Evaluating renal lesions using deep-learning based extension of dual-energy FoV in dual-source CT-A retrospective pilot study.

Journal: European journal of radiology
Published Date: Jun 1, 2021

Abstract

PURPOSE: Dual-source (DS) CT, dual-energy (DE) field of view (FoV) is limited to the size of the smaller detector array. The purpose was to establish a deep learning-based approach to DE extrapolation by estimating missing image data using data from both tubes to evaluate renal lesions.

Authors

  • Fides R Schwartz
    Department of Radiology, Duke University, Durham, NC, 27710, USA.
  • Darin P Clark
  • Yuqin Ding
    Duke University Health System, Department of Radiology, 2301 Erwin Road, Box 3808, Durham, NC, 27710, United States; Department of Radiology, Zhongshan Hospital, Fudan University; Shanghai Institute of Medical Imaging, Shanghai, 200032, People's Republic of China. Electronic address: yuqin.ding@duke.edu.
  • Juan Carlos Ramirez-Giraldo
    CT R&D Collaborations at Siemens Healthineers, 2424 Erwin Road - Hock Plaza, Durham, NC, 27705, United States. Electronic address: juancarlos.ramirezgiraldo@siemens-healthineers.com.
  • Cristian T Badea
  • Daniele Marin
    Department of Radiology, Duke University, Durham, NC, 27710, USA.

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