Multiparametric Ultrasound Imaging of Prostate Cancer Using Deep Neural Networks.

Journal: Ultrasound in medicine & biology
PMID: 39174376

Abstract

OBJECTIVE: A deep neural network (DNN) was trained to generate a multiparametric ultrasound (mpUS) volume from four input ultrasound-based modalities (acoustic radiation force impulse [ARFI] imaging, shear wave elasticity imaging [SWEI], quantitative ultrasound-midband fit [QUS-MF], and B-mode) for the detection of prostate cancer.

Authors

  • Derek Y Chan
    Department of Biomedical Engineering, Duke University, Durham, NC, USA. Electronic address: derek.chan@duke.edu.
  • D Cody Morris
    Department of Biomedical Engineering, Duke University, Durham, NC, USA.
  • Spencer R Moavenzadeh
    Department of Biomedical Engineering, Duke University, Durham, NC, USA.
  • Theresa H Lye
    Department of Radiology, Weill Cornell Medicine, New York, NY, USA; Topcon Advanced Biomedical Imaging Laboratory, Topcon Healthcare, Oakland, NJ, USA.
  • Thomas J Polascik
    Departments of Urology and Radiology, Duke University Medical Center, Durham, NC, USA.
  • Mark L Palmeri
    Department of Biomedical Engineering, Duke University, Durham, NC, USA.
  • Jonathan Mamou
  • Kathryn R Nightingale
    Department of Biomedical Engineering, Duke University, Durham, NC, USA.

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