Motion Estimation and Motion-Compensated Reconstruction for Four-Dimensional Cone Beam Computed Tomography (4D-CBCT)




Huang, Xiaokun

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The emerging of sophisticated radiation therapy such as stereotactic body radiation therapy (SBRT) characterizing as high dose in each fraction and few fraction number requires higher accuracy for tumor localization. For organs influenced by the respiration, respiration induced motion becomes the principal cause for tumor localization uncertainty and four dimensional (4D) cone beam computed tomography (CBCT) has been developed to locate tumor in each respiration phase to better estimate the possible motion range of the tumor motion during the treatment. However, 4D-CBCT reconstructed by conventional methods on current commercial scanners is not optimal for tumor localization due to low image quality caused by insufficient number of projections located in each phase after the projection binning according to respiration phases. The specific aims of this dissertation research are to: 1) improving the accuracy of inter-phase motion model to feed in a motion-compensated reconstruction scheme to improve the 4D-CBCT image quality; 2) utilizing high-quality 4D-CBCT for motion evaluation and 4D dose accumulation for lung cancer patients receiving SBRT. The motion-compensated reconstruction suppresses motion and improves image quality by deforming other phase image to the reference phase using inter-phase motion model to reconstruct reference phase image using projections from all phases. Therefore, it is essential to improve the inter-phase motion model accuracy. Two methods, biomechanical modeling based and convolutional neural network (CNN) based, were applied to fine-tune the inner lung motion model. The biomechanical modeling is a physics-driven method which introduced tissue related elasticity properties to simulate the movement of lung and solve the deformation by finite-element analysis. Biomechanical modeling requires boundary condition which is the deformation vector fields (DVFs) estimated from a 2D-3D registration. For CNN based methods, boundary DVFs are also used as the input for the U-net based architectures to predict the inner lung motion. All methods can improve accuracy of DVFs and further improve reconstructed 4D-CBCT images quality. After obtaining high-quality 4D-CBCT images, we created a tool using 4D-CBCT images to evaluate the motion variation as well as calculate the accumulated 4D dose to monitor and evaluate the delivered dose for lung SBRT patients.

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