Clinical evaluation of accelerated diffusion-weighted imaging of rectal cancer using a denoising neural network.

Journal: European journal of radiology
PMID: 39467396

Abstract

BACKGROUND: To evaluate the effectiveness of a deep learning denoising approach to accelerate diffusion-weighted imaging (DWI) and thus improve diagnostic accuracy and image quality in restaging rectal MRI following total neoadjuvant therapy (TNT).

Authors

  • Iva Petkovska
    Body Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Or Alus
    Department of Medical Physics, Memorial Sloan Kettering Cancer Cencer, New York, NY, USA.
  • Lee Rodriguez
    Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Maria El Homsi
    Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Jennifer S Golia Pernicka
    Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY.
  • Maria Clara Fernandes
    Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA.
  • Junting Zheng
    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Marinela Capanu
    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Ricardo Otazo
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

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