Adaptive weighted log subtraction based on neural networks for markerless tumor tracking using dual-energy fluoroscopy.

Journal: Medical physics
PMID: 31797397

Abstract

PURPOSE: To present a novel method, based on convolutional neural networks (CNN), to automate weighted log subtraction (WLS) for dual-energy (DE) fluoroscopy to be used in conjunction with markerless tumor tracking (MTT).

Authors

  • Maksat Haytmyradov
    Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL, 60153, USA.
  • Hassan Mostafavi
    Varian Medical Systems, 3120 Hansen Way, Palo Alto, CA, 94304, USA.
  • Roberto Cassetta
    Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL, 60153, USA.
  • Rakesh Patel
    Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL, 60153, USA.
  • Murat Surucu
    Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL, 60153, USA.
  • Liangjia Zhu
    Varian Medical Systems, 3120 Hansen Way, Palo Alto, CA, 94304, USA.
  • John C Roeske
    Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL, 60153, USA.

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