Affiliations: Department of Radiation Oncology, Stanford University,
Stanford, CA, USA | Department of Electrical Engineering, Stanford
University, Stanford, CA, USA | Department of Statistics, Stanford University,
Stanford, CA, USA | Department of Radiation Oncology, University of
Pittsburgh Medical Center, Pittsburgh, PA, USA | Research Institute of Biomedical Engineering, The
Catholic University of Korea, Seoul, Korea
Note: [] Corresponding author: Lei Xing, Research Institute of Biomedical
Engineering, The Catholic University of Korea, Seoul, Korea. E-mail: lei@stanford.edu
Abstract: In four-dimensional (4D) cone-beam computed tomography (CBCT), there
is a spatio-temporal tradeoff that currently limits the accuracy. The aim of
this study is to develop a Bregman iteration based formalism for high quality
4D CBCT image reconstruction from a limited number of low-dose projections. The
4D CBCT problem is first divided into multiple 3D CBCT subproblems by grouping
the projection images corresponding to the phases. To maximally utilize the
information from the under-sampled projection data, a compressed sensing (CS)
method with Bregman iterations is employed for solving each subproblem. We
formulate an unconstrained optimization problem based on least-square criterion
regularized by total-variation. The least-square criterion reflects the
inconsistency between the measured and the estimated line integrals.
Furthermore, the unconstrained problem is updated and solved repeatedly by
Bregman iterations. The performance of the proposed algorithm is demonstrated
through a series of simulation studies and phantom experiments, and the results
are compared to those of previously implemented compressed sensing technique
using other gradient-based methods as well as conventional filtered
back-projection (FBP) results. The simulation and experimental studies have
shown that artifact suppressed images can be obtained with as small as 41
projections per phase, which is adequate for clinical 4D CBCT reconstruction.
With such small number of projections, the conventional FDK failed to yield
meaningful 4D CBCT images, and CS technique using conjugate gradient was not
able to recover sharp edges. The proposed method significantly reduces the
radiation dose and scanning time to achieve the high quality images compared to
the 4D CBCT imaging based on the conventional FDK technique and the existing CS
techniques.
Keywords: Four dimensional cone beam computed tomography, compressed sensing, Bregman iteration, fast first-order method
