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Sensing and Imaging

, 19:14 | Cite as

Reduction of Angularly-Varying-Data Truncation in C-Arm CBCT Imaging

  • Dan Xia
  • Yu-Bing Chang
  • Joe Manak
  • Adnan H. Siddiqui
  • Zheng Zhang
  • Buxin Chen
  • Emil Y. Sidky
  • Xiaochuan Pan
Original Paper
  • 102 Downloads
Part of the following topical collections:
  1. Recent Developments in Sensing and Imaging

Abstract

C-arm cone-beam computed tomography (CBCT) has been used increasingly as an imaging tool for yielding 3D anatomical information about the subjects in surgical and interventional procedures. In the clinical applications, the limited field-of-view (FOV) of C-arm CBCT can lead to significant data truncation, resulting in image artifacts that can obscure low contrast tumor embedded within soft-tissue background, thus limiting the utility of C-arm CBCT. The truncation issue can become serious as most of the surgical and interventional procedures would involve devices and tubes that are placed outside the FOV of C-arm CBCT and thus can engender angularly-varying-data truncation. Existing methods may not be adequately applicable to dealing with the angularly-varying truncation. In this work, we seek to reduce truncation artifacts by tailoring optimization-based reconstruction directly from truncated data, without performing pre-reconstruction data compensation, collected from physical phantoms and human subjects. The reconstruction problem is formulated as a constrained optimization program in which a data-derivative-\(\ell _{2}\)-norm fidelity is included for effectively suppressing image artifacts caused by the angularly-varying-data truncation, and the generic Chambolle–Pock algorithm is tailored to solve the optimization program. The results of the study suggest that an appropriately designed optimization-based reconstruction can be exploited for yielding images with reduced artifacts caused by angularly-varying-data truncation.

Keywords

Computed tomography Image reconstruction Optimization program Primal-dual algorithm Angularly-varying-data truncation Data derivative 

Notes

Acknowledgements

This work was supported in part by NIH R01 Grants Nos. CA182264, and EB018102. The contents of this article are solely the responsibility of the authors and do not necessarily represent the official NIH views.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of RadiologyThe University of ChicagoChicagoUSA
  2. 2.Canon Medical Research Institute USA, Inc.Vernon HillsUSA
  3. 3.University at Buffalo Neurosurgery, Inc.BaffuloUSA

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