Browsing by Subject "Movement"
Now showing 1 - 2 of 2
- Results Per Page
- Sort Options
Item Motion Estimation and Motion-Compensated Reconstruction for Four-Dimensional Cone Beam Computed Tomography (4D-CBCT)(2020-05-01T05:00:00.000Z) Huang, Xiaokun; Jia, Xun; Wang, Jing; Sun, Xiankai; Jiang, Steve B.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.Item Protein Mechanics Through X-Ray Crystallography(2016-04-14) White, Kristopher Ian; Rice, Luke M.; Ranganathan, Rama; Rosen, Michael K.; Yu, HongtaoProteins are dynamic entities which often cycle through a variety of conformational states as they carry out their functions. Despite the success of biophysical methods in determining the physical structures of proteins--that is, the precise three-dimensional configuration of all of their constituent atoms--no comprehensive physical models exist which accurately describe or predict the conformational cycling of proteins. For such models to be built, comprehensive knowledge of the energetics of intramolecular interactions in model proteins is essential. Here, we present three approaches which begin to address this problem, each through a different form of perturbation to a series of members of the PDZ protein family. First, we show that cycles of mutagenesis coupled with X-ray crystallography can reveal an anisotropic, distributed pattern of physical interactions in a PDZ domain, PSD-95 PDZ3. This pattern is functionally important and deeply connected to the evolution of PDZ domains in general. Second, we present a new approach for identifying essential dynamical features of proteins from relatively conventional X-ray diffraction data. Through combined analysis of nine different PDZ domain diffraction data sets, we show that collective features can be extracted and averaged, yielding a consensus picture of dynamics in the PDZ domain family. Finally, we report the development of a novel pump-probe method for directly inducing and reading out motions in proteins through the combined use of strong electric fields and time-resolved X-ray crystallography. We show that the method can be used to drive functionally relevant motions in a PDZ domain, LNX2 PDZ2, and provide a foundation for future efforts designed to directly probe the energetic architecture of proteins.