Optimization-based image reconstruction with artifact reduction in C-arm CBCT. Academic Article uri icon

Overview

abstract

  • We investigate an optimization-based reconstruction, with an emphasis on image-artifact reduction, from data collected in C-arm cone-beam computed tomography (CBCT) employed in image-guided interventional procedures. In the study, an image to be reconstructed is formulated as a solution to a convex optimization program in which a weighted data divergence is minimized subject to a constraint on the image total variation (TV); a data-derivative fidelity is introduced in the program specifically for effectively suppressing dominant, low-frequency data artifact caused by, e.g. data truncation; and the Chambolle-Pock (CP) algorithm is tailored to reconstruct an image through solving the program. Like any other reconstructions, the optimization-based reconstruction considered depends upon numerous parameters. We elucidate the parameters, illustrate their determination, and demonstrate their impact on the reconstruction. The optimization-based reconstruction, when applied to data collected from swine and patient subjects, yields images with visibly reduced artifacts in contrast to the reference reconstruction, and it also appears to exhibit a high degree of robustness against distinctively different anatomies of imaged subjects and scanning conditions of clinical significance. Knowledge and insights gained in the study may be exploited for aiding in the design of practical reconstructions of truly clinical-application utility.

publication date

  • October 3, 2016

Research

keywords

  • Artifacts
  • Cone-Beam Computed Tomography
  • Image Processing, Computer-Assisted

Identity

PubMed Central ID

  • PMC5109550

Scopus Document Identifier

  • 84991709371

PubMed ID

  • 27694700

Additional Document Info

volume

  • 61

issue

  • 20