Pix2Pix generative-adversarial network in improving the quality of T2-weighted prostate magnetic resonance imaging: a multi-reader study.

Journal: Diagnostic and interventional radiology (Ankara, Turkey)
Published Date: May 5, 2025

Abstract

PURPOSE: To assess the performance and feasibility of generative deep learning in enhancing the image quality of T2-weighted (T2W) prostate magnetic resonance imaging (MRI).

Authors

  • Yeliz Başar
    Acıbadem Healthcare Group, Department of Radiology, İstanbul, Türkiye.
  • Mustafa Said Kartal
    Cumhuriyet University Faculty of Medicine, Sivas, Türkiye.
  • Mustafa Ege Seker
    University of Wisconsin-Madison, School of Medicine, Department of Radiology, Madison, USA.
  • Deniz Alis
    Acıbadem Mehmet Ali Aydınlar University Faculty of Medicine, Department of Radiology, İstanbul, Türkiye.
  • Delal Seker
    Dicle University Faculty of Engineering, Department of Electrical-Electronics Engineering, Diyarbakır, Türkiye.
  • Müjgan Orman
    Acıbadem Healthcare Group, Department of Radiology, İstanbul, Türkiye.
  • Sabri Şirolu
    University of Health Sciences Türkiye, Şişli Hamidiye Etfal Training and Research Hospital, Clinic of Radiology, İstanbul, Türkiye.
  • Serpil Kurtcan
    Acıbadem Healthcare Group, Department of Radiology, İstanbul, Türkiye.
  • Aydan Arslan
    Ümraniye Training and Research Hospital, Clinic of Radiology, İstanbul, Türkiye.
  • Nurper Denizoğlu
    University of Health Sciences Türkiye, Sultan 2. Abdulhamid Han Training and Research Hospital, Clinic of Radiology, İstanbul, Türkiye.
  • İlkay Öksüz
    İstanbul Technical University Faculty of Engineering, Department of Computer Engineering, İstanbul, Türkiye.
  • Ercan Karaarslan
    Acıbadem Mehmet Ali Aydınlar University Faculty of Medicine, Department of Radiology, İstanbul, Türkiye.

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