title: Few-view cone-beam CT reconstruction with deformed prior image.
abstract: OBJECTIVE
  Prior images can be incorporated into the image reconstruction process to improve the quality of subsequent cone-beam CT (CBCT) images from sparse-view or low-dose projections. The purpose of this work is to develop a deformed prior image-based reconstruction (DPIR) strategy to mitigate the deformation between the prior image and the target image.
  
  
  METHODS
  The deformed prior image is obtained by a projection-based registration approach. Specifically, the deformation vector fields used to deform the prior image are estimated through iteratively matching the forward projection of the deformed prior image and the measured on-treatment projections. The deformed prior image is then used as the prior image in the standard prior image constrained compressed sensing (PICCS) algorithm. A simulation study on an XCAT phantom and a clinical study on a head-and-neck cancer patient were conducted to evaluate the performance of the proposed DPIR strategy.
  
  
  RESULTS
  The deformed prior image matches the geometry of the on-treatment CBCT more closely as compared to the original prior image. Consequently, the performance of the DPIR strategy from few-view projections is improved in comparison to the standard PICCS algorithm, based on both visual inspection and quantitative measures. In the XCAT phantom study using 20 projections, the average root mean squared error is reduced from 14% in PICCS to 10% in DPIR, and the average universal quality index increases from 0.88 in PICCS to 0.92 in DPIR.
  
  
  CONCLUSIONS
  The present DPIR approach provides a practical solution to the mismatch problem between the prior image and target image, which improves the performance of the original PICCS algorithm for CBCT reconstruction from few-view or low-dose projections.
authors.affiliation: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75390.
authors.forename: Hua
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75390.
authors.forename: Luo
authors.initials: L
authors.lastname: Ouyang
authors.affiliation: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Huang
authors.affiliation: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Ma
authors.affiliation: Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
authors.affiliation: Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75390.
authors.forename: Jing
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D054893:Cone-Beam Computed Tomography / Q000295:instrumentation / Q000379:methods*; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008954:Models, Biological; D019047:Phantoms, Imaging