A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images.

Journal: Journal of computer assisted tomography
Published Date: Mar 22, 2023

Abstract

OBJECTIVE: Advancements in computed tomography (CT) reconstruction have enabled image quality improvements and dose reductions. Previous advancements have included iterative and model-based reconstruction. The latest image reconstruction advancement uses deep learning, which has been evaluated for polychromatic imaging only. This article characterizes a commercially available deep learning imaging reconstruction applied to dual-energy CT.

Authors

  • Ethan P Nikolau
    From the Departments of Medical Physics.
  • Giuseppe V Toia
    Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Mailbox 3252, Madison, WI 53792.
  • Brian Nett
    GE Healthcare, Waukesha, WI.
  • Jie Tang
    Department of Computer Science and Technology, Tsinghua University, Beijing, China jietang@tsinghua.edu.cn.
  • Timothy P Szczykutowicz
    Department of Radiology, University of Wisconsin Madison, 1111 Highland Ave, 1005 WIMR, Madison, WI 53705.

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