@article{Optimization-Based:1330,
      recid = {1330},
      author = {Chen, Buxin},
      title = {Optimization-Based Image Reconstruction for X-Ray CT with  Multispectral Data},
      publisher = {The University of Chicago},
      school = {Ph.D.},
      address = {2017-06},
      pages = {208},
      abstract = {Computed tomography (CT) has grown into a major workhorse  in radiology since its emergence in the 1970's, for its  noninvasiveness, three-dimensional information, and  superior contrast resolution. There had been a number of  major advances in the CT technology, including  optimization-based reconstruction methods, which can be  designed to reduce image artifacts and enable flexible  scanning configuration design. More recently, there has  been a renewed interest in exploring the energy information  in CT imaging using multispectral scans. A number of  commercial scanners are available to acquire dual-energy  scan data for a range of clinical applications. On the  other hand, a common limitation shared by almost all  commercial dual-energy CT scanners is the significant  addition of special hardware to conventional diagnostic CT,  adding on to the already-expensive cost of CT systems. Part  of the reason for the dependence on the special hardware to  acquire dual-energy or multispectral CT data is the need to  conform to the data conditions required by the  reconstruction methods that include either data-domain or  image-domain decomposition and the failure to take  advantage of the design flexibility enabled by  fully-modeled, optimization-based reconstruction methods,  such as the one-step inversion methods for multispectral  CT. 

In this dissertation work, we aim to propose a  one-step, optimization-based reconstruction method and  enable novel, non-standard scan configurations of potential  practical significance for multispectral CT that can be  readily implemented on existing conventional CT scanners  with no or minimum system modification. We start with the  development of the method, including a non-linear data  model, a non-convex optimization program, and an algorithm  for numerically solving the program, and applied the method  to both simulated and real data collected from standard,  full-scan and non-standard, partial-scan configurations.  The results suggest that fast, low-dose, and low-cost  multispectral CT can be enabled by the proposed  optimization-based reconstruction and the ASD-NC-POCS  algorithm.},
      url = {http://knowledge.uchicago.edu/record/1330},
      doi = {https://doi.org/10.6082/uchicago.1330},
}