MRI-based automated detection of implanted low dose rate (LDR) brachytherapy seeds using quantitative susceptibility mapping (QSM) and unsupervised machine learning (ML).

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Sep 19, 2018

Abstract

BACKGROUND AND PURPOSE: Permanent seed brachytherapy is an established treatment option for localized prostate cancer. Currently, post-implant dosimetry is performed on CT images despite challenging target delineation due to limited soft tissue contrast. This work aims to develop an MRI-only workflow for post-implant dosimetry of prostate brachytherapy seeds.

Authors

  • Reyhaneh Nosrati
    Department of Physics, Ryerson University, Toronto, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada. Electronic address: reyhaneh.nosrati@ryerson.ca.
  • Abraam Soliman
    Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada.
  • Habib Safigholi
    Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada.
  • Masoud Hashemi
    Anesthesiology Research Center, Emam Hossein Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Matthew Wronski
    Department of Medical Physics, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Canada.
  • Gerard Morton
    Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
  • Ana Pejović-Milić
    Department of Physics, Ryerson University, Toronto, Canada.
  • Greg Stanisz
    Department of Physics, Ryerson University, Toronto, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Canada.
  • William Y Song
    Department of Physics, Ryerson University, Toronto, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Canada.

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