phases of a respiratory cycle (10 public lung datasets, 3 upper abdomen datasets and 1 digital phantom dataset). TRE results of the 10 lung datasets were compared to results from six other existing DIR methods. For the three upper abdomen patient datasets, DIR accuracy was evaluated using manually defined landmarks across the lung and the abdomen. For the digital phantom dataset, DIR accuracy was evaluated using the ground truth displacement of a total 40,000 points that were evenly distributed across the phantom. RESULTS: The results showed that the sliding motion was preserved near the surface of chest wall and abdominal wall. The average target registration error (TRE) was reduced by 35.1% using the proposed method in comparison with five other methods on the 10 lung datasets. The sum of squared difference (SSD) after registration using the proposed method was 4.4% and 11.4% smaller than the SSDs obtained using isotropic smoothing and bilateral smoothing respectively. On the digital phantom, the average TRE was reduced by 59.6% near the surface of liver and by 53.7% near the surface of spleen using the proposed method. Contour propagation and Jacobian determinant analysis of DVF suggested an overall improved accuracy using the proposed method. CONCLUSION: An adaptive direction-dependent DVF regularization method has been developed to model the sliding tissue motion of the thoracic and abdominal organs. The overall motion estimation accuracy has been improved especially near the chest wall and abdominal wall where large organ sliding motion occurs.